Effective Bug Triage Based on Historical Bug-Fix Information

Hu, Hao; Zhang, Hongyu; Xuan, Jifeng; Sun, Weigang

doi:10.1109/issre.2014.17

Cited by 85 publications

(49 citation statements)

References 31 publications

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“…To make our results comparable, we choose a metric called Recall@K used in many prior studies [5] [19]. Recall@K is the proportion of bugs whose associated developers is ranked in the top k (k = 1, 3, 5) of the returned results.…”

Section: A Datasetmentioning

confidence: 99%

“…If the number of classes is small, these methods are effective enough. However, when the number of classes increases to hundreds or even thousands, it is hard to design an accurate and efficient classifier [5].…”

Section: A Advantagesmentioning

confidence: 99%

“…Besides, some developers may have fixed many bugs relevant to a bug. However, at the time of bug triage, they are not free for some reasons, so the bug is assigned to another less relevant developer [5].…”

Section: B Limitationsmentioning

confidence: 99%

“…Recall@K is a proper metric for this purpose. It also has been adopted by many prior bug triage studies [5] [19].…”

Section: Threats To Validitymentioning

confidence: 99%

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Improving Bug Triage with Relevant Search

Peng¹,

Zhou²,

Jin³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Bug triage is a process where bugs are assigned to developers. In large open source projects such as Mozilla and Eclipse, bug triage is time-consuming because numerous bugs are submitted everyday. To improve bug triage, many studies have proposed automatic approaches to recommend proper developers for resolving bugs. These approaches are based on machine learning algorithms, which treat bug triage like text classification. Although they are effective, the accuracy of them can be further improved. Our goal is to propose a method not only has good performance but also is simple. We propose a method based on relevant search technique to recommend developers for the given bugs. First, we construct an index for bugs to make them searchable. Then, for a given bug to be assigned, we utilize the index to search for the bugs related to it. Finally, we analyze these related bugs and recommend developers based on them. We conduct experiments on bugs of Mozilla and Eclipse to evaluate our method. The results indicate that our method has a good performance and outperforms machine learning algorithms like Naïve Bayes and SVM.

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Section: A Datasetmentioning

confidence: 99%

Section: A Advantagesmentioning

confidence: 99%

Section: B Limitationsmentioning

confidence: 99%

“…Recall@K is a proper metric for this purpose. It also has been adopted by many prior bug triage studies [5] [19].…”

Section: Threats To Validitymentioning

confidence: 99%

See 2 more Smart Citations

Improving Bug Triage with Relevant Search

Peng¹,

Zhou²,

Jin³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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show abstract

“…Hao et al [22] proposed a defect report triage system called "BugFixer". They used a new tokenization algorithm and Vector Space Model to compute similarity between two defect reports.…”

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

A Survey on Automatic Defect Report Triage

Sayali¹,

S.²

2017

IJCA

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An empirical investigation of single‐objective and multiobjective evolutionary algorithms for developer's assignment to bugs

Karim

Ruhe

Rahman³

et al. 2016

J Software Evolu Process

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In this paper, the modeling of developers' assignment to bugs (DAB) is studied. The problem is modeled both as a single objective (minimize bug fix time) and as a bi-objective (minimize bug fix time and cost) combinatorial optimization problem. Two models of developer assignment are considered where in the first model a single developer is assigned per bug (single developer model), while in the second model a single developer is assigned for each competency area of a bug (individual competency model). The latter model is proposed in this paper. For the single developer model, GA@DAB, an existing genetic algorithm-based approach, is extended to support precedence among bugs. For the individual competency model of DAB, one genetic algorithm-based approach (Competence@DAB) and one nondominated sorting genetic algorithm II-based approach (CompetenceMulti 2 @DAB ) are proposed to generate solutions minimizing time and minimizing both time and cost, respectively. The performance of the proposed approaches was evaluated for 2040 bugs of 19 open-source milestone projects from the ECLIPSE platform. Our results and analysis show that the proposed individual competency model is far better than the single developer model, with average bug fix time reduction of 39.7% across all projects.

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Effective Bug Triage Based on Historical Bug-Fix Information

Cited by 85 publications

References 31 publications

Improving Bug Triage with Relevant Search

Improving Bug Triage with Relevant Search

A Survey on Automatic Defect Report Triage

An empirical investigation of single‐objective and multiobjective evolutionary algorithms for developer's assignment to bugs

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