Automatic Software Bug Triage System (BTS) Based on Latent Semantic Indexing and Support Vector Machine

Ahsan, Syed Nadeem; Ferzund, Javed; Wotawa, Franz

doi:10.1109/icsea.2009.92

Cited by 74 publications

(50 citation statements)

References 7 publications

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“…By training a classier, incoming bug reports can automatically be assigned to developers. A wide variety of classiers have been suggested, and previous studies report promising prediction accuracies ranging from 40% to 60% (Anvik et al, 2006;Ahsan et al, 2009;Jeong et al, 2009;Lin et al, 2009). Previous work has focused on Open Source Software (OSS) development projects, especially the Eclipse and Mozilla projects.…”

Section: Introductionmentioning

confidence: 99%

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

et al. 2015

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Context: Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects.Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. Objective: The goal of this study is to evaluate automated bug assignment techniques that are based on machine learning classication. In particular, we study the state-of-the-art ensemble learner Stacked Generalization (SG) that combines several classiers. Method: We collect more than 50,000 bug reports from ve development projects from two companies in dierent domains. We implement automated bug assignment and evaluate the performance in a set of controlled experiments. Results: We show that SG scales to large scale industrial application and that it outperforms the use of individual classiers for bug assignment, reaching prediction accuracies from 50% to 90% when large training sets are used. In addition, we show how old training data can decrease the prediction accuracy of bug assignment. Conclusions: We advice industry to use SG for bug assignment in proprietary contexts, using at least 2,000 bug reports for training. Finally, we highlight the importance of not solely relying on results from cross-validation when evaluating automated bug assignment.

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Section: Introductionmentioning

confidence: 99%

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

et al. 2015

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“…By using the text categorization, they can decide which developer is responsible for a certain bug. Ahsan et al [1] uses other features such as the titles and author of the bug reports in the classifier. These factors help the prediction become more accurate.…”

Section: Background and Related Workmentioning

confidence: 99%

Exploring the Influence of Time Factor in Bug Report Prioritization

Xu¹,

Zhang

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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Time factor has been widely applied into a wide range of data mining areas, such as social network and information retrieval. The main idea of taking time factor into consideration is that human activities may have some relations to time pattern. However, little attention has been pulled on the study of time factor in the area of software engineering. In this paper, we endeavour to explore to what extent time factor affects the prioritization of bug reports, a specified while important task in software engineering. Specifically, we test four time factors that may have some influence on this task, which are time of day, normal time, day of week, and days to major version. After the validation of relatedness of all these factors, we conduct an extensive set of experiments on two datasets to verify the effectiveness of these factors. The experimental results demonstrate that we can effectively improve the results by metrics of both Precision and Recall, on two classical models, i.e., the SVM model and Naive Bayes model.

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“…The experiment results show SVM performed better than others on their datasets. Ahsan et al [15] use feature selection and Latent Sematic Indexing [16] to reduce the dimensionality of the term-to-document matrix. Their results showed the bug triage system combined LSI and SVM has the best performance.…”

Section: Threats To Validitymentioning

confidence: 99%

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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Automatic Software Bug Triage System (BTS) Based on Latent Semantic Indexing and Support Vector Machine

Cited by 74 publications

References 7 publications

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

Exploring the Influence of Time Factor in Bug Report Prioritization

Improving Bug Triage with Relevant Search

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