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Support vector regression for enhancement effort prediction of Scrum projects from COSMIC functional size

  • S.I. : Software and Systems Reuse
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Abstract

The frequent changes in software projects may influence the accuracy of the software effort prediction and hinder the success of managing Agile projects. A survey on agile software prediction revealed that the most common cost driver among effort prediction models is the software size. Therefore, it is crucial to keep managers informed with the information regarding the functional size of a change so that an accurate enhancement effort can be made. Previous research works proved the effectiveness of the COSMIC method when compared to other FSM methods for sizing functional changes and its impact on the estimated effort with the use of ML techniques in the context of traditional software enhancement projects. However, the method has not been sufficiently verified within the context of Scrum projects to confirm its usefulness. This paper has a twofold objective: (1) investigate the correlation between the input feature describing the COSMIC sizing of an enhancement and its required effort within the Scrum context and (2) compare the impact of an enhancement functional size generated from COSMIC method with that of the Story Points on the enhancement effort prediction. In this paper, two types of empirical analysis were conducted. One with the use of the COSMIC FSM method, and the other without any consideration of the enhancement size. A dataset obtained from an industrial Scrum software project is used for training and testing the prediction model. With the determination of what dataset to be used, the next step is to identify the features that best contribute to the enhancement effort prediction. Using the correlation-based feature selection algorithm, the selected features are used for training and testing the SVR prediction enhancement effort models. Given these models, comparisons can be made with actual and estimated effort. Results show that the use of the COSMIC functional change size as input to the SVR prediction model for predicting Scrum enhancement effort provides significantly better results. In conclusion, the use of the COSMIC method may improve the enhancement effort prediction within the context of the Scrum software project.

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Availability of data and materials

ISBSG dataset, available in https://www.isbsg.org/project-data/.

Notes

  1. https://fr.scribd.com/

  2. http://www.its-all-design.com/

  3. https://www.investopedia.com/terms/p/positive-correlation.asp

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  1. Asma Sellami and Nadia Bouassida contributed equally to this work.

Authors and Affiliations

  1. Faculty of Economics and Management of Sfax, University of Sfax, Sfax, Tunisia

    Zaineb Sakhrawi

  2. Higher Institute of Computer Science and Multimedia, University of Sfax, Sfax, Tunisia

    Asma Sellami & Nadia Bouassida

Authors
  1. Zaineb Sakhrawi
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  2. Asma Sellami
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  3. Nadia Bouassida
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All manuscript’s authors conceived and designed the analysis, performed the analysis, and wrote the paper; the first author collected the data and contributed to data and analysis tools.

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Correspondence to Zaineb Sakhrawi.

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Sakhrawi, Z., Sellami, A. & Bouassida, N. Support vector regression for enhancement effort prediction of Scrum projects from COSMIC functional size. Innovations Syst Softw Eng 18, 137–153 (2022). https://doi.org/10.1007/s11334-021-00420-8

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