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.

We describe the method used to create a dataset of open-source safety-critical software, such as that used for autonomous cars, healthcare, and autonomous aviation, through a systematic and rigorous selection process. The dataset can be used for empirical studies regarding the quality assessment of safety-critical software, its dependencies, and its development process, as well as comparative studies considering software from other domains.