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Estimating the development cost of custom software
Abstract
In this paper an approach for the estimation of software development costs is presented. The method is based on the characterization of the software to be developed in terms of project and environment attributes and comparison with some similar completed project(s) recovered from a historical database. A case study is also presented, focusing on the calibration and application of the method on 59 information systems implementing supply chain functions in industry. Various strategies are explored, the best of which predicted effort quite effectively, with a mean estimation error of 24% with respect to the actual effort.
... Effort estimations are helpful both for the IT client and the IT developer. "In particular, based on these estimations, the acquiring organization may assess and monitor implementation costs, evaluate bids and develop realistic budgets and sched-ules" (Stamelos et al., 2003). Suppliers hereby can be seen as co-producers and major partners of a value network of procuring organizations since they usually provide production factors such as manpower, information, know-how, or funding (LSE Enterprise, 2009). ...
... Therefore it is both safer and more realistic to produce interval estimates, i.e. a lower and an upper bound along with some probability that the real effort lays within that interval. Relying blindly on a single point estimate, given the error magnitude associated to most cost estimation techniques, may easily lead to wrong managerial decisions and project failures (Stamelos et al., 2003). An interval estimate may still provide a point estimate for practical purposes (the mean of the interval), but provides additionally often invaluable information about the reliability of the estimate. ...
... Positive A use case method can be applied to estimate software development effort Stamelos et al., 2003 Positive An analogy technique combined with function point sizing predicted effort effectively. ...
Embedded software is becoming an integral part of the everyday environment. The share of software costs of the total system consisting of hardware and software has risen over the past decades and is expected to increase even further in the future. With this development, there is a need in procurement organizations for appropriate methods to reach fair but competitive prices for embedded software development.
Software is intangible, invisible, and intractable. It is inherently more difficult to understand and estimate a product or process that cannot be seen and touched. Software estimation has been a subject of academic and practitioners research since the start of software engineering in the 1950s. Software estimation methods are, however, mostly used in governmental projects and the aerospace and defense industries today.
The hypothesis of this thesis is that those methods could also be applied to the procurement of embedded software to facilitate fact-based negotiations with suppliers. Within this research the state-of-the art concepts, methods, and tools are described and applied in a case study with three completed projects in the application area of IBM’s Cost Engineering Team. Their results are evaluated and compared with the requirements of IBM’s Cost Engineering Team. However, also in this master thesis no ‘Silver Bullet’ of software estimation has been found.
... The CBR method can be divided into different categories in accordance with different similarity functions. Euclidean distance [4, 7, 8, 15], Manhattan distance [4, 7, 8, 16] , Minkowski dis- tance [15, 17], grey relational coefficient [7, 8, 18], Mahalanobis distance192021, and Gaussian distance [22] are the widely used distance metrics in CBR estimations. Stamelos et al. compared various distance metrics for effort estimation [17]. ...
... Euclidean distance [4, 7, 8, 15], Manhattan distance [4, 7, 8, 16] , Minkowski dis- tance [15, 17], grey relational coefficient [7, 8, 18], Mahalanobis distance192021, and Gaussian distance [22] are the widely used distance metrics in CBR estimations. Stamelos et al. compared various distance metrics for effort estimation [17]. They found that CBR estimations using different distance metrics come up with dissimilar results. ...
... Euclidian distance case based reasoning (Euc-CBR) method applies Euclidian distance to measure the similarity between two projects. The Euc-CBR method uses weighted Euclidian distance to reflect the relative importance of each effort driver, Euc-CBR CBR method with Euclidean distance [4, 7, 8, 15], Man-CBR CBR method with Manhattan distance [4, 7, 8, 16], Min-CBR CBR method with Minkowski distance [15, 17] Gre-CBR CBR method with grey relational coefficient [7, 8, 18] Gau-CBR CBR method with Gaussian distance [22] Mah-CBR CBR method with Mahalanobis distance192021 which can be expressed as (5) and (6). Equation (5) shows the overall distance between two projects derived from the Euc- CBR method, where n is the number of effort drivers. ...
Since software development has become an essential investment for many organizations recently, both the software industry and academic communities are more and more concerned about a reliable and accurate estimation of the software development effort. This study puts forward six widely used case-based reasoning (CBR) methods with optimized weights derived from the particle swarm optimization (PSO) method to estimate the software effort. Meanwhile, four combination methods are adopted to assemble the results of independent CBR methods. The experiments are carried out using two datasets of software projects from Desharnais dataset and Miyazaki dataset. Experimental results show that different CBR methods can get the best results in different parameters settings, and there is not a best method for the software effort estimation among the six different CBR methods. Currently, combination methods proposed in this study outperform independent methods, and the weighted mean combination (WMC) method can get the better result.
Keywords: Software effort estimation-Case-based reasoning-Weight optimization-Particle swarm optimization-Linear combination forecasting
... The intervalbased approach is also applied in the empirical part of our study. In [14] similar approach to the one proposed in this paper is developed to predict development effort. The main difference to [13] and [14] is that our approach first seeks for optimal team size and then predicts the development effort. ...
... In [14] similar approach to the one proposed in this paper is developed to predict development effort. The main difference to [13] and [14] is that our approach first seeks for optimal team size and then predicts the development effort. The study [14] supports our decision to use size intervals with the conclusion that projects of similar size exhibit similar productivity. ...
... The main difference to [13] and [14] is that our approach first seeks for optimal team size and then predicts the development effort. The study [14] supports our decision to use size intervals with the conclusion that projects of similar size exhibit similar productivity. Stamelos et al. [14] performed their research on the ISBSG [12] version 6. ...
This paper explores the relationship between software size, development effort and team size. We propose an approach aimed at finding the team size where the project effort has its minimum. The approach was applied to the ISBSG repository containing nearly 4000 software projects. Based on the results we provide our recommendation for the optimal or near-optimal team size in seven project groups defined by four project properties.
... Cost engineering can be defined as systematic approach to manage cost throughout the lifecycle (Hollmann, 2014). Cost prediction is the best estimate at a particular point made with a degree confidence for which research has shown that simple methods are preferable to complex methods (Armstrong, 2001, Stamelos et al., 2003. ...
... However, the information provided by the supplier may be intentionally incorrect as it might be used against them. Whereas, suppliers can also be seen as co-producers in a value network since they can provide production factors such as knowhow due to limitations of internal resources, strategic focus or simply cheaper (Stamelos et al., 2003, Roser et al., 2009 ...
The goal of this research is to understand more clearly lifecycle costs of supplier selection using methods of artificial intelligence (AI) with a total cost of ownership (TCO) model. AI is a key technology for procurement and its usage is still in its infancy (Schiele, 2017). Only few have successfully integrated AI methods into their operations and across their supply chains (Hazen et al., 2014, Schoenherr and Speier-Pero, 2015). This constitutes a research opportunity on how AI increase the performance of procurement and evaluation of suppliers (Chae et al., 2014, Sanders, 2016, Hülsbömer, 2017, Nguyen et al., 2017). The research question is how to reduce uncertainty in order to provide better information for selecting the right supplier, which in turn adds value to the organization. A case study is conducted in the automotive industry with three distinct data sets over the lifecycle mainly in electronics and connectivity. Exaptation, extending existing solutions to novel problems is a recognized and valid way to contribute (Gregor and Hevner, 2013, Evermann et al., 2017). Concepts are drawn that are applied in IT, chemicals, aerospace, and the military. Naïve algorisms are evaluated as baselines for quality of cost prediction based on nomination data and particularly data of change requests since they often lead to price increases (Bode and Peters, 2016). In addition, cost breakdowns are considered, as they are applicable during several phases of the lifecycle (Hellen, 1963). In particular, regression trees and Bayesian optimization seem prone to deal with uncertainty inherit in supplier selection (Brochu et al., 2010, Jain et al., 2014). Contribution is twofold: The work makes uncertainty measureable within the TCO framework. Furthermore, the research indicates that at AI models are able to reduce uncertainty inherit in supplier selection-at least to a large degree than the naïve baselines and standard regression.
