Improving effort-aware defect prediction by directly learning to rank software modules

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

5 Scopus Citations
View graph of relations

Author(s)

  • Xiao Yu
  • Jiqing Rao
  • Lei Liu
  • Guancheng Lin
  • Wenhua Hu
  • Junwei Zhou
  • Jianwen Xiang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number107250
Journal / PublicationInformation and Software Technology
Volume165
Online published13 May 2023
Publication statusPublished - Jan 2024

Abstract

Context: Effort-Aware Defect Prediction (EADP) ranks software modules according to the defect density of software modules, which allows testers to find more bugs while reviewing a certain amount of Lines Of Code (LOC). Most existing methods regard the EADP task as a regression or classification problem. Optimizing the regression loss or classification accuracy might result in poor effort-aware performance. Objective: Therefore, we propose a method called EALTR to improve the EADP performance by directly maximizing the Proportion of the found Bugs (PofB@20%) value when inspecting the top 20% LOC. Method: EALTR uses the linear regression model to build the EADP model, and then employs the composite differential evolution algorithm to generate a set of coefficient vectors for the linear regression model. Finally, EALTR selects the coefficient vector that achieves the highest PofB@20% value on the training dataset to construct the EADP model. To further reduce the Initial False Alarms (IFA) value of EALTR, we propose a re-ranking strategy in the prediction phase. Results: Our experimental results on eleven project datasets with 41 releases show that EALTR can find 5.83%–54.47% more bugs than the baseline methods whose IFA values are less than 10 and the re-ranking strategy significantly reduces the IFA value by 16.95%. Conclusion: Our study verifies the effectiveness of directly optimizing the effort-aware metric (i.e., PofB@20%) to build the EADP model. EALTR is recommended as an effective EADP method, since it can help software testers find more bugs. © 2023 Elsevier B.V.

Research Area(s)

  • Effort aware, Genetic algorithm, Software Defect Prediction

Citation Format(s)

Improving effort-aware defect prediction by directly learning to rank software modules. / Yu, Xiao; Rao, Jiqing; Liu, Lei et al.
In: Information and Software Technology, Vol. 165, 107250, 01.2024.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review