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The International Software Benchmarking Standards Group (ISBSG) provides the Software Engineering community with a repository of project data which, up to now, have been used mostly for benchmarking and for estimating project effort. The 2005 version of the ISBSG repository includes data on more than 3,000 projects from various countries, sized wit...
Contexts in source publication
Context 1
... ISO 9126-1 quality model is defined as "a frame- work which explains the relationship between different ap- proaches to quality" [3], and distinguishes three views of software quality: internal quality, external quality and qual- ity in use (Figure 1): ...
Context 2
... set of ISO 9126 quality views in Figure 1 is based on the belief that internal quality has an impact on external quality, which in turn has an impact on quality in use. There- fore, the achievement of quality in use depends to some extent on the achievement of external quality, which in turn depends on the achievement of the internal quality of the software product itself. ...
Context 3
... ISO 9126 quality model refers to three types of quality shown in Figure 1 (internal quality, external quality and quality in use) covering the various phases of the SLC. The ISBSG questionnaire for project data collection is avail- able on their Web site, and has been used as the key input to analyze whether or not the ISBSG repository contains the appropriate information for using ISO models of software quality. ...
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Citations
... In [23] International Software Benchmarking Standards Group (ISBSG) repository was used to explore the scope of its uses for software product quality benchmarking on the basis of ISO 9126. Therefore the purpose of benchmarking software product quality in [24] ISBSG was chosen as a repository for software projects to find if it can be used for benchmarking product quality characteristics from ISO 9126 point of view". ...
The International Software Benchmarking Standards Group (ISBSG) development and enhancement dataset is a source of data using by academia and industry over around the world. It contains several software projects developed and/or enhanced in different countries for many industrial types or to be used by academia for a systematic empirical studies. This paper explores empirically only the software Development Projects of Renewable Energy Applications in the ISBSG Dataset v.13 based on software project factors such as effort and team work size to define the correlations between them. In this work, three data analysis techniques were applied: statistical analysis, data clustering, and data visualization. Both SPSS and Rapid Miner are used to conduct the statistical analysis and data visualization.
... 7 Benchmarking can be performed either internally with data collected within the organization, or externally with data collected outside the organization or available from multi-organizational datasets. 5,[8][9][10][11] For external benchmarking, the following criteria were used to select a benchmarking repository: ...
The Guide to the Software Engineering Body of Knowledge (the SWEBOK Guide) represents the consensus on the knowledge
that software engineers, and their organizations, should use whenever and wherever appropriate in software development.
This paper presents an innovative use of this SWEBOK Guide as a benchmarking reference for software organizations interested
in process improvement and looking for best practices. Process improvement approaches help organizations improve
their processes and their performance. Before implementing improvements to existing processes, it is necessary to benchmark
organization’s practices already in place against a reference, identifying process weaknesses and looking for best practices
that can contribute to process improvement according to corporate priorities. This paper presents two industry case studies
illustrating the use of the SWEBOK Guide for benchmarking purposes and process improvements. This paper presents
also quantitative results of productivity and quality analyses in both organizations and discusses the candidate linkages.
... The internal view of the ISBSG data repository corresponds closely to their data collection questionnaire, with some additional fields added by their repository manager [26]. The data repository of the ISBSG [13] is a publicly available multi-company data set which contains software project data collected from various organizations around the world from 1989 on. ...
... • Effort prediction: such as effort by phases, summary work effort, normalized work effort, etc. The ISBSG questionnaire contains six parts [26]: ...
... For the purpose of software benchmarking, ISBSG collects, analyzes and reports data relating to products developed and processes implemented within organizational units in order to [26]: ...
The International Software Benchmarking Standards Group (ISBSG) provides to researchers and practitioners a repository of software projects’ data that has been used to date mostly for benchmarking and project estimation purposes, but rarely for software defects analysis. Sigma, in statistics, measures how far a process deviates from its goal. Six Sigma focuses on reducing variations within processes, because such variations may lead to an inconsistency in achieving projects’ specifications which represent ‘defects’, which means not meeting customers’ satisfaction.
