  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.
