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Software cost estimation is of the most challenging task in project management in order to ensuring smoother development operation and target achievement. There has been evolution of various standards tools and techniques for cost estimation practiced in the industry at present times. However, it was never investigated about the overall picturizati...
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Citations
... Among these,the analogy-based method stands out as one of the most popular software cost estimation methods. Inspired by instinctive decision-making of humans, this method was initially employed in software cost estimation in 1997 and has since gained widespread adoption [3]. The accuracy of cost estimation in the analogy-based method is dependent on four components similarity function, historical project sets, the number of the closest similarities, and the solution function. ...
Human effort is considered one of the most important factors for a better estimation of software development costs. Therefore, the cost of software product development is usually seen based on the amount of effort required for its development. Numerous methods and paradigms are provided for software project estimation. The analogy-based method is one of the simplest and most prevalent methods. There have been many attempts to improve this method, yet, it seems it still can be improved. This research aims to provide a novel model and make use of the gray wolf optimization algorithm inside the similarity function to enhance the analogy-based software development cost. The provided method is compared to four metaheuristic and evolutionary algorithms using four prominent datasets. The evaluation results according to the MMRE, MdMRE, and PRED (0.25) indicate the proposed method provides a higher precision in estimating the analogy-based software development cost.
... These methods and models, are focused on particular criteria or parameters. However, none of them estimate the required cost and effort with 100% precision, performing better or worse -based on their context-specific situations [3,4]. Therefore, managers and developers should choose an approach that fits their project and intended productions. ...
... Leung et al. [21] classified cost estimation methods into algorithmic and non-algorithmic. Many articles, including [22,23], used this categorization. Bingamawa and Kamalrudin [24] also partly used this categorization and added a new category called hybrid, which represents a combination of algorithmic and non-algorithmic methods. ...
Accurate effort estimation in software products is all-important and indispensable. So far, different taxonomies have been proposed in the literature of software cost estimation. Each taxonomy has specific principles and factors and is utilized in particular applications. In recent years, there has been significant progress in this field of research. Many papers have adopted different strategies to the problem of software cost estimation; each one proposes a relative taxonomy concerning a specific approach. In this paper, we introduce a novel taxonomy based on techniques presented in the field of software cost estimation.KeywordsSoftware cost estimationSoftware estimation modelsArtificial intelligenceExpert judgmentCOCOMOTaxonomy
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Augmented reality acts as an add-on to teachers while teaching students, and this helps the teachers and students to have an interactive session. Augmented reality’s usage in education is cited as one of the major changes in the educational sector. Thus, the work carried out makes a positive impact in the educational industry. Augmented reality provides features like image recogntion, motion tracking, facial recognition, plane detection, etc., to provide interactive sessions. Simultaneous localization and mapping and concurrent odometry and mapping have proved to be efficient algorithms for augmented reality on mobile devices. The work carried out allows students to view interactive newspapers while reading a specific article. It also allows them to view a dynamic three-dimensional model of the solar system on their smartphone using augmented reality.
... However, companies are still facing the issue of choosing which cost estimation technique to use [1], [2]. Throughout the years a number of cost estimation techniques have emerged [8], [9]. These techniques vary from model-based techniques such as COCOMO and Function points where a model is followed with a set of steps and guidelines to come up with a relatively accurate software cost estimation [10], [11]. ...
The accurate software cost estimation is a critical factor in the success of software projects. Multiple method has been proposed to estimate the cost and the size of the software projects. These cost estimation methods have their advantages and disadvantages, selecting the right method is important to get accurate estimations. However, determining which method is the right one is difficult. In this paper we aim to explore the different cost estimation methods to help organizations in determining which of these methods is the most suitable and for what types of projects. This paper presents Software Cost Estimation Using Expert Judgement, COCOMO and Function Points: A Comparative Study. We look at the following methods to estimate the software cost: Expert Judgement, COCOMO and Function Points. Then through a case study we compare the estimations of these three methods to the actual cost of the software development.