Six Sigma provides two methodologies to solve organizations’ problems: ‘De-fine-Measure-Analyze-Improve-Control’ process cycle (DMAIC) and Design of Six Sigma (DFSS). The DMAIC focuses on improving the existed processes, while the DFSS focuses on redesigning the existing processes and developing new ones. This paper presents an approach to provide an analysis of the ISBSG repository based on Six Sigma measurements. It investigates the use of the ISBSG data repository with some of the related Six Sigma measurement aspects, including Sigma defect meas-urement and software defect estimation. This study presents the dataset preparation consisting of two levels of data preparations, and then analyzes the quality-related data fields in the ISBSG MS-Excel data extract (Release 12 - 2013). It also presents an analysis of the extracted dataset of software projects. This study has found that the ISBSG MS-Excel data extract has a high ratio of missing data within the data fields of ‘Total Number of Defects’ variable, which represents a serious challenge when the ISBSG dataset is being used for software defect estimation.
... The selection criteria were: @BULLET Projects completed within the previous two years, and @BULLET Project documentation available for functional size measurement. For this study, all data were recorded using the data field definitions of data collection questionnaire of the International Software Benchmarking Standards Group [11] [12]. ...
Management interest is not limited to accurate
estimate of software projects, but also to being more
productive than your peers. This paper proposes an estimation
approach based on economics concepts, such as productivity
models with fixed/variable costs and economies/diseconomies
of scale. This paper also reports on an empirical study in a
Canadian organization that illustrates this approach.
Conceptual models are key artefacts in software production processes that are based on MDD technology. These conceptual models are used as inputs in the process of code generation. Therefore, it is very important to be able to evaluate the quality of the models in order to improve the quality of the corresponding final applications. The development of an effective quality assurance technique requires knowing what kind of defects may occur in practice in the conceptual models used in MDD approaches. Conventional Conceptual Modeling approaches focus on the detection of defects that comes from either the data perspective or the process perspective. However, the interaction perspective also matters! This paper presents a list of technical defects that can be identified when performing the interaction modeling of an MDD environment. This list of defects provides an initial approach to evaluate the completeness of Interaction Models with respect to their use for the automatic generation of a final application. This paper also presents an example that illustrates how the completeness of an Interaction Model can be evaluated through defect detection.
Accuracy gain in the software estimation is constantly being sought by researchers. On the same time new techniques and methodologies are being employed for getting capability of intelligence and prediction in estimation models. Today the target of estimation research is not only the achievement of accuracy but also fusion of different technologies and introduction of new factors. In this paper we advise improvement in some existing work by introducing mechanism of gaining accuracy. The paper focuses on method for tuning the fuzziness function and fuzziness value. This document proposes a research for development of intelligent Bayesian Network which can be used independently to calculate the estimated effort for software development, uncertainty, fuzziness and effort estimation. The comparison of relative error and magnitude relative error bias helps the selection of parameters of fuzzy function; however the process can be repeated n-times to get suitable accuracy. We also present an example of fuzzy set development for ISBSG data set in order to elaborate working of proposed system.
Software estimation is an active research area with researchers working on areas like accuracy, new model development and statistical analysis. Estimates are probabilistic values and can be represented with a degree of uncertainty. Prior distributions are one of the way to represent the historical and organizational data which can be used by researchers to conduct further estimations. In this paper we introduce the software estimation landscape and prior distributions of significant factors, determined from ISBSG data set. These priors can be used for development of estimation models e.g. Bayesian networks. The paper make contributions in number of ways, it provides a brief overview of quality of data set. It also provides statistics of vital factors from dataset. This paper also provides prior distributions of productivity for Architecture e.g. Standalone, Client server and mixture of architectures.
