Analyzing software change in open source projects using Artificial Immune System algorithms

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.