... The experimental effects display that the presented method achieves provides excellent protection as well as it minimizes storage cost and reduces bandwidth. Naik and Nayak [31] have presented its involvement by demonstrating classifications of predictable cost-estimation methods and after that investigates the investigation trends towards commonly addressed issues in it. Kulkarni control of grid power technique by using the remote cloud server. ...
Irrespective of different forms and strategies implementing for securing Wireless Sensor Network (WSN), there are very less strategies that offers cost effective security over heterogeneous network. Therefore, this paper presents an integrated set of different processes that emphasize over secure routing, intellectual and delay-compensated routing, and optimization principle with a sole intention of securing the communication to and from the sensor nodes during data aggregation. The processed system advocates the non-usage of complex cryptography and encourages the usage of probability their and analytical modelling in order to render more practical implementation. The simulated outcome of study shows that proposed system offers reduced delay, more throughputs, and reduced energy consumption in contrast to existing system.
... CFP is one of the recent FSM methods. Function Point variants are mainly used in software cost estimation [2] and productivity of the development organisations [3]. Function Point is also a good indicator in identifying business complexity of the software [4]. ...
Cosmic Function Point (CFP) measurement errors leads budget, schedule and quality problems in software projects. Therefore, it’s important to identify and plan requirements engineers’ CFP training need quickly and correctly. The purpose of this paper is to identify software requirements engineers’ COSMIC Function Point measurement competence development need by using machine learning algorithms and requirements artifacts created by engineers. Used artifacts have been provided by a large service and technology company ecosystem in Telco. First, feature set has been extracted from the requirements model at hand. To do the data preparation for educational data mining, requirements and COSMIC Function Point (CFP) audit documents have been converted into CFP data set based on the designed feature set. This data set has been used to train and test the machine learning models by designing two different experiment settings to reach statistically significant results. Ten different machine learning algorithms have been used. Finally, algorithm performances have been compared with a baseline and each other to find the best performing models on this data set. In conclusion, REPTree, OneR, and Support Vector Machines (SVM) with Sequential Minimal Optimization (SMO) algorithms achieved top performance in forecasting requirements engineers’ CFP training need.
... CFP is one of the recent FSM methods. Function Point variants are mainly used in software cost estimation [2] and productivity of the development organisations [3]. Function Point is also a good indicator in identifying business complexity of the software [4]. ...
Cosmic Function Point (CFP) measurement errors leads budget, schedule and quality problems in software projects. Therefore, it's important to identify and plan requirements engineers' CFP training need quickly and correctly. The purpose of this paper is to identify software requirements engineers' COSMIC Function Point measurement competence development need by using machine learning algorithms and requirements artifacts created by engineers. Used artifacts have been provided by a large service and technology company ecosystem in Telco. First, feature set has been extracted from the requirements model at hand. To do the data preparation for educational data mining, requirements and COSMIC Function Point (CFP) audit documents have been converted into CFP data set based on the designed feature set. This data set has been used to train and test the machine learning models by designing two different experiment settings to reach statistically significant results. Ten different machine learning algorithms have been used. Finally, algorithm performances have been compared with a baseline and each other to find the best performing models on this data set. In conclusion, REPTree, OneR, and Support Vector Machines (SVM) with Sequential Minimal Optimization (SMO) algorithms achieved top performance in forecasting requirements engineers' CFP training need.
With the evolution of pervasive and ubiquitous application, the rise of web-based application as well as its components is quite rising as such applications are used both for development and analysis of the web component by developers. The estimation of software cost is controlled by multiple factors right from human-driven to process driven. Most importantly, some of the factors are never even can be guessed. At present, there are no records of literature to offer a robust cost estimation model to address this problem. Therefore, the proposed system introduces an intellectual model of software cost model that is mainly targets to perform optimization of entire cost estimation modeling by incorporating predictive approach. Powered by deep learning approach, the outcome of the proposed model is found to be cost effective in comparison to existing cost estimation modeling.
