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• W771934433 abstract "In open source software development we have bug repository to which both developers and users can report bugs. Bug triage, deciding what to do with an incoming bug report, takes a large amount of developer resources and time. All newly coming bug reports must be triaged to determine whether the report is correct and requires attention and if it is, which potentially experienced developer/fixer will be assigned the responsibility of resolving the bug report. In this paper, we propose to apply association mining to assist in bug triage by using Apriori algorithm to predict the developer that should work on the bug based on the bug’s severity, priority and summary terms. We demonstrate our approach on collection of 1,695 bug reports of Thunderbird, AddOnSDK and Bugzilla products of Mozilla open source project. We have analyzed the association rules for top five assignee of the three products. Association rules can support the managers to improve its process during development and save time and resources." @default.
• W771934433 created "2016-06-24" @default.
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• W771934433 date "2015-01-01" @default.
• W771934433 modified "2023-09-26" @default.
• W771934433 title "Bug Assignee Prediction Using Association Rule Mining" @default.
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• W771934433 doi "https://doi.org/10.1007/978-3-319-21410-8_35" @default.
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