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.

机器学习 (ML) 技术提高了软件工程 (SE) 生命周期活动的有效性。我们系统地收集、质量评估、总结和分类了 2009 年至 2022 年间发表的 83 篇 ML for SE 评论，涵盖 6,117 项主要研究。ML 处理最多的 SE 领域是软件质量和测试，而以人为中心的领域似乎对 ML 更具挑战性。我们针对 SE 研究挑战和行动提出了许多 ML，包括对 ML 进行进一步的实证验证和工业研究，重新考虑有缺陷的 SE 方法，记录和自动化数据收集和管道流程，重新检查工业从业者如何分发他们的专有数据，以及实施增量机器学习方法。