Empirical analysis of search based algorithms to identify change prone classes of open source software. (January 2017)

• Record Type: Journal Article
• Authors: Bansal, Ankita
• Is Part Of: Computer languages, systems & structures. Volume 47:Part 2(2017)
• Page Start: 211
• Page End: 231
• Publication Date: 2017-01
• Subjects: Change proneness -- Metrics -- Object oriented paradigm -- Search based algorithms -- Software quality -- Empirical validation; Programming languages (Electronic computers) -- Periodicals; Computer networks -- Periodicals; Computer architecture -- Periodicals; Computer systems -- Periodicals; Langage de programmation; Réseau d'ordinateurs; Architecture d'ordinateur; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 005.13
• DOI: 10.1016/j.cl.2016.10.001
• Languages: English
• ISSNs: 1477-8424

Abstract

Abstract: There are numerous reasons leading to change in software such as changing requirements, changing technology, increasing customer demands, fixing of defects etc. Thus, identifying and analyzing the change-prone classes of the software during software evolution is gaining wide importance in the field of software engineering. This would help software developers to judiciously allocate the resources used for testing and maintenance. Software metrics can be used for constructing various classification models which can be used for timely identification of change prone classes. Search based algorithms which form a subset of machine learning algorithms can be utilized for constructing prediction models to identify change prone classes of software. Search based algorithms use a fitness function to find the best optimal solution among all the possible solutions. In this work, we analyze the effectiveness of hybridized search based algorithms for change prediction. In other words, the aim of this work is to find whether search based algorithms are capable for accurate model construction to predict change prone classes. We have also constructed models using machine learning techniques and compared the performance of these models with the models constructed using Search Based Algorithms. The validation is carried out on two open source Apache projects, Rave and Commons Math. The results prove the effectiveness of hybridized search based algorithms in predicting change prone classes of software. Thus, they can be utilized by the software developers to produce an efficient and better developed software. Highlights: Used hybridized search based algorithms to identify change prone classes. For empirical validation, two open source projects (Apache Rave, Commons Math) used. Assessed performance of search based algorithms using g -mean and accuracy. Machine learning models constructed and performance compared with hybridised models. Results showed that hybridised models outperformed machine learning models.