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- W4309395280 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 2009-2022, 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.
- W4309395280 created "2022-11-26" @default.
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- W4309395280 date "2022-11-17" @default.
- W4309395280 modified "2023-09-27" @default.
- W4309395280 title "Machine Learning for Software Engineering: A Tertiary Study" @default.
- W4309395280 doi "https://doi.org/10.48550/arxiv.2211.09425" @default.
- W4309395280 hasPublicationYear "2022" @default.
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