Bug Fix Time Optimization Using Matrix Factorization and Iterative Gale-Shaply Algorithms

TL;DR

This paper introduces a novel approach combining matrix factorization, Gale-Shapley algorithm, and differential evolution to optimize bug fix times and evenly distribute workload among developers, significantly improving automation in bug triage.

Contribution

It presents the first integration of matrix factorization with Gale-Shapley and differential evolution for bug triage optimization, addressing fix time and workload balance.

Findings

Reduces bug fixing time by up to 80.67%

Achieves more uniform developer workload

Effective across multiple software repositories

Abstract

Bug triage is an essential task in software maintenance phase. It assigns developers (fixers) to bug reports to fix them. This process is performed manually by a triager, who analyzes developers profiles and submitted bug reports to make suitable assignments. Bug triaging process is time consuming thus automating this process is essential to improve the quality of software. Previous work addressed triaging problem either as an information retrieval or classification problem. This paper tackles this problem as a resource allocation problem, that aims at the best assignments of developers to bug reports, that reduces the total fixing time of the newly submitted bug reports, in addition to the even distribution of bug reports over developers. In this paper, a combination of matrix factorization and Gale Shapely algorithm, supported by the differential evolution is firstly introduced to optimize the total fix time and normalize developers work load. Matrix factorization is used to establish a recommendation system for Gale-Shapley to make assignment decisions. Differential evolution provides the best set of weights to build developers score profiles. The proposed approach is assessed over three repositories, Linux, Apache and Eclipse. Experimental results show that the proposed approach reduces the bug fixing time, in comparison to the manual triage, by 80.67%, 23.61% and 60.22% over Linux, Eclipse and Apache respectively. Moreover, the workload for the developers is uniform.