... The literature offers several methods for performing this task. The main ones are: expert judgment, which is based on the accumulated experience of a team of experts; analogy, which is based on similar projects developed by the organisation; algorithmic, which is based on a mathematical model derived through statistical data analysis (O'Brien, 2009;Stamelos et al., 2003); and function point, which is based on the amount of business functionality a system provides to a user (O'Brien, 2009). ...
... Expert judgment is the most commonly used method for software effort estimations in planning (Stamelos et al., 2003). Experts can perform this task in the planning (Jørgensen, 2004) by examining a project from a broad view to provide the effort estimation (top-down approach) or by decomposing the project into activities, estimating them individually and then calculating the sum of all activities (bottom-up approach) (Shepperd and Cartwright, 2001;Jørgensen, 2004). ...
As business competition gets tougher, there is much pressure on software development projects to become more productive and efficient. Previous research has shown that quality planning is a key factor in enhancing the success of software development projects. The research method selected for this study was design science research (DSR), and the design science research process (DSRP) model was adopted to conduct the study. This research describes the design and development of the quality of planning (QPLAN) tool and the quality of planning evaluation model (QPEM), which are two innovative artefacts that evaluate the quality of project planning and introduce best planning practices, such as providing references from historical data, suggesting how to manage in an appropriate way and including lessons learnt in the software development process. In particular, the QPEM is based on cognitive maps that represent the project manager’s know-how, project manager’s characteristics and technological expertise, as well as top management support, enterprise environmental factors and the quality of methods and tools in a form that corresponds closely with humans’ perceptions. Data were collected from 66 projects undertaken in 12 organisations from eight types of industries in six countries. The results show that the QPLAN tool has been significantly contributing to enhancing the success rate of projects
... The literature offers several methods for performing this task. The main ones are: expert judgment, which is based on the accumulated experience of a team of experts; analogy, which is based on similar projects developed by the organisation; algorithmic, which is based on a mathematical model derived through statistical data analysis (O'Brien, 2009;Stamelos et al., 2003); and function point, which is based on the amount of business functionality a system provides to a user (O'Brien, 2009). ...
... Expert judgment is the most commonly used method for software effort estimations in planning (Stamelos et al., 2003). Experts can perform this task in the planning (Jørgensen, 2004) by examining a project from a broad view to provide the effort estimation (top-down approach) or by decomposing the project into activities, estimating them individually and then calculating the sum of all activities (bottom-up approach) (Shepperd and Cartwright, 2001;Jørgensen, 2004). ...
As business competition gets tougher, there is much pressure on software development projects to become more productive and efficient. Previous research has shown that quality planning is a key factor in enhancing the success of software development projects. The research method selected for this study was design science research (DSR), and the design science research process (DSRP) model was adopted to conduct the study. This research describes the design and development of the quality of planning (QPLAN) tool and the quality of planning evaluation model (QPEM), which are two innovative artefacts that evaluate the quality of project planning and introduce best planning practices, such as providing references from historical data, suggesting how to manage in an appropriate way and including lessons learnt in the software development process. In particular, the QPEM is based on cognitive maps that represent the project manager’s know-how, project manager’s characteristics and technological expertise, as well as top management support, enterprise environmental factors and the quality of methods and tools in a form that corresponds closely with humans’ perceptions. Data were collected from 66 projects undertaken in 12 organisations from eight types of industries in six countries. The results show that the QPLAN tool has been significantly contributing to enhancing the success rate of projects.
... According to the recent research: the majority of the outsourced projects involving software development activities, suffer from budget and schedule overruns, caused among other reasons, by insufficient initial estimations [19]. Effort estimations are helpful for both IT developers as well as for IT clients, based on these estimations, the acquiring organization may assess and monitor implementation costs, evaluate bids and develop realistic budgets and schedule [20]. Estimating and predicting development cost of software project success is a well-researched area, but maintaining the ratio of sound precision is still a great challenge for project managers. ...
... It is necessary to utilize various estimating techniques to effectively estimate software project cost within the information technology domain. In the shade of experience, it is always difficult for any generic software cost estimation technique to produce accurate statistics that are better than the target value of 25% when applied to some project data set [20]. The efficiency of a project state can be defined as the relationship of cost to its success probability, and the action of optimizing this relationship is equivalent to a multi-objective problem [22]. ...
Offshore Software Development Outsourcing (OSDO) is increasingly becoming the normal practice in the software industry. It offers a bundle of core benefits for client organizations which includes: high quality, fast and cost effective development of software products. However, OSDO possesses substantial risks and limitations during project management. To handle such problems Portfolio Cost Management (PCM) is used one of the best approaches. It is a set of centralized management of processes, methods and technologies used between client and vendor to reduce software costs and improve quality. We have performed a Systematic Literature Review (SLR) by applying customized search strings obtained from our research questions, along with the other SLR steps such as protocol development, initial publication selection, final publication selection, publication quality assessment, data extraction process and data synthesis. In this research, we explore 16 success factors of PCM to analyze the association between a client and vendor. It includes: 'efficient cost estimation strategies', 'efficient project management', 'efficient knowledge sharing management', 'efficient software effort estimation', 'planning realistic goals', and nine others. Furthermore, we analyze these factors based on different types of organizations, i.e. research and non-research. For best results in the software industry, it is proposed for vendor organizations to address the factors: 'efficient cost estimation strategies', 'efficient project management', 'efficient knowledge sharing management', 'efficient software effort estimation' and 'planning realistic goals'.
... Software development has become the current and future work that projects the continuity and evolution of companies, which is why the European Union focuses its business objectives on the contribution and design of software generating direct jobs (staff directly linked to the software) and indirect jobs (staff involved in the organization through the software project). The Economist Intelligence Unit in the year 2014 made an analysis of how the economy has improved with the systems, measuring the percentage of increase in jobs, salaries and GDP in the European Union, the data expressed shows an increase in total employment of 5.3% over the previous year (2013) generating 11.6 million jobs, also it is extracted that the salaries generated by the software industry are 34% higher than the average of the other sectors and 80% higher than the service sector, the average salary of the software industry is 333 which in Chilean pesos translates to approximately $33,000,000 (Al-Qudah et al, 2015;Jorgensen and Shepperd, 2007;Stamelos et al., 2003). ...
The use of technological resources and software has become standardized in today's society, which is why there is a need to be able to update according to the requirements that the market and industry demand from companies that develop products through a software engineering process. The role of the auditor is extremely important since he is the one who must make sure that everything is controlled and that the required needs are being fulfilled, as well as he is concerned about the security of the entities and their internal background. In this context, it is necessary to constantly improve the auditor's procedures and the legislation that regulates them, since the multiple frauds that companies suffer in terms of information obtained easily and quickly, without any major control, are well known, and it is here where care must be taken in order to reduce the levels of violations of access to unauthorized information assets. The objective of this paper is to present everything that surrounds the process of auditing requirements of software engineering projects, both generically and specifically in projects in particular the financial area, generally covering everything that is present in a software engineering project considering the need, what is obtained from them and why they arise in organizations.
... Hankkeen osana tarkasteltiin, millaisia kustannusvaikutuksia sovellusten elinkaarikustannuksiin aiheutuu, mikäli sovellus toteutetaan lohkoketjuperusteisen älysopimusjärjestelmän päälle perinteisen pilvipalveluratkaisun sijaan. Kustannusvaikutuksia on soveltuvin osin tarkasteltu mukautetun ohjelmistokehityksen ja pilvipalveluiden kustannusanalyysimalleja soveltaen (Stamelos et al., 2003;Li et al., 2009). Heuristisen kokonaisarvioinnin perusteella Ethereum-älysopimusjärjestelmän kustannusvaikutukset perinteisiin pilvipalveluratkaisuihin verrattuna näyttäytyvät seuraavanlaisina. ...
