Exploration of development projects of renewable energy applications in the ISBSG dataset: Empirical study

Meridji, Kenza; Al-Sarayreh, Khalid T.; Abu-Arqoub, Mohammad; Hadi, Wael

doi:10.1109/it-dreps.2017.8277808

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…Meridji et al [46] selected three clustering attributes, namely, Organization Type, Application Type, and Development Type, to test hypotheses about the relationship between team size and work effort and between productivity and project function size. They showed that the correlations between these two pairs of factors are proportional.…”

Section: Related Workmentioning

confidence: 99%

“…Clustering Method References 1 Development Type (DT) [45], [46], [48], [49], [52], [55], [56], [58] 2 Development Platform (DP) [45], [50], [52], [54], [55], [56], [58] 3 Language Type (LT) [50], [51], [52], [55], [56], [57], [58] 4 Industry Sector (IS) [25], [36], [47], [48], [52], [53], [56] 5 Organization Type (OT) [45], [46], [54], [58] 6 Relative Size (RS) [47], [53], [56], [57] 7 Application Type (AT) [45], [46], [48] [56]…”

Section: Nomentioning

confidence: 99%

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Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

et al. 2022

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Introduction:The precise estimation of software effort is a significant difficulty that project managers encounter during software development. Inaccurate forecasting leads to either overestimating or underestimating software effort, which can be detrimental for stakeholders. The International Function Point Users Group's Function Point Analysis (FPA) method is one of the most critical methods for software effort estimation. However, the practice of using the FPA method in the same fashion across all software areas needs to be reexamined. Aim: We propose a model for evaluating the influence of data clustering on software development effort estimation and then finding the best clustering method. We call this model the effort estimation using machine learning applied to the clusters (EEAC) model. Method: We cluster the dataset according to the clustering method and then apply the FPA and EEAC methods to these clusters for effort estimation. The clustering methods we use in this study include five categorical variable criteria (Development Platform, Industrial Sector, Language Type, Organization Type, and Relative Size) and the k-means clustering algorithm. Results: The experimental results show that the estimation accuracy obtaining with clustering consistently outperforms the accuracy without clustering for both the FPA and EEAC methods. Significantly, using the FPA method, the average improvement rate from using clustering as opposed to non-clustered was highest at 58.06%, according to the RMSE. With the EEAC method, this number reached 65.53%. The Industry Sector categorical variable achieves the best accuracy estimation compared to the other clustering criteria and k-means clustering. The improvement in accuracy in terms of the RMSE when applying this criterion is 63.68% for the FPA method and 72.02% for the EEAC method. Conclusion: Better results are obtained through dataset clustering compared to no clustering for both the FPA and EEAC methods. The Industry Sector is the most suitable clustering method among the tested clustering methods.

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Section: Related Workmentioning

confidence: 99%

Section: Nomentioning

confidence: 99%

Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

et al. 2022

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“…In addition, to what was mentioned earlier, work focused on data characterization of ISBSG projects using data analytics (Buglione and Abran, 2002;Abran et al, 2005Abran et al, , 2015 and using the same techniques as in this study. More specifically, many research works such as by Abran (2001, 2005), Meridji et al (2017), Fernandez-Diego and Gonzalez-Ladron- De-Guevara (2014) proposed several models in this domain to conduct a systematic and empirical research using ISBSG to improve the software product quality with the minimum time and cost and find the difference between performance and quality. multi-organizational data be used for effort estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Lastly, the potential of ISBSG to develop new research is also outlined. However, Meridji et al (2017) explore 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 teamwork size to define the correlations between them. In this research, three data analysis techniques were applied: statistical analysis, data clustering and datavisualization.…”

Section: Introductionmentioning

confidence: 99%

Performance Calculation and Benchmarking using the ISBSG Dataset Release 12 Data Repository: Empirical Study

Alkhatib¹,

Abuawwad²,

Almakadmeh³

et al. 2019

Asian J. Inf. Technol.

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The International Software Benchmarking Standards Group (ISBSG) maintains a software development repository with 6,006 software projects. The definition of productivity is a single ratio of output to input and then combined with various cost factors leading to a single value. Of these values we have dataset makes it possible to calculate the productivity of projects, effort, size and quality. By contrast, the concept of performance is more comprehensive than productivity. This study explores a comparison between performance and productivity and how it can affect projects by several other factors that affect its using ISBSG dataset V.12. In this research, tree data analysis techniques were applied: data clustering, neural network. SPSS was used to conduct statistical analysis and data visualization.

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Exploration of development projects of renewable energy applications in the ISBSG dataset: Empirical study

Cited by 2 publications

References 14 publications

Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

Performance Calculation and Benchmarking using the ISBSG Dataset Release 12 Data Repository: Empirical Study

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