22 lines
2.1 KiB
TeX
22 lines
2.1 KiB
TeX
Currently, open source projects receive various kinds of issues daily,
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because of the extreme openness of Issue Tracking System (ITS) in GitHub.
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ITS is a labor-intensive and time-consuming task of issue categorization for project managers.
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%,and utilizing automatic approach to predict category of issue reports can effectively reduce management costs.
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%Many researches have worked on this problem in ITS like Bugzilla, ITracker, etc,
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%and adding structured information of ITS to classifier is a common method to improve performance of classification model.
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However, a contributor is only required a short textual abstract to report an issue in GitHub.
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Thus, most traditional classification approaches based on detailed and structured data
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(\eg priority, severity, software version and so on) are difficult to adopt.
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In addition, mislabeled issues is common in ITS, but less works study how mislabeled data affect the performance of classification model.
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%However, nearly all research works on few projects, and we don't know how these methods perform for most projects.
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%Otherwise, for ITS of GitHub, these structured information is hard to collect, which makes the method useless.
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In this paper, issue classification approaches on a large-scale dataset,
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including 80 popular projects and over 252,000 issue reports collected from GitHub, were investigated.
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First, four traditional text-based classification methods and their performances were discussed.
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Semantic perplexity (\ie an issue’s description confuses bug-related sentences with
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nonbug-related sentences) is a crucial factor that affects the classification performances based on quantitative and qualitative study.
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Then, A two-stage classifier framework based on the novel metrics of semantic perplexity of issue reports was designed.
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Results show that our two-stage classification can significantly improve issue classification performances.
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Finally, we study how mislabeled issues influence performance of issue classification model.
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We find when there are many semantic perplexity issues existing in dataset, our 2-stage framework has a well anti-noise ability. |