IMNRFixer: A hybrid approach to alleviate class‐imbalance problem for predicting the fixability of Non‐Reproducible bugs. Issue 3 (30th June 2020)

• Record Type: Journal Article
• Authors: Goyal, Anjali; Sardana, Neetu
• Is Part Of: Journal of software. Volume 35:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-30
• Subjects: bug fixing -- bug report -- class‐imbalance -- classification -- ensemble techniques -- machine learning -- mining software repositories -- Non‐Reproducible bugs -- prediction tool -- sampling; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.2290
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Software maintenance is an important phase in the software development life cycle. Software projects maintain bug repositories to gather, organize, and keep track of bug reports. These bug reports are resolved by numerous software developers. Whenever the reported bug does not get resolved by the assigned developer, he marks the resolution of bug report as Non‐Reproducible (NR). When NR bugs are reconsidered, few of them get resolved, and their resolution changes from NR to fix (NRF). The main aim of this paper is to predict these fixable NRF bug reports. A major challenge in predicting NRF bugs from NR bugs is that only a small portion of NR bugs get fixed, i.e., class‐imbalance problem. For example, NRF bugs account for only 8.64%, 4.73 %, 4.56%, and 1.06% in NetBeans, Eclipse, Open Office, and Mozilla Firefox projects respectively. In this paper, we work on improving the classification performance on these imbalanced datasets. We propose IMNRFixer, a novel and hybrid NRF prediction tool. IMNRFixer uses three different techniques to combat class‐imbalance problem: undersampling, oversampling, and ensemble models. We evaluate the performance of IMNRFixer models on four large and open‐source projects of Bugzilla repository. Our results show that IMNRFixer outperforms conventional machine learning techniques. IMNRFixer achieves performance up to 71.7%, 93.1%, 91.7%, and 96.5% while predicting the minority class (NRF) for NetBeans, Eclipse, Open Office, and Mozilla Firefox projects, respectively.