This study examined whether blockchain-based smart contracts can be used to automate the process of taxing wage income. The aim was also to clarify the relevant concepts and to elucidate the challenges and opportunities from the perspective of the tax authority.
As a part of the study, an illustrative conceptualization of a smart contract wage tax application was also drafted. The conceptualization was used to identify points of friction in the utilization of blockchain technology, especially in the context of public administration.
The potential of blockchain technology and distributed ledgers has been widely discussed recently. In recent years, the concepts in the field have expanded and taken on new meanings, further inflating the beliefs about the revolutionary effects of the phenomenon. However, for the time being, blockchains and distributed ledgers have not succeeded in delivering on those expectations.
In the light of this study, the potential for exploiting blockchain technology in the context of wage income taxation seems unfruitful. The possibilities for applying distributed ledgers appear somewhat brighter. However, as the conceptual demarcation of distributed ledgers and traditional integrative IT systems is somewhat vaguely defined, the benefits of such application are difficult to estimate.
... Euclidean distance (Shepperd and Schofield 1997;Jeffery et al. 2000;Huang and Chiu 2006;Chiu and Huang 2007;Li et al. 2009a, b), Manhattan distance (El Emam et al. 2001;Chiu and Huang 2007;Li et al. 2009a, b), and grey relational grade (Huang et al. 2008;Azzeh et al. 2010;Hsu and Huang 2011;Liang et al. 2012) are the three frequently used metrics in CBR estimations. Stamelos et al. (2003) analyzed the influence of different distance metrics for the CBR method, and the results indicated that different distance metrics would produce different results. ...
Software effort estimation (SEE) is the process of forecasting the effort required to develop a new software system, which is critical to the success of software project management and plays a significant role in software management activities. This study examines the potentials of the SEE method by integrating particle swarm optimization (PSO) with the case-based reasoning (CBR) method, where the PSO method is adopted to optimize the weights in weighted CBR. The experiments are implemented based on two datasets of software projects from the Maxwell and Desharnais datasets. The effectiveness of the proposed model is compared with other published results in terms of the performance measures, which are MMRE, Pred(0.25), and MdMRE. Experimental results show that the weighed CBR generates better software effort estimates than the unweighted CBR methods, and PSO-based weighted grey relational grade CBR achieves better performance and robustness in both datasets than other popular methods.
... Number of scientists and researchers are trying their best for developing more and more accurate, new software cost estimation techniques. Most of the software cost estimation techniques are based on the algorithmic models, expert judgment and machine learning approaches [7,8,9]. Accurate cost estimation is important because: a) To measure the impact of variations and support planning and re-planning. ...
As software cost estimation in software projects is a very difficult, confusing and challenging work for any software company and software cost estimation is the primary step to start any software project. It gives the overview of resources, efforts and time/schedule required for a software project in respect of cost to the software company. Software project success generally depends on software cost estimation as it provides us with an initial idea of the track, challenges and risk involved in the software project development. The software cost estimation in software engineering is very challenging to match the actual cost of the software project with estimated cost.
Effective software cost estimation can help software company make more consistent decisions in planning the software project risk. If the predicted estimates are wrong it may lead to negative results for a software company. Many software companies find, search and analyse software project performance by estimating software cost estimation accuracy. Unfortunately, regardless of the large body of experienced and skilled with estimation models, the accuracy of these models is not adequate. In this research paper observation on the performance of the software cost estimation methods and description of methodologies and technique used into software project cost estimation included. This research paper gives comparative comparison study of software cost estimation methods and reviews several classes of software cost estimation models and techniques. Also, study the pros and cons of different software cost estimation modelling techniques.
... Expert judgment is the most commonly used method for software effort estimations in planning (Stamelos et al., 2003). Experts can perform this task in the planning by examining a project from a broad viewpoint to provide the effort estimations (top-down) or by estimating them individually and then calculating the sum of all the activities (bottom-up) (Shepperd and Cartwright, 2001). ...
The research and development industry shifts significant resources, from physical products to software. This is triggered by the need to stay competitive in a tough market. However, the poor performance of new product development in the field of software development may restrict this trend. Following a research stream that focuses on NPD planning, we introduce the quality of planning evaluation model (QPEM) and a knowledge base for improving the quality of planning evaluation. QPEM suggests planning quality should be evaluated using two distinct and complementary approaches of top-down and bottom-up for enhancing the accuracy of planning: a) an established measure that assesses 16 planning products and b) a novel measure that assesses 55 factors that affect project performance. This second measure uses cognitive maps, which is a methodology based on expert knowledge that graphically describes the behaviour of a system, and represents the project manager's know-how and characteristics, technological expertise, top management support, enterprise environmental factors, and the quality of methods and tools in a form that corresponds closely with humans' perceptions. The alignment between these two approaches is demonstrated through multiple case studies.
... 3 Notwithstanding, it is the firm-specific application software that is used in the IT application. Nowadays, the main economic rationale for tailor-made software is that there is no software package available on the market for that specific AD or that the existing packages, even though configurable and customizable, are not compatible with the firm-specific needs, especially the functional requirements of the company in question (e.g., Stamelos et al. 2003). ...
One of the prevailing views is that technology-based IT assets do not have the potential for sustainable competitive advantage because they are usually not rare and can be easily acquired by competitors. However, previous RBV-based research on the competitive potential of IT has paid inadequate attention to IT applications, which are a combination of application software and (digital) information content. Conducting an RBV-inspired analytical-argumentative evaluation, we challenge this view and argue that different types of IT applications as sub-group of technology-based IT assets differ in their potential to yield competitive parity, temporary and sustainable competitive advantage. More specifically, we show that out of nine types of IT applications, one has the potential to yield competitive parity only, three have the potential to yield temporary competitive advantage, and five actually have the potential to yield
sustainable competitive advantage. Based on our theoretical analysis we suggest six
propositions for future empirical research.
... Effort estimates are useful for both the client and for the developer. [5]. Based on these estimates, the organization that wants to hire the project can assess and monitor the implementation costs, evaluate proposals, and develop realistic budgets and schedules. ...
... There are many software cost estimation techniques [1, 2, 3] and models which are classified as algorithmic and nonalgorithmic approach [4]. The algorithmic approach is based on size of project function point analysis, Linear Models, Multiplicative models and COCOMO. ...
... The projects in the data set were completed between years 1989-2001 in over seven different countries. The data are used in many other studies and is publicly available from the International Software Benchmarking Standards Group [7][8][9]. ...
In this paper, we identify a set of factors that may be used to forecast software productivity and software development time. Software productivity was measured in function points per person hours, and software development time was measured in number of elapsed days. Using field data on over 130 field software projects from various industries, we empirically test the impact of team size, integrated computer aided software engineering (ICASE) tools, software development type, software development platform, and programming language type on the software development productivity and development time. Our results indicate that team size, software development type, software development platform, and programming language type significantly impact software development productivity. However, only team size significantly impacts software development time. Our results indicate that effective management of software development teams, and using different management strategies for different software development type environments may improve software development productivity.
... The validation of this model on the same dataset yields better adjustment figure than its crisp counterparts. Stamelos et al. (2003) proposed an approach for software development cost estimation based on the characterization of the software to be developed in terms of project and environment attributes and comparison with some similar completed projects recovered from historical database. Their best estimation strategy predicted effort with a mean estimation error of 24 % with respect to the actual effort. ...
Unsupervised technique like clustering may be used for software cost estimation in situations where parametric models are difficult to develop. This paper presents a software cost estimation model based on a modified K-Modes clustering algorithm. The aims of this paper are: first, the modified K-Modes clustering which is an enhancement over the simple K-Modes algorithm using a proper dissimilarity measure for mixed data types, is presented and second, the proposed K-Modes algorithm is applied for software cost estimation. We have compared our modified K-Modes algorithm with existing algorithms on different software cost estimation datasets, and results showed the effectiveness of our proposed algorithm.
