A multi-objective effort-aware defect prediction approach based on NSGA-II

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

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Author(s)

  • Xiao Yu
  • Liming Liu
  • Lin Zhu
  • Zijian Wang
  • Fuyang Li

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number110941
Journal / PublicationApplied Soft Computing
Volume149
Issue numberPart A
Online published17 Oct 2023
Publication statusPublished - Dec 2023

Abstract

Effort-Aware Defect Prediction (EADP) technique sorts software modules by the defect density and aims to find more bugs when testing a certain number of Lines of Code (LOC). The existing EADP methods ignore the number of required inspected modules and thus resulting in more testing cost. Therefore, we propose a multi-objective effort-aware defect prediction approach based on NSGA-II named MOOAC for EADP, which aims to maximize the Proportion of the found Bugs (PofB@20%) and minimize the Proportion of Module Inspected (PMI@20%) when inspecting the top 20% LOC. MOOAC firstly trains a random forest classification model. Then, it builds a logistic regression model, and utilizes the NSGA-II algorithm to generate the coefficient vector of the model by maximizing the PofB@20% value and minimizing the PMI@20% value simultaneously. In the model prediction phase, MOOAC firstly employs the built random forest classifier to decide whether modules are defective. Next, the predicted defective modules are first inspected based on the ratio between the predicted defect probability by the logistic regression model and LOC, which can make testers to find more bugs and test as fewer LOC as possible. The clean modules are then inspected to reduce the Initial False Alarms (IFA), if there is still the testing budget left. The results show that MOOAC exhibits the best overall performance on the PofB@20% and PMI@20%. In other words, MOOAC enables testers to identify more bugs per 1% module. © 2023 Elsevier B.V.

Research Area(s)

  • Defect density, Effort-aware, Logistic regression, Multi-objective optimization, Software defect prediction

Citation Format(s)

A multi-objective effort-aware defect prediction approach based on NSGA-II. / Yu, Xiao; Liu, Liming; Zhu, Lin et al.
In: Applied Soft Computing, Vol. 149, No. Part A, 110941, 12.2023.

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