A time-based approach to automatic bug report assignment

Shokripour, Ramin; Anvik, John; Kasirun, Zarinah Mohd; Zamani, Sima

doi:10.1016/j.jss.2014.12.049

Cited by 65 publications

(36 citation statements)

References 11 publications

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“…Shokripour et al find that training a prediction model by using the whole history of a software system may cause a loss of performance [34]. Here, we would like to investigate whether it is the same case for bug triaging.…”

Section: Effect Of Training Using the Last Framementioning

confidence: 98%

Improving Automated Bug Triaging with Specialized Topic Model

Xia

Ding

et al. 2017

IIEEE Trans. Software Eng.

154

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Bug triaging refers to the process of assigning a bug to the most appropriate developer to fix. It becomes more and more difficult and complicated as the size of software and the number of developers increase. In this paper, we propose a new framework for bug triaging, which maps the words in the bug reports (i.e., the term space) to their corresponding topics (i.e., the topic space). We propose a specialized topic modeling algorithm named multi-feature topic model (MTM) which extends Latent Dirichlet Allocation (LDA) for bug triaging. MTM considers product and component information of bug reports to map the term space to the topic space. Finally, we propose an incremental learning method named TopicMiner which considers the topic distribution of a new bug report to assign an appropriate fixer based on the affinity of the fixer to the topics. We pair TopicMiner with MTM (TopicMiner M T M ). We have evaluated our solution on 5 large bug report datasets including GCC, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 227,278 bug reports. We show that TopicMiner M T M can achieve top-1 and top-5 prediction accuracies of 0.4831 -0.6868, and 0.7686 -0.9084, respectively. We also compare TopicMiner M T M with Bugzie, LDA-KL, SVM-LDA, LDA-Activity, and Yang et al.'s approach. The results show that TopicMiner M T M on average improves top-1 and top-5 prediction accuracies of Bugzie by 128.48% and 53.22%, LDA-KL by 262.91% and 105.97%, SVM-LDA by 205.89% and 110.48%, LDA-Activity by 377.60% and 176.32%, and Yang et al.'s approach by 59.88% and 13.70%, respectively.

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Section: Effect Of Training Using the Last Framementioning

confidence: 98%

Improving Automated Bug Triaging with Specialized Topic Model

Xia

Ding

et al. 2017

IIEEE Trans. Software Eng.

154

View full text Add to dashboard Cite

show abstract

“…Extra information in the metadata can help obtain higher accuracies in some cases, but still, text-based methods are the most effective techniques used 44,78 and those metadata-based approaches are sometimes difficult to setup; for example, some need to know who maintained different pieces of code in the IDE 56 or who interacted online with whom in different setups. 16 As a result, the textual elements remain the most prevalent information used for BA.…”

Section: Knowledge Assumptions Of Bug-assignment Methodsmentioning

confidence: 99%

“…The most prevalent formulation of BA is as follows: ''given a new bug report, identify a ranked list of developers, whose expertise (based on their record of contributions to the project) qualifies them to fix the bug'' 2,[42][43][44][45] ; this is the formulation most researchers adopted and used in their studies. Also, in this paper, we investigate our research questions considering this formulation.…”

Section: Bug-assignment Tasks and Objectivesmentioning

confidence: 99%

“…It is used to emphasize specific keywords and de-emphasize the common terms, and shown to work well on textual data. 16,17,41,44,45,99 Tf-idf gives an ''overlap score'' 79 to a document regarding the query. Having several documents (developer profiles), the document with the highest score is considered the document most similar (and most relevant as a response) to the given query (bug report).…”

Section: Experiments Setup and Data Setmentioning

confidence: 99%

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Guidelines for evaluating bug‐assignment research

Sajedi‐Badashian

Stroulia

2020

J Software Evolu Process

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Bug assignment is the task of ranking candidate developers in terms of their potential competence to fix a bug report. Numerous methods have been developed to address this task, relying on different methodological assumptions and demonstrating their effectiveness with a variety of empirical studies with numerous data sets and evaluation criteria. Despite the importance of the subject and the attention it has received from researchers, there is still no unanimity on how to validate and comparatively evaluate bug‐assignment methods and, often times, methods reported in the literature are not reproducible. In this paper, we first report on our systematic review of the broad bug‐assignment research field. Next, we focus on a few key empirical studies and review their choices with respect to three important experimental‐design parameters, namely, the evaluation metric(s) they report, their definition of who the real assignee is, and the community of developers they consider as candidate assignees. The substantial variability on these criteria led us to formulate a systematic experiment to explore the impact of these choices. We conducted our experiment on a comprehensive data set of bugs we collected from 13 long‐term open‐source projects, using a simple Tf‐IDf similarity metric. On the basis of our arguments and/or experiments, we provide useful guidelines for performing further bug‐assignment research. We conclude that mean average precision (MAP) is the most informative evaluation metric, the developer community should be defined as “all the project members,” and the real assignee should be defined as “any developer who worked toward fixing a bug.”

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“…Literature [10] suggested a similarity measurement, which is based on TF-IDF method, and analyzes similarity between important terms in text documents. Literature [11] improved term weighting algorithm named Document Triage-Term Frequency-Inverse Document Frequency (DT-TF-IDF) was proposed by introducing document scores and users annotation to TF-IDF and giving a greater weight to annotated term. In [9, 10, and 11] will greatly increase the algorithm time complexity, some of them also need analysis tools with the help of third party tools.…”

Section: A Current Status Of the Researchmentioning

confidence: 99%

Optimized TF-IDF Algorithm with the Adaptive Weight of Position of Word

Chen¹,

Liang²

2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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The classical TF-IDF algorithm only considers the weight of the term frequency and the inverse document frequency, without considering the weights of other feature of word. After the author analyzing summary of Chinese expression habits, an adaptive weight of position of word algorithm based on TF-IDF is proposed in this paper, which can be called TF-IDF-AP algorithm. The TF-IDF-AP algorithm can dynamically determine the weight of position of word according to the position of word. This paper introduced the vector space model (VSM) and designed comparative experiment under the scene of Chinese document clustering. The results show that the F-measure of TF-IDF-AP algorithm has been improved by 12.9% comparing with the classical TF-IDF algorithm.

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A time-based approach to automatic bug report assignment

Cited by 65 publications

References 11 publications

Improving Automated Bug Triaging with Specialized Topic Model

Improving Automated Bug Triaging with Specialized Topic Model

Guidelines for evaluating bug‐assignment research

Optimized TF-IDF Algorithm with the Adaptive Weight of Position of Word

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