... The former was used to assess the appropriateness of ASEE techniques for a specific dataset and to address the problem of feature and case selection [20][21][22][23]. The latter was usually applied for model calibration and the computation of prediction intervals [24][25][26][27]. We investigated the use of FL in combination with ASEE in the selected studies: the main purpose of using FL was to handle linguistic attributes and to deal with imprecision and uncertainty. ...
Context
Analogy-based Software development Effort Estimation (ASEE) techniques have gained considerable attention from the software engineering community. However, to our knowledge, no systematic mapping has been created of ASEE studies and no review has been carried out to analyze the empirical evidence on the performance of ASEE techniques.
Objective
The objective of this research is twofold: (1) to classify ASEE papers according to five criteria: research approach, contribution type, techniques used in combination with ASEE methods, and ASEE steps, as well as identifying publication channels and trends; and (2) to analyze these studies from five perspectives: estimation accuracy, accuracy comparison, estimation context, impact of the techniques used in combination with ASEE methods, and ASEE tools.
Method
We performed a systematic mapping of ASEE studies published in the period 1990-2012, and reviewed them based on an automated search of four electronic databases.
Results
In total, we identified 65 studies published between 1990 and 2012, and classified them based on our predefined classification criteria. The mapping study revealed that most researchers focus on addressing problems related to the first step of an ASEE process, that is, feature and case subset selection. The results of our detailed analysis show that ASEE methods outperform the eight techniques with which they were compared, and tend to yield acceptable results especially when combining ASEE techniques with Fuzzy Logic (FL) or Genetic Algorithms (GA).
Conclusion
Based on the findings of this study, the use of other techniques such FL and GA in combination with an ASEE method is promising to generate more accurate estimates. However, the use of ASEE techniques by practitioners is still limited: developing more ASEE tools may facilitate the application of these techniques and then lead to increasing the use of ASEE techniques in industry.
... No one method is necessarily better or worse than the other, in fact, their strengths and weaknesses are often complimentary to each other. [1] Numerous researchers and scientists are constantly working on developing new software cost estimation techniques [2,3,4]. ...
Software cost estimation is the process of predicting the effort required to develop a software system. Accurate cost estimation helps us complete the project within time and budget. For completing the project in time and budget, one must have efficient estimation technique for predicting project efforts. Artificial neural network is a promising technique to provide efficient and good results when dealing with problems where there are complex relationship between inputs and outputs. Researchers proved better estimation using back propagation techniques like RBP and Bayesian regulation. In this paper further discussion will be about the study and the efficiency of Neural based one step secant back propagation based cost estimation model, Powell-Beale conjugate gradient model and Fletcher-reeves conjugate gradient model. Result is concluded with the best effort predicting model.
... As atividades de teste de software (TS) envolvem etapas definidas (planejamento, projeto de casos de teste, execução e avaliação dos resultados -Maldonado e Fabbri, 2001b) e devem ser realizadas sob o rigor da técnica e planejadas de acordo com métodos específicos para cada caso (Paula Filho, 2001). TS é, de fato, aspecto crucial do desenvolvimento de software (Huq, 2000), sobretudo quando se entende que o alinhamento estratégico de sistemas de informação (sistemas que incluem um componente software - Stamelos et al., 2003) é um dos maiores desafios dos executivos (Chan et al., 1997). ...
... The projects in the data set were completed between years 1989-2001 in over seven different countries. The data are used in many other studies and is publicly available from the International Software Benchmarking Standards Group [5,66,87]. ...
... Stamelos et. al. [19], and Mendes et. al. [11,12] compared between different types of distance metrics in analogy software estimation and revealed that using different distance metrics yield dissimilar results which indicate the importance of distance between software projects on effort estimation. ...
A reliable and accurate similarity measurement between two software projects has always been a challenge for analogy-based
software cost estimation. Since the effort for a new project is retrieved from similar historical projects, it is essentially
to use the appropriate similarity measure that finds those close projects which in turn increases the estimation accuracy.
In software engineering literature, there is a relatively little research addressed the issue of how to find out similarity
between two software projects when they are described by numerical and categorical features. Despite simplicity of exiting
similarity techniques such as: Euclidean distance, weighted Euclidean distance and maximum distance, it is hard to deal with
categorical features. In this paper we present two approaches to measure similarity between two software projects based on
fuzzy C-means clustering and fuzzy logic. The new approaches are suitable for both numerical and categorical features.
... The essence of Analogy estimation process is to assess similarity degree between software projects. (Stamelos, Angelis and Morisio, 2003) reported that using different similarity measures produce dissimilar results which indicate the importance of reliable measure on estimation accuracy. However, in earlier work, we have proposed and validated similarity measurement approach based on fuzzy Cmeans (Azzeh, Neagu, Cowling, 2008). ...
Analogy estimation is a well known approach for software effort estimation. The underlying assumption of this approach is the more similar the software project description attributes are, the more similar the software project effort is. One of the difficult activities in analogy estimation is how to derive a new estimate from retrieved solutions. Using retrieved solutions without adjustment to considered problem environment is not often sufficient. Thus, they need some adjustment to minimize variation between current case and retrieved cases. The main objective of the present paper is to investigate the applicability of fuzzy logic based software projects similarity measure to adjust analogy estimation and derive a new estimate. We proposed adaptation techniques which take into account the similarity between two software projects in terms of each feature. In earlier work, a similarity measure between software projects based on fuzzy Cmeans has been proposed and validated theoretically against some well known axioms such as: Normality, Symmetry, transitivity, etc. This similarity measure will be guided towards deriving a new estimate.
This research has developed a theoretical model that will help to improve productivity without reducing quality in software projects. It has been observed that while trying to increase productivity, quality gets hit in software projects. However, customer requirements in these projects demand that the projects be delivered on time without having defects or bugs in the final deliverables. Thus, it becomes important to identify variables that would increase productivity without compromising the quality. In the present study, through literature review different variables were identified that would affect both productivity and quality simultaneously. The data were collected from 36 software projects and were analysed to check the relationships between the identified variables, productivity and quality. Using structured equation modelling it was found that level of application complexity, training, level of client support, reusing existing code and quality of document management system significantly impact productivity without compromising quality. The findings can be used in projects engaged in customized software development as well as in commercial software development.
The field of software‐development effort estimation explores ways of defining effort through prediction approaches. Even though this field has a crucial impact on budgeting and project planning in industry, the number of works classifying and examining currently available approaches is still small. This article, therefore, presents a comprehensive overview of these approaches, and pinpoints research gaps, challenges and trends. A systematic mapping of the literature was designed and performed based on well‐established practical guidelines. In total, 120 primary studies were selected, analysed and categorised, after applying a careful filtering process from a sample of 3746 candidate studies to answer six research questions. Over 70% of the selected studies adopted multiple effort estimation approaches; over 45% adopted evaluation research as a research method; over 90% of the participants were students, rather than professionals; most studies had their quality assessed as high, and were most commonly published in journals. Our study benefits practitioners and researchers by providing a body of knowledge about the current literature, serving as a starting point for upcoming studies. This article reports challenges worth investigating, regarding the use of cognitive load and team interaction.
