IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

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
View Sample PDF
Author(s): Miloud Dahane (Université Oran1, Oran, Algeria), Mustapha Kamel Abdi (Université Oran1, Oran, Algeria), Mourad Bouneffa (Université du Littoral Côte d'Opale, Dunkirk, France), Adeel Ahmad (Laboratoire d'Informatique Signal et Image de la Côte d'Opale, Calais, France)and Henri Basson (Université du Littoral Côte d'Opale, Dunkirk, France)
Copyright: 2019
Volume: 10
Issue: 1
Pages: 18
Source title: International Journal of Open Source Software and Processes (IJOSSP)
Editor(s)-in-Chief: Marta Catillo (Università degli Studi del Sannio, Italy)
DOI: 10.4018/IJOSSP.2019010102

Purchase

View Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation on the publisher's website for pricing and purchasing information.

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.

Related Content

Roland Robert Schreiber. © 2023. 20 pages.
Sushil Kumar, SK Muttoo, V. B. Singh. © 2022. 16 pages.
Satya Sobhan Panigrahi, Ajay Kumar Jena. © 2022. 20 pages.
Ekbal Rashid, Mohan Prakash. © 2022. 16 pages.
Ritu Garg, Rakesh Kumar Singh. © 2022. 18 pages.
Neelamadhab Padhy, Sanskruti Panda, Jigyashu Suraj. © 2022. 20 pages.
Anil Kumar Patidar, Ugrasen Suman. © 2022. 17 pages.
Body Bottom