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- W4311012008 abstract "Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009 and 2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions, including conducting further empirical validation and industrial studies on ML, reconsidering deficient SE methods, documenting and automating data collection and pipeline processes, reexamining how industrial practitioners distribute their proprietary data, and implementing incremental ML approaches." @default.
- W4311012008 created "2022-12-22" @default.
- W4311012008 creator A5015449522 @default.
- W4311012008 creator A5031930371 @default.
- W4311012008 creator A5070988231 @default.
- W4311012008 date "2023-03-02" @default.
- W4311012008 modified "2023-09-27" @default.
- W4311012008 title "Machine Learning for Software Engineering: A Tertiary Study" @default.
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