Paper Info
Title
Analyzing software change in open source projects using Artificial Immune System algorithms
Abstract
Development of software change prediction models, based on the change histories of a software, are valuable for early identification of change prone classes. Classification of these change prone classes is vital to yield competent use of limited resources in an organization. This paper validates Artificial Immune System (AIS) algorithms for development of change prediction models using six open source data sets. It also compares the performance of AIS algorithms with other machine learning and statistical algorithms. The results of the study indicate, that the models developed, are effective means of predicting change prone classes in the future versions of the software. However, AIS algorithms do not perform better that machine learning and other statistical algorithms. The study provides conclusive results about the capabilities of AIS algorithms and reports whether there are any significant differences in the performance of different algorithms using a statistical test.
Year
DOI
Venue
2014
10.1109/ICACCI.2014.6968363
Advances in Computing, Communications and Informatics
Keywords
DocType
Citations 
artificial immune systems,learning (artificial intelligence),public domain software,software maintenance,statistical testing,AIS algorithm,artificial immune system,change prone classes identification,machine learning,open source projects,software change analysis,software change history,software change prediction models,statistical algorithm,statistical test,Artificial Immune System algorithms,Change proneness,Object- Oriented metrics,Open source projects,Software Quality
Conference
0
PageRank 
References 
Authors
0.34
13
2
Name
Order
Citations
PageRank
Ruchika Malhotra153335.12
Megha Khanna2596.47