Welcome to the InfoSci Platform
Cross-Project Change Prediction Using Meta-Heuristic Techniques

Cross-Project Change Prediction Using Meta-Heuristic Techniques

Ankita Bansal, Sourabh Jajoria
Copyright: © 2021 |Pages: 21
ISBN13: 9781799880486|ISBN10: 1799880486|EISBN13: 9781799880998
DOI: 10.4018/978-1-7998-8048-6.ch015
Cite Chapter Cite Chapter

MLA

Bansal, Ankita, and Sourabh Jajoria. "Cross-Project Change Prediction Using Meta-Heuristic Techniques." Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, IGI Global, 2021, pp. 279-299. https://doi.org/10.4018/978-1-7998-8048-6.ch015

APA

Bansal, A. & Jajoria, S. (2021). Cross-Project Change Prediction Using Meta-Heuristic Techniques. In I. Management Association (Ed.), Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms (pp. 279-299). IGI Global. https://doi.org/10.4018/978-1-7998-8048-6.ch015

Chicago

Bansal, Ankita, and Sourabh Jajoria. "Cross-Project Change Prediction Using Meta-Heuristic Techniques." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, 279-299. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8048-6.ch015

Export Reference

Mendeley
Favorite

Abstract

Changes in software systems are inevitable. Identification of change-prone modules can help developers to focus efforts and resources on them. In this article, the authors conduct various intra-project and cross-project change predictions. The authors use distributional characteristics of dataset to generate rules which can be used for successful change prediction. The authors analyze the effectiveness of meta-heuristic decision trees in generating rules for successful cross-project change prediction. The employed meta-heuristic algorithms are hybrid decision tree genetic algorithms and oblique decision trees with evolutionary learning. The authors compare the performance of these meta-heuristic algorithms with C4.5 decision tree model. The authors observe that the accuracy of C4.5 decision tree is 73.33%, whereas the accuracy of the hybrid decision tree genetic algorithm and oblique decision tree are 75.00% and 75.56%, respectively. These values indicate that distributional characteristics are helpful in identifying suitable training set for cross-project change prediction.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.