This study has mainly focused on the use of multinomial logistic regression in predicting employee satisfaction of the academics in Sri Lankan universities. A questionnaire was used to gather data from acdemics and it is prepared to collect demographic data and eight main factors. Demographic factors were analyzed with multinomial logistic regression, and it resulted in three elements namely, sector, salary, and gender. Before examining the main factors in the questionnaire, a reliability analysis was done. Factors were analyzed with multinomial logistic regression and resulted in different models and the best model out of all is presented in this paper. By comparing the models with R-squared values, goodness-of-fit statistics and residuals, the best model was obtained. This study revealed thatfitting of the abilities and knowledge with the job, ability to use the full potential in work, superior behavior and freedom are significant factors in predicting employee satisfaction of academics in Sri Lankan universities. Abstract-This study has mainly focused on the use of multinomial logistic regression in predicting employee satisfaction of the academics in Sri Lankan universities. A questionnaire was used to gather data from acdemics and it is prepared to collect demographic data and eight main factors. Demographic factors were analyzed with multinomial logistic regression, and it resulted in three elements namely, sector, salary, and gender. Before examining the main factors in the questionnaire, a reliability analysis was done. Factors were analyzed with multinomial logistic regression and resulted in different models and the best model out of all is presented in this paper. By comparing the models with R-squared values, goodness-of-fit statistics and residuals, the best model was obtained. This study revealed thatfitting of the abilities and knowledge with the job, ability to use the full potential in work, superior behavior and freedom are significant factors in predicting employee satisfaction of academics in Sri Lankan universities.
Programming planning estimation and examination especially, cost estimation exercises have been in the point of convergence of thought for a few associations. Maker explores the usage of the ace result declaration and machine learning methods using intelligent framework and moreover focusing COCOMO II method to manage estimate the cost of programming. Few basic techniques in the usage of neural framework in surveying programming cost. Made to great degree exact results, however the genuine incident in their work was a direct result of the way that the precision of the report depended enthusiastically on the degree of the planning set [4]. Getting the hardship in implementing neural frameworks, the maker makes a dynamic neural framework that would at first use COCOMO II. Sorting out upgrades and its results the amount of instructive gathering augmentations with commitment from ace finalizing that effects the studying strategy.
The software development (SD) has its own rules of designing, evolving, testing, packaging, and finally releasing which has not been studied widely with regard to its design calibrations to date. This paper introduces SD briefly to the readers by presenting main steps in SD, from need to documentation of a software, for both early and modern methods. At last, the paper suggests a method—with appropriate formulation for measuring the complexity of algorithms in a SD procedure—and solves examples based on it to show the fairness of the new idea. The method can be used generally to evaluate the total cost of a design based on known and unknown parameters that are mainly estimated based on designers' experiences.
Na indústria brasileira de desenvolvimento de software para sistemas de informações, observa-se sensibilidade das empresas quanto à necessária melhoria contínua na qualidade do processo produtivo. Uma das formas de tratar a qualidade do desenvolvimento de software – na expectativa de alinhar os processos de negócio do cliente às rotinas computacionalmente programadas no sistema de informações – dá-se por meio do teste de software. O teste de software busca institucionalizar práticas de gestão de projetos e desenvolvimento de produtos de software, com o objetivo de localizar os problemas – e não garantir a sua inexistência. A partir de uma perspectiva sociotécnica e dos modelos teóricos SET (gerencial) e ST-TS (processual) desenvolvidos nesta pesquisa para um melhor entendimento e orientação das atividades de teste, entrevistas em profundidade com dez especialistas em qualidade e teste de software permitiram a identificação e validação de categorias analíticas que mediam a relação entre fatores desses dois modelos, disto derivando-se o modelo organizacional VAST para auxiliar gestores, desenvolvedores e clientes em projetos de software para sistemas de informações empresariais.
Delivering an accurate estimate of software development effort plays a decisive role in successful management of a software project. Therefore, several effort estimation techniques have been proposed including analogy based techniques. However, despite the large number of proposed techniques, none has outperformed the others in all circumstances and previous studies have recommended generating estimation from ensembles of various single techniques rather than using only one solo technique. Hence, this paper proposes two types of homogeneous ensembles based on single Classical Analogy or single Fuzzy Analogy for the first time. To evaluate this proposal, we conducted an empirical study with 100/60 variants of Classical/Fuzzy Analogy techniques respectively. These variants were assessed using standardized accuracy and effect size criteria over seven datasets. Thereafter, these variants were clustered using the Scott-Knott statistical test and ranked using four unbiased errors measures. Moreover, three linear combiners were used to combine the single estimates. The results show that there is no best single Classical/Fuzzy Analogy technique across all datasets, and the constructed ensembles (Classical/Fuzzy Analogy ensembles) are often ranked first and their performances are, in general, higher than the single techniques. Furthermore, Fuzzy Analogy ensembles achieve better performance than Classical Analogy ensembles and there is no best Classical/Fuzzy ensemble across all datasets and no evidence concerning the best combiner.
Case-based reasoning represents a memory-based, data-driven estimation method. In other words, it is an estimation method in which estimates are based solely on the analysis of quantitative project data and in which the data need to be available at the time of estimation.
Software development is an inherently uncertain activity. To deal with the uncertainty and vagueness from humans’ subjective perception and experience in decision process, this chapter proposes a disciplined evaluation approach to improve the quality of decision-making in the software development project under uncertain conditions. We first present an evaluation concept and the necessary evaluation models and techniques that help the evaluators to reduce their risks under uncertainty in software development process. Next, we propose a methodology based on the extended fuzzy analytic hierarchy process modeling to assess the adequate economic (tangible) and quality (intangible) balance. Two key factors of economic and quality are evaluated separately by fuzzy approaches and both factors’ estimates are combined to obtain the preference degree associated with each software development project strategy alternatives for selecting the most appropriate one. Using the proposed approach, the ambiguities involved in the assessment data can be effectively represented and processed to assure a more convincing and effective decision-making process.
Despite the fact that IT investment evaluation has been a well established research area for many years, researchers still struggle to gain a common understanding of how to define and evaluate such evaluation. In particular, the existing literature outlines different views on what perspectives and criteria should guide the evaluation process. This paper provides a literature review of different perspectives for evaluating IT investments in organizations. In doing so, it identifies different evaluation perspectives, each having their own criteria and rationale. The identified perspectives are strategic match, impact on organization, impact on surroundings, stakeholders view, IT specific perspective, benefits, costs, risks & uncertainty, and project organizing. In view of a fragmented research field, the paper contributes a comprehensive overview of the evaluation literature focused on IT investments in organizations with regard to perspectives and their criteria. Implications for research and practice are presented.
Enterprise Resource Planning (ERP) packages are information systems that automate the business processes of organizations thereby improving their operational efficiency substantially. ERP projects that involve customization are often affected by inaccurate estimation of efforts. Size of the software forms the basis for effort estimation. Methods used for effort estimation either employ function points (FP) or lines of code (LOC) to measure the size of customized ERP packages. Literature review reveals that the existing software size methods which are meant for custom-built software products may not be suitable for COTS products such as customized ERP packages. Hence, the effort estimation using conventional methods for customized ERP packages may not be accurate. This paper proposes a new approach to estimate the size of customized ERP packages using Package Points (PP). The proposed approach was validated with data collected from 14 ERP projects delivered by the same company. A positive correlation was observed between Package Points (PP) and the efforts of these projects. This result indicates the feasibility of our proposed approach as well as the positive climate for its utility by the project managers of future ERP projects. Lastly, we examine the implication of these results for practice and future research scope.
Data quality is an important issue which has been addressed and recognised in research
communities such as data warehousing, data mining and information systems. It has been
agreed that poor data quality will impact the quality of results of analyses and that it
will therefore impact on decisions made on the basis of these results. Empirical software
engineering has neglected the issue of data quality to some extent. This fact poses the
question of how researchers in empirical software engineering can trust their results without
addressing the quality of the analysed data. One widely accepted definition for data quality
describes it as ‘fitness for purpose’, and the issue of poor data quality can be addressed by
either introducing preventative measures or by applying means to cope with data quality
issues. The research presented in this thesis addresses the latter with the special focus on
noise handling.
