Paper Info
Title
Software Change Prediction: A Systematic Review and Future Guidelines
Abstract
Background: The importance of Software Change Prediction (SCP) has been emphasized by several studies. Numerous prediction models in literature claim to effectively predict change-prone classes in software products. These models help software managers in optimizing resource usage and in developing good quality, easily maintainable products. Aim: There is an urgent need to compare and assess these numerous SCP models in order to evaluate their effectiveness. Moreover, one also needs to assess the advancements and pitfalls in the domain of SCP to guide researchers and practitioners. Method: In order to fulfill the above stated aims, we conduct an extensive literature review of 38 primary SCP studies from January 2000 to June 2019. Results: The review analyzes the different set of predictors, experimental settings, data analysis techniques, statistical tests and the threats involved in the studies, which develop SCP models. Conclusion: Besides, the review also provides future guidelines to researchers in the SCP domain, some of which include exploring methods for dealing with imbalanced training data, evaluation of search-based algorithms and ensemble of algorithms for SCP amongst others.
Year
DOI
Venue
2019
10.5277/e-Inf190107
E-INFORMATICA-SOFTWARE ENGINEERING JOURNAL
Keywords
DocType
Volume
change-proneness,machine learning,software quality,systematic review
Journal
13
Issue
ISSN
Citations 
1
1897-7979
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Ruchika Malhotra153335.12
Megha Khanna2596.47