Predictive software maintenance utilizing cross-project data

Yogita Khatri () and Sandeep Kumar Singh ()
Additional contact information
Yogita Khatri: Jaypee Institute of Information Technology
Sandeep Kumar Singh: Jaypee Institute of Information Technology

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 4, No 15, 1503-1518

Abstract: Abstract To improve the software quality and reduce the maintenance cost, cross-project fault prediction (CPFP) identifies faulty software components in a particular project (aka target project) using the historical fault data of other projects (aka source/reference projects). Although several diverse approaches/models have been proposed in the past, there exists room for improvement in the prediction performance. Further, they did not consider effort-based evaluation metrics (EBEMs), which are important to ensure the model’s application in the industry, undertaking a realistic constraint of having a limited inspection effort. Besides, they validated their respective approaches using a limited number of datasets. Addressing these issues, we propose an improved CPFP model with its validation on a large corpus of data containing 62 datasets in terms of EBEMs (PIM@20%, Cost-effectiveness@20%, and IFA) and other machine learning-based evaluation metrics (MLBEMs) like PF, G-measure, and MCC. The reference data and the target data are first normalized to reduce the distribution divergence between them and then the relevant training data is selected from the reference data using the KNN algorithm. Seeing the experimental and statistical test results, we claim the efficacy of our proposed model over state-of-the-art CPFP models namely the Turhan-Filter and Cruz model comprehensively. Thus, the proposed CPFP model provides an effective solution for predicting faulty software components, enabling practitioners in developing quality software with lesser maintenance cost.

Keywords: Cross-project fault prediction; Machine learning-based evaluation metrics; Effort-based evaluation metrics; Software quality; Software maintenance (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-023-01957-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:4:d:10.1007_s13198-023-01957-6

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-023-01957-6

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().