Three noise handling techniques, which utilise decision trees, are proposed for application
to software engineering data sets. Each technique represents a noise handling approach:
robust filtering, where training and test sets are the same; predictive filtering, where training
and test sets are different; and filtering and polish, where noisy instances are corrected. The
techniques were first evaluated in two different investigations by applying them to a large
real world software engineering data set. In the first investigation the techniques’ ability to
improve predictive accuracy in differing noise levels was tested. All three techniques improved
predictive accuracy in comparison to the do-nothing approach. The filtering and polish was
the most successful technique in improving predictive accuracy. The second investigation
utilising the large real world software engineering data set tested the techniques’ ability to
identify instances with implausible values. These instances were flagged for the purpose of
evaluation before applying the three techniques. Robust filtering and predictive filtering
decreased the number of instances with implausible values, but substantially decreased the
size of the data set too. The filtering and polish technique actually increased the number of
implausible values, but it did not reduce the size of the data set.
Since the data set contained historical software project data, it was not possible to know
the real extent of noise detected. This led to the production of simulated software engineering
data sets, which were modelled on the real data set used in the previous evaluations to ensure
domain specific characteristics. These simulated versions of the data set were then injected
with noise, such that the real extent of the noise was known. After the noise injection
the three noise handling techniques were applied to allow evaluation. This procedure of
simulating software engineering data sets combined the incorporation of domain specific
characteristics of the real world with the control over the simulated data. This is seen as a
special strength of this evaluation approach.
The results of the evaluation of the simulation showed that none of the techniques performed well. Robust filtering and filtering and polish performed very poorly, and based on
the results of this evaluation they would not be recommended for the task of noise reduction.
The predictive filtering technique was the best performing technique in this evaluation, but
it did not perform significantly well either.
An exhaustive systematic literature review has been carried out investigating to what extent the empirical software engineering community has considered data quality. The findings
showed that the issue of data quality has been largely neglected by the empirical software
engineering community.
The work in this thesis highlights an important gap in empirical software engineering. It
provided clarification and distinctions of the terms noise and outliers. Noise and outliers are
overlapping, but they are fundamentally different. Since noise and outliers are often treated
the same in noise handling techniques, a clarification of the two terms was necessary.
To investigate the capabilities of noise handling techniques a single investigation was
deemed as insufficient. The reasons for this are that the distinction between noise and
outliers is not trivial, and that the investigated noise cleaning techniques are derived from
traditional noise handling techniques where noise and outliers are combined. Therefore
three investigations were undertaken to assess the effectiveness of the three presented noise
handling techniques. Each investigation should be seen as a part of a multi-pronged approach.
This thesis also highlights possible shortcomings of current automated noise handling
techniques. The poor performance of the three techniques led to the conclusion that noise
handling should be integrated into a data cleaning process where the input of domain knowledge and the replicability of the data cleaning process are ensured.
One of the most valuable asset in any software industry is the correct estimation of effort and hence cost estimation of the software to be developed by them. Because of the highly dynamic nature of the Software development, it becomes more and more difficult to get a correct software effort estimation and software cost estimation, which is one of the most important factor which makes software more competitive and is essential for controlling Software Development Cost. Software Cost Estimation is one of the challenging managerial activity, because values of many of the variables are not known and not easy to predict at an early stage of Software Development. An ideal Software Cost Estimation Model should provide ample confidence, precision and accuracy from its predictions. In this paper, we have performed an analysis of most of the algorithmic techniques which has been developed till now for Software Cost Estimation. We have also tried to analyze the advantages and shortcomings of every technique.
Context
The International Software Benchmarking Standards Group (ISBSG) maintains a software development repository with over 6,000 software projects. This dataset makes it possible to estimate a project’s size, effort, duration, and cost.
Objective
The aim of this study was to determine how and to what extent, ISBSG has been used by researchers from 2000, when the first papers were published, until June of 2012.
Method
A systematic mapping review was used as the research method, which was applied to over 129 papers obtained after the filtering process.
Results
The papers were published in 19 journals and 40 conferences. Thirty-five percent of the papers published between years 2000 and 2011 have received at least one citation in journals and only five papers have received six or more citations. Effort variable is the focus of 70.5% of the papers, 22.5% center their research in a variable different from effort and 7% do not consider any target variable. Additionally, in as many as 70.5% of papers, effort estimation is the research topic, followed by dataset properties (36.4%). The more frequent methods are Regression (61.2%), Machine Learning (35.7%), and Estimation by Analogy (22.5%). ISBSG is used as the only support in 55% of the papers while the remaining papers use complementary datasets. The ISBSG release 10 is used most frequently with 32 references. Finally, some benefits and drawbacks of the usage of ISBSG have been highlighted.
Conclusion
This work presents a snapshot of the existing usage of ISBSG in software development research. ISBSG offers a wealth of information regarding practices from a wide range of organizations, applications, and development types, which constitutes its main potential. However, a data preparation process is required before any analysis. Lastly, the potential of ISBSG to develop new research is also outlined.
The use of computers and Information Technology (IT) solutions is a vital necessity for enterprises, which requires continuously increasing investments in hardware and software applications. Prior to any software development project estimation of cost has to be carried out. Because of the complex nature of software applications, it is often difficult to predict the cost of software development accurately. Recently, various methods have been proposed by researchers to predict the effort of software projects and estimate the cost accordingly. In this study, first a discussion on the major available models for software cost estimation along with their strengths and weaknesses is presented. Next, using Genetic Algorithms (GAs), three new models are introduced in order to estimate the cost of software development projects. The performances of these three models are tested using real data. The results show that the proposed models are able to provide better estimates in comparison to previously discussed models.
Accurate estimates of efforts in software development are necessary in project management practices. Project managers or domain experts usually conduct software effort estimation using their experience; hence, subjective or implicit estimates occur frequently. As most software projects have incomplete information and uncertain relations between effort drivers and the required development effort, the grey relational analysis (GRA) method has been applied in building a formal software effort estimation model for this study. The GRA in the grey system theory is a problem-solving method that is used when dealing with similarity measures of complex relations. This paper examines the potentials of the software effort estimation model by integrating a genetic algorithm (GA) to the GRA. The GA method is adopted to find the best fit of weights for each software effort driver in the similarity measures. Experimental results show that the software effort estimation using an integration of the GRA with GA method presents more precise estimates over the results using the case-based reasoning (CBR), classification and regression trees (CART), and artificial neural networks (ANN) methods.
A reliable and accurate estimate of software development effort has always been a challenge for both the software industry and academia. Analogy is a widely adopted problem solving technique that has been evaluated and confirmed in software effort or cost estimation domains. Similarity measures between pairs of effort drivers play a central role in analogy-based estimation models. However, hardly any research has addressed the issue of how to decide on suitable weighted similarity measures for software effort drivers. The present paper investigates the effect on estimation accuracy of the adoption of genetic algorithm (GA) to determine the appropriate weighted similarity measures of effort drivers in analogy-based software effort estimation models. Three weighted analogy methods, namely, the unequally weighted, the linearly weighted and the nonlinearly weighted methods are investigated in the present paper. We illustrate our approaches with data obtained from the International Software Benchmarking Standards Group (ISBSG) repository and the IBM DP services database. The experimental results show that applying GA to determine suitable weighted similarity measures of software effort drivers in analogy-based software effort estimation models is a feasible approach to improving the accuracy of software effort estimates. It also demonstrates that the nonlinearly weighted analogy method presents better estimate accuracy over the results obtained using the other methods.
Analogy-based estimation is a widely adopted problem solving method that has been evaluated and confirmed in software effort or cost estimation domains. The similarity measures between pairs of projects play a critical role in the analogy-based software effort estimation models. Such a model calculates a distance between the software project being estimated and each of the historical software projects, and then retrieves the most similar project for generating an effort estimate. Although there exist numerous analogy-based software effort estimation models in literature, little theoretical or experimental works have been reported on the method of deriving an effort estimate from the adjustment of the reused effort based on the similarity distance. The present paper investigates the effect on the improvement of estimation accuracy in analogy-based estimations when the genetic algorithm method is adopted to adjust reused effort based on the similarity distances between pairs of projects. The empirical results show that applying a suitable linear model to adjust the analogy-based estimations is a feasible approach to improving the accuracy of software effort estimates. It also demonstrates that the proposed model is comparable with those obtained when using other effort estimation methods.
