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Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation

Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation

Miloud Dahane, Mustapha Kamel Abdi, Mourad Bouneffa, Adeel Ahmad, Henri Basson
Copyright: © 2019 |Volume: 10 |Issue: 1 |Pages: 18
ISSN: 1942-3926|EISSN: 1942-3934|EISBN13: 9781522565529|DOI: 10.4018/IJOSSP.2019010102
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MLA

Dahane, Miloud, et al. "Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation." IJOSSP vol.10, no.1 2019: pp.16-33. http://doi.org/10.4018/IJOSSP.2019010102

APA

Dahane, M., Abdi, M. K., Bouneffa, M., Ahmad, A., & Basson, H. (2019). Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation. International Journal of Open Source Software and Processes (IJOSSP), 10(1), 16-33. http://doi.org/10.4018/IJOSSP.2019010102

Chicago

Dahane, Miloud, et al. "Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation," International Journal of Open Source Software and Processes (IJOSSP) 10, no.1: 16-33. http://doi.org/10.4018/IJOSSP.2019010102

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Abstract

Software evolution control mostly relies on the better structure of the inherent software artifacts and the evaluation of different qualitative factors like maintainability. The attributes of changeability are commonly used to measure the capability of the software to change with minimal side effects. This article describes the use of the design of experiments method to evaluate the influence of variations of software metrics on the change impact in developed software. The coupling metrics are considered to analyze their degree of contribution to cause a change impact. The data from participant software metrics are expressed in the form of mathematical models. These models are then validated on different versions of software to estimate the correlation of coupling metrics with the change impact. The proposed approach is evaluated with the help of a set of experiences which are conducted using statistical analysis tools. It may serve as a measurement tool to qualify the significant indicators that can be included in a Software Maintenance dashboard.

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