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Machine Learning for Software Engineering: A Tertiary Study
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DOI：10.1145/3572905.png)
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Abstract
En 中文
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.
Keywords:
Tertiary study
machine learning
software engineering
systematic literature
review
Journal
IF:
28
Papers:
2.5K
Citations:
3.5W
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