Analogy-based estimation is a widely adopted method in software cost estimation that identifies analogous projects to the one under estimation and uses their data to derive an estimate, i.e. it is a Case Based Reasoning approach. The similarity measures between pairs of projects are critical for identifying the most appropriate historical data from which the estimation will be generated. Usually the similarity measures are selected empirically, using jackknife-like procedures. Typically, the measures that identify the most similar projects in most of the cases are considered the most appropriate ones and are applied in every new estimation procedure. However there are situations that the default similarity measures may not be the most appropriate ones. In this study we determine the situations in which the default parameters are not the best and we propose the similarity measures for these cases. In particular we provide rules that point out which projects are not accurately estimated with the default parameters.
Traditionally, software measurement literature considers the uncertainty of cost drivers in project estimation as a challenge
and treats it as such. This paper develops the position that uncertainty can be seen as an asset. It draws on results of a
case study in which we replicated an approach to balancing uncertainties of project context characteristics in requirements-based
effort estimation for ERP implementations.
Uncertainty is a crucial element in managing projects. This paper’s aim is to shed some light into the issue of uncertain
context factors when estimating the effort needed for implementing enterprise resource planning (ERP) projects. We outline
a solution approach to this issue. It complementarily deploys three techniques to allow a tradeoff between ERP projects requiring
more effort than expected and those requiring less. We present the results of a case study carried out in a telecommunication
company site.
This research examines the use of the International Software Benchmarking Standards Group (ISBSG) repository, which is a large database of completed software projects from different organisations, for estimating the required effort for new software projects. The accuracy of the estimates based on this repository is compared with the results obtained from using a one-company data set from a company called Megatec. This study investigates two questions: (1) What are the differences in accuracy between a traditional technique such as ordinary least-squares (OLS) regression and Analogy-based estimation? (2) Is there a difference between estimates derived from multi-company data and estimates derived from company-specific data? Regarding the first question, our results show that OLS regression performs as well (when based on one-company data) and significantly better than (when based on multi-organisational data) Analogy-based estimation. This result is in contrast to previous studies that showed promising results applying Analogy on software engineering data. On the other hand, the result confirms the outcomes of investigating Analogy on another large multi-organisational database (called Laturi) from the business applications domain. Addressing the second question, we found two results. When applying Analogy, significantly more accurate models could be built based on company-specific data than based on multi-organisational data. The results reveal that Analogy-based procedures do not seem as robust when using data external to the organisation for which the model is built. When applying OLS regression, no significant advantage was found when using local, company-specific data opposed to multi-organisational data. Again, this result is consistent with a previously perform...
Estimation of a software project effort, based on project analogies, is a promising method in the area of software cost estimation. Projects in a historical database, that are analogous (similar) to the project under examination, are detected, and their effort data are used to produce estimates. As in all software cost estimation approaches, important decisions must be made regarding certain parameters, in order to calibrate with local data and obtain reliable estimates. In this paper, we present a statistical simulation tool, namely the bootstrap method, which helps the user in tuning the analogy approach before application to real projects. This is an essential step of the method, because if inappropriate values for the parameters are selected in the first place, the estimate will be inevitably wrong. Additionally, we show how measures of accuracy and in particular, confidence intervals, may be computed for the analogy-based estimates, using the bootstrap method with different assumptions about the population distribution of the data set. Estimate confidence intervals are necessary in order to assess point estimate accuracy and assist risk analysis and project planning. Examples of bootstrap confidence intervals and a comparison with regression models are presented on well-known cost data sets.
Manufacturing enterprises are being forced into greater collaboration with customers and suppliers in order to produce quality products in smaller batches, shorter lead times and with greater variety. Consequently, the design-for-manufacturing task must be conducted in these virtual and distributed enterprises across traditional organizational boundaries. This paper proposes the use of standard information models to support the product realization process. While extensive work has been performed in developing product data models little effort has been performed in developing a manufacturing model. The design-for-manufacturing stages are identified with their requisite information requirements. Different approaches used to model various aspects of manufacturing processes are reviewed and found inadequate for supporting the entire design-for-manufacturing task. The development of a standard manufacturing systems information model written in EXPRESS and based upon the modelling me thodology adhered to by standard for the exchange of product (STEP) is proposed to fill the void. Initial developments in this area are discussed, the model is illustrated with an example, and the potential benefits to manufacturing are reviewed.
The globalisation of markets is having a profound effect on
business and information technology strategies of individual
organisations. The move away from nationally focused business units to a
global product-market focus requires an effective international
coordination of a firm's activities. To support a global outlook, many
firms are implementing enterprise resource planning (ERP) systems.
Although ERP has become the de facto standard for international
organisations there are few documented examples of implementation. This
paper seeks to make a contribution to this important area. A case study
analysis of the strategic context and implementation of a global ERP
project in a multinational textiles group is presented. It illustrates
the transformation of a conglomerate of nationally organised businesses
into a pan-European organisation. The case analysis demonstrates the
organisational and technical complexity of ERP implementation and
identifies the factors that determined the total cost of the system.
Opportunities for future research are outlined
Practitioners have expressed concern over their inability to accurately estimate costs associated with software development. This concern has become even more pressing as costs associated with development continue to increase. As a result, considerable research attention is now directed at gaining a better understanding of the software-development process as well as constructing and evaluating software cost estimating tools. This paper evaluates four of the most popular algorithmic models used to estimate software costs (SLIM, COCOMO, Function Points, and ESTIMACS). Data on 15 large completed business data-processing projects were collected and used to test the accuracy of the models' ex post effort estimation. One important result was that Albrecht's Function Points effort estimation model was validated by the independent data provided in this study [3]. The models not developed in business data-processing environments showed significant need for calibration. As models of the software-development process, all of the models tested failed to sufficiently reflect the underlying factors affecting productivity. Further research will be required to develop understanding in this area.
An effective IT infrastructure can support a business vision and strategy; a poor, decentralized one can break a company. More and more companies are turning to off-the-shelf ERP (enterprise resource planning) solutions for IT planning and legacy systems management. The authors have developed a framework to help managers successfully plan and implement an ERP project
The authors investigate the accuracy of cost estimates when applying most commonly used modeling techniques to a large-scale industrial data set which is professionally maintained by the International Software Standards Benchmarking Group (ISBSG). The modeling techniques applied are ordinary least squares regression (OLS), analogy based estimation, stepwise ANOVA, CART, and robust regression. The questions addresses in the study are related to important issues. The first is the appropriate selection of a technique in a given context. The second is the assessment of the feasibility of using multi-organizational data compared to the benefits from company-specific data collection. We compare company-specific models with models based on multi-company data. This is done by using the estimates derived for one company that contributed to the ISBSG data set and estimates from using carefully matched data from the rest of the ISBSG data. When using the ISBSG data set to derive estimates for the company, generally poor results were obtained. Robust regression and OLS performed most accurately. When using the company's own data as the basis for estimation, OLS, a CART-variant, and analogy performed best. In contrast to previous studies, the estimation accuracy when using the company's data is significantly higher than when using the rest of the ISBSG data set. Thus, from these results, the company that contributed to the ISBSG data set, would be better off when using its own data for cost estimation
Accurate project effort prediction is an important goal for the
software engineering community. To date most work has focused upon
building algorithmic models of effort, for example COCOMO. These can be
calibrated to local environments. We describe an alternative approach to
estimation based upon the use of analogies. The underlying principle is
to characterize projects in terms of features (for example, the number
of interfaces, the development method or the size of the functional
requirements document). Completed projects are stored and then the
problem becomes one of finding the most similar projects to the one for
which a prediction is required. Similarity is defined as Euclidean
distance in n-dimensional space where n is the number of project
features. Each dimension is standardized so all dimensions have equal
weight. The known effort values of the nearest neighbors to the new
project are then used as the basis for the prediction. The process is
automated using a PC-based tool known as ANGEL. The method is validated
on nine different industrial datasets (a total of 275 projects) and in
all cases analogy outperforms algorithmic models based upon stepwise
regression. From this work we argue that estimation by analogy is a
viable technique that, at the very least, can be used by project
managers to complement current estimation techniques
A summary is presented of the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.
