A Novel Developer Ranking Algorithm for Automatic Bug Triage Using Topic Model and Developer Relations

Zhang, Tao; Yang, Geunseok; Lee, Byungjeong; Lua, Eng Keong

doi:10.1109/apsec.2014.43

Cited by 32 publications

(16 citation statements)

References 16 publications

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“…In this survey paper, we classify each selected paper into a specific category for each task based on the different algorithms presented in the papers. For example, for the task of bug triage, some studies like [37,38] utilized machine-learning algorithms to recommend the appropriate bug fixers while [30,50,51,53] used topic model to complete the same task. On this occasion, we classify the former into the class 'Machine-learning-based recommender', and categorize the latter into the class 'Topic model-based recommender'.…”

Section: Analysis and Classification Methodsmentioning

confidence: 99%

“…By employing multiple techniques such as machine-learning algorithms and social network analysis, the previous studies [29,30,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] can achieve the purpose of automatic assignee recommendation. According to the different techniques, we classify these studies into five category such as machine learning-based recommender, expertise model-based recommender, tossing graph-based recommender, social network-based recommender and topic model-based recommender, which are detailed in the following subsections, respectively.…”

Section: Bug Triagementioning

confidence: 99%

“…The scholars expect to introduce topic model for improving the accuracy of automatic bug triage. Studies [30,50,51,53] adopted topic model to recommend the best developers for fixing the given bugs.…”

Section: Topic Model-based Recommendermentioning

confidence: 99%

“…Zhang et al [53] utilized LDA to extract the topics from historical bug reports. By capturing the developers' behavior on historical bug reports belonging to a same topic with the given bug report, they can verify whether the developer has interest in this topic.…”

Section: Topic Model-based Recommendermentioning

confidence: 99%

“…Except for reassignments, processing a multitude of bug reports places a heavy burden on triagers. To resolve this problem, several automatic bug triage approaches [29,30,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] have been proposed to recommend the best developers for fixing the given bugs. (3) Bug fixing: The assigned developers, usually called as assignees, are responsible for fixing the reported bugs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Literature Review of Research in Bug Resolution: Tasks, Challenges and Future Directions

Zhang

Jiang

Wang

et al. 2015

The Computer Journal

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Due to the increasing scale and complexity of software products, software maintenance especially on bug resolution has become a challenging task. Generally in large-scale software programs, developers depend on software artifacts (e.g., bug report, source code and change history) in bug repositories to complete the bug resolution task. However, a mountain of submitted bug reports every day increase the developers' workload. Therefore, 'How to effectively resolve software defects by utilizing software artifacts?' becomes a research hotspot in software maintenance. Considerable studies have been done on bug resolution by using multi-techniques, which cover data mining, machine learning and natural language processing. In this paper, we present a literature survey on tasks, challenges and future directions of bug resolution in software maintenance process. Our investigation concerns the most important phases in bug resolution, including bug understanding, bug triage and bug fixing. Moreover, we present the advantages and disadvantages of each study. Finally, based on the investigation and comparison results, we propose the future research directions of bug resolution.

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Section: Analysis and Classification Methodsmentioning

confidence: 99%

Section: Bug Triagementioning

confidence: 99%

Section: Topic Model-based Recommendermentioning

confidence: 99%

Section: Topic Model-based Recommendermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Literature Review of Research in Bug Resolution: Tasks, Challenges and Future Directions

Zhang

Jiang

Wang

et al. 2015

The Computer Journal

Self Cite

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IM_NRFixer: A hybrid approach to alleviate class‐imbalance problem for predicting the fixability of Non‐Reproducible bugs

Goyal

Sardana

2020

J Software Evolu Process

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Software maintenance is an important phase in the software development life cycle. Software projects maintain bug repositories to gather, organize, and keep track of bug reports. These bug reports are resolved by numerous software developers. Whenever the reported bug does not get resolved by the assigned developer, he marks the resolution of bug report as Non‐Reproducible (NR). When NR bugs are reconsidered, few of them get resolved, and their resolution changes from NR to fix (NRF). The main aim of this paper is to predict these fixable NRF bug reports. A major challenge in predicting NRF bugs from NR bugs is that only a small portion of NR bugs get fixed, i.e., class‐imbalance problem. For example, NRF bugs account for only 8.64%, 4.73 %, 4.56%, and 1.06% in NetBeans, Eclipse, Open Office, and Mozilla Firefox projects respectively. In this paper, we work on improving the classification performance on these imbalanced datasets. We propose IMNRFixer, a novel and hybrid NRF prediction tool. IMNRFixer uses three different techniques to combat class‐imbalance problem: undersampling, oversampling, and ensemble models. We evaluate the performance of IMNRFixer models on four large and open‐source projects of Bugzilla repository. Our results show that IMNRFixer outperforms conventional machine learning techniques. IMNRFixer achieves performance up to 71.7%, 93.1%, 91.7%, and 96.5% while predicting the minority class (NRF) for NetBeans, Eclipse, Open Office, and Mozilla Firefox projects, respectively.

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Toward more accurate developer recommendation via inference of development activities from interaction with bug repair process

Wang

et al. 2021

J Software Evolu Process

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Software projects usually receive a large number of submitted bug reports every day. Manually triaging the bug reports is often time‐consuming and error‐prone; thus, it is necessary to automatically assign the bug reports to the suitable developers for bug repair, with the help of bug tracking systems. Aiming to reducing the time consumption and mismatch of bug report assignments, we present a developer recommendation model for bug repair based on weighted recurrent neural network, namely, DTPM, which contains two parts: One obtains multisource semantic information of bug reports and fuses them into high‐dimensional semantic feature vectors, and the other combines a penalty matrix into a single hidden layer neural network to obtain more reasonable developer recommendations. We conduct experiments on five datasets of open bug repositories (NetBeans, OpenOffice, GCC, Mozilla, and Eclipse), and the experimental results show that DTPM can achieve better performance than state‐of‐the‐art models LDA_KL, LDA_KL, LDA_SVM, DERTOM, DREX, and DeepTriage.

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A Novel Developer Ranking Algorithm for Automatic Bug Triage Using Topic Model and Developer Relations

Cited by 32 publications

References 16 publications

A Literature Review of Research in Bug Resolution: Tasks, Challenges and Future Directions

A Literature Review of Research in Bug Resolution: Tasks, Challenges and Future Directions

IM_NRFixer: A hybrid approach to alleviate class‐imbalance problem for predicting the fixability of Non‐Reproducible bugs

Toward more accurate developer recommendation via inference of development activities from interaction with bug repair process

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A Novel Developer Ranking Algorithm for Automatic Bug Triage Using Topic Model and Developer Relations

Cited by 32 publications

References 16 publications

A Literature Review of Research in Bug Resolution: Tasks, Challenges and Future Directions

A Literature Review of Research in Bug Resolution: Tasks, Challenges and Future Directions

IMNRFixer: A hybrid approach to alleviate class‐imbalance problem for predicting the fixability of Non‐Reproducible bugs

Toward more accurate developer recommendation via inference of development activities from interaction with bug repair process

Contact Info

Product

Resources

About

IM_NRFixer: A hybrid approach to alleviate class‐imbalance problem for predicting the fixability of Non‐Reproducible bugs