Machine Learning for Software Engineering: A Tertiary Study
arXiv:2211.09425 · doi:10.1145/3572905
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.
37 pages, 6 figures, 7 tables, journal article
References in corpus (3)
- Systematic Literature Reviews in Software Engineering -- Enhancement of the Study Selection Process using Cohen's Kappa Statistic
- Machine Learning for Detecting Data Exfiltration: A Review
- Software Effort Estimation Accuracy Prediction of Machine Learning Techniques: A Systematic Performance Evaluation