Estimation by analogy is a technique that has been proposed since a long time as a valid alternative to algorithmic cost estimation and expert judgement. On the other hand, it is widely accepted that public domain software cost estimation techniques need to be calibrated, i.e. properly adjusted, to historical project data, in order to produce accurate estimates for new projects. Another important requirement for any modern cost estimation method is the ability to produce interval estimates, not mere point estimates. Statistical simulation techniques may be used for both purposes. The paper presents BRACE, a software tool that supports the practical application of the analogy based method using a simulation approach, namely bootstrap.
An abstract is not available.
It has been found from the contemporary research in the "elds of concurrent engineering and supply chain management that signi"cant bene"ts can be achieved if suppliers are involved in the early stages of product development process. However, recent investigation in manufacturing industries has also revealed that this approach is not widely practised in industries and its implementation has been a great challenge to researchers and practitioners. The research reported here proposes to develop an overall methodology for enabling better supplier involvement in new product development process and to demonstrate the framework through a prototype web-based platform on the Internet/intranets using the web technology. This paper presents some results from the initial investigation, development and implementation of the proof-of-the-concept prototype system called WeBid.
This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.
To help industrialize the use of formal description techniques in the software development of communication protocols, improving the software project management is as equally important as improving the software development techniques. An early software project estimation is a prerequisite for the quantitative software project management to be started early in the development life cycle. However, relatively little work has been done on such an issue in the formal communication protocol development environment. This paper proposes a two-stage software sizing process and product decomposition technique for establishing Estelle specification and implementation size models tailored to the formal communication protocol development environment. Based on the size estimates obtained from the size models above, this paper also presents the practical application in conducting a software cost model COCOMO II to estimate the development cost and project schedule of the ISO ROSE protocol development.
Although typically a software development organisation is involved in more than one project simultaneously, the available tools in the area of software cost estimation deal mostly with single software projects. In order to calculate the possible cost of the entire project portfolio, one must combine the single project estimates taking into account the uncertainty involved. In this paper, statistical simulation techniques are used to calculate confidence intervals for the effort needed for a project portfolio. The overall approach is illustrated through the adaptation of the analogy-based method for software cost estimation to cover multiple projects.
In function point analysis, fourteen “general systems characteristics” (GSCs) are used to construct a “value adjustment factor” (VAF), with which a basic function point count is adjusted. Although the GSCs and VAF have been criticized on both theoretical and practical grounds, they are used by many practitioners. This paper reports on an empirical investigation into their use and practical value. We conclude that recording the GSCs may be useful for understanding project cost drivers and for comparing similar projects, but the VAF should not be used: doubts about its construction are not balanced by any practical benefit. A new formulation is needed for using the GSCs to explain effort; factors identified here could guide further research.
This research examined the use of the International Software Benchmarking Standards Group (ISBSG) repository for estimating effort for software projects in an organization not involved in ISBSG. The study investigates two questions: (1) What are the differences in accuracy between ordinary least-squares (OLS) regression and Analogy-based estimation? (2) Is there a difference in accuracy between estimates derived from the multi-company ISBSG data and estimates derived from company-specific data? Regarding the first question, we found that OLS regression performed as well as Analogy-based estimation when using company-specific data for model building. Using multi-company data the OLS regression model provided significantly more accurate results than Analogy-based predictions. Addressing the second question, we found in general that models based on the company-specific data resulted in significantly more accurate estimates.
Conventional approaches to software cost estimation have focused on algorithmic cost models, where an estimate of effort is calculated from one or more numerical inputs via a mathematical model. Analogy-based estimation has recently emerged as a promising approach, with comparable accuracy to algorithmic methods in some studies, and it is potentially easier to understand and apply. The current study compares several methods of analogy-based software effort estimation with each other and also with a simple linear regression model. The results show that people are better than tools at selecting analogues for the data set used in this study. Estimates based on their selections, with a linear size adjustment to the analogue's effort value, proved more accurate than estimates based on analogues selected by tools, and also more accurate than estimates based on the simple regression model.
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
The paper explores the possibility of generating a
multi-organisational software cost estimation model by analysing the
software cost data collected by the International Software Benchmarking
Standards Group. This database contains data about recently developed
projects characterised mostly by attributes of categorical nature such
as the project business area, organisation type, application domain and
usage of certain tools or methods. The generation of the model is based
on a statistical technique which has been proposed as alternative to the
standard regression approach, namely the categorical regression or
regression with optimal scaling. This technique starts with the
quantification of the qualitative attributes (expressed either on
nominal or ordinal scale), that appear frequently within such data, and
proceeds by using the obtained scores as independent variables of a
regression model. The generated model is validated by measuring certain
indicators of accuracy
The authors discuss the sources of uncertainty and risk, their
implications for software organizations, and how risk and uncertainty
can be managed. Specifically, they assert that uncertainty and risk
cannot be managed effectively at the individual project level. These
factors must be considered in an organizational context
One of the most important problems faced by software developers and users is the prediction of the size of a programming system and its development effort. As an alternative to "size," one might deal with a measure of the "function" that the software is to perform. Albrecht [1] has developed a methodology to estimate the amount of the "function" the software is to perform, in terms of the data it is to use (absorb) and to generate (produce). The "function" is quantified as "function points," essentially, a weighted sum of the numbers of "inputs," "outputs,"master files," and "inquiries" provided to, or generated by, the software. This paper demonstrates the equivalence between Albrecht's external input/output data flow representative of a program (the "function points" metric) and Halstead's [2] "software science" or "software linguistics" model of a program as well as the "soft content" variation of Halstead's model suggested by Gaffney [7].
To have general validity, empirical results must converge. To be credible, an experimental science must understand the limitations and be able to explain the disagreements of empirical results. We describe an experiment to replicate previous studies which claim that estimation by analogy outperforms regression models. In the experiment, 68 experienced practitioners each estimated a project from a dataset of 48 industrial COTS projects. We applied two treatments, an analogy tool and a regression model, and we used the estimating performance when aided by the historical data as the control. We found that our results do not converge with previous results. The reason is that previous studies have used other datasets and partially different data analysis methods, and last but not least, the tools have been validated in isolation from the tool users. This implies that the results are sensitive to the experimental design: the characteristics of the dataset, the norms for removing outliers and other data points from the original dataset, the test metrics, significance levels, and the use of human subjects and their level of expertise. Thus, neither our results nor previous results are robust enough to claim any general validity.
Acquiring Enterprise Software
- J Verville
- A Halingten
J. Verville, A. Halingten, Acquiring Enterprise Software, Prentice-Hall, Upper Saddle River, NJ, 2001.
Size adjusted median 4 MTS, PPL 42.90 57.14 TRADEOFF 30 Canberra Size adjustment 1 FP
- Kaufman
- Rousseuw
- Vaf
- Rl
- Dp
- Lt
- Ppl
- Id
- Ot
Kaufman–Rousseuw Size adjusted median 4 MTS, PPL 42.90 57.14 TRADEOFF 30 Canberra Size adjustment 1 FP, VAF, RL, DP, LT, PPL, ID, OT 28.84 46.67 TRADEOFF-H 26
Approaching Information Week
- R Adhikari
R. Adhikari, Approaching 2000, Information Week, October 7, 1996, p. 44.
- C Laudon
- J Laudon
C. Laudon, J. Laudon, Management Information Systems, 7th
ed., Prentice-Hall, Upper Saddle River, NJ, 2002.
Counting Practices Manual, Release 4.0, International Function Point Users Group
- Ifpug
IFPUG, Counting Practices Manual, Release 4.0, International Function Point Users Group, Westerville, OH, 1994.