Paper Info
Title
Prediction of change prone classes using evolution-based and object-oriented metrics.
Abstract
Determination of change prone classes is crucial in providing guidance to software practitioners for efficient allocation of limited resources and to develop favorable quality software products with optimum costs. Previous literature studies have proposed successful use of design metrics to predict classes which are more prone to change in an Object-Oriented (OO) software. However, the use of evolution-based metrics suite, which quantifies history of changes in a software, release by release should also be evaluated for effective prediction of change prone classes. Evolution-based metrics are representative of evolution characteristics of a class over all its previous releases and are important in order to understand progression and change-prone nature of a class. This study evaluates the use of evolution-based metrics when used in conjunction with OO metrics for prediction of classes which are change prone in nature. In order to empirically validate the results, the study uses two application packages of the Android software namely Contacts and Gallery2. The results indicate that evolution based metrics when used in conjunction with OO metrics are the best predictors of change prone classes. Furthermore, the study statistically evaluates the superiority of this combined metric suite for change proneness prediction.
Year
DOI
Venue
2018
10.3233/JIFS-169468
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Change proneness,evolution-based metrics,empirical validation,machine learning techniques
Object-oriented programming,Artificial intelligence,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
34
3
1064-1246
Citations 
PageRank 
References 
1
0.35
19
Authors
2
Name
Order
Citations
PageRank
Ruchika Malhotra153335.12
Megha Khanna2596.47