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Open AccessArticle

A Multi-Factor Approach for Selection of Developers to Fix Bugs in a Program

1
The College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
2
The School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
3
Collaborative Innovation Center for Transport Studies of Dalian Maritime University, Dalian 116026, China
4
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
5
Sichuan University-Pittsburgh Institute, Chengdu 610207, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3327; https://doi.org/10.3390/app9163327
Received: 26 June 2019 / Revised: 6 August 2019 / Accepted: 9 August 2019 / Published: 13 August 2019
(This article belongs to the Special Issue Applied Sciences Based on and Related to Computer and Control)
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Abstract

In a software tracking system, the bug assignment problem refers to the activities that developers perform during software maintenance to fix bugs. As many bugs are submitted on a daily basis, the number of developers required is quite large, and it therefore becomes difficult to assign the right developers to resolve issues with specific bugs. Inappropriate dispatches results in delayed processing of bug reports. In this paper, we propose an algorithm called ABC-DR to solve the bug assignment problem. The ABC-DR algorithm is a two-part composite approach that includes analysis between bug reports (i.e., B-based analysis) and analysis between developers and bug reports (i.e., D-based analysis). For analysis between bug reports, we use the multi-label k-nearest neighbor (ML-KNN) algorithm to find similar bug reports when compared with the new bug reports, and the developers who analyze similar bug reports recommend developers for the new bug report. For analysis between developers and bug reports, we find developer rankings similar to the new bug report by calculating the relevance scores between developers and similar bug reports. We use the artificial bee colony (ABC) algorithm to calculate the weight of each part. We evaluated the proposed algorithms on three datasets—GCC, Mozilla, and NetBeans—comparing ABC-DR with DevRec, DREX, and Bugzie. The experimental results show that the proposed ABC-DR algorithm achieves the highest improvement of 51.2% and 53.56% over DevRec for [email protected] and [email protected] in the NetBeans dataset. View Full-Text
Keywords: developer recommendation; composite method; ML-KNN algorithm; relevance score developer recommendation; composite method; ML-KNN algorithm; relevance score
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Guo, S.; Chen, S.; Wang, S.; Zhang, D.; Liu, Y.; Guo, C.; Li, H.; Li, T. A Multi-Factor Approach for Selection of Developers to Fix Bugs in a Program. Appl. Sci. 2019, 9, 3327.

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