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

Author:

Dahane Miloud1ORCID,Abdi Mustapha Kamel1ORCID,Bouneffa Mourad2,Ahmad Adeel3,Basson Henri2

Affiliation:

1. Université Oran1, Oran, Algeria

2. Université du Littoral Côte d'Opale, Dunkirk, France

3. Laboratoire d'Informatique Signal et Image de la Côte d'Opale, Calais, France

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.

Publisher

IGI Global

Reference36 articles.

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2. Abdi, M.K. & Dinedane. M.Z. (2015). Change Impact Identification in Object-Oriented System: Dependence Graph Approach. International Journal of Education and Management Engineering, 1-8

3. Abdi, M.K, Lounis, H. & Sahraoui, H. (2009). Analyse et prédiction de l’impact de changements dans un système à objets: Approche probabiliste. Revue des nouvelles technologies de l’information RNTI-L3, 113-138.

4. Ahmad, A., Basson, H., & Bouneffa, M. (2017). Analyzing and modeling the structural and qualitative interdependencies of software evolution. In Proceedings of 7th International Workshop on Computer Science and Engineering, (WCSE 2017), international Conference on Software Engineering (ICOSE), Beijing, China.

5. Using indirect coupling metrics to predict package maintainability and testability

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