Affiliation:
1. Athens University of Economics and Business, Athens, Greece
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 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.
Funder
European Union’s Horizon 2020 research and innovation programme
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Reference181 articles.
1. Software smell detection techniques: A systematic literature review;AbuHassan Amjad;J. Softw.: Evol. Process,2021
2. A systematic literature review on using machine learning algorithms for software requirements identification on stack overflow;Ahmad Arshad;Secur. Commun. Netw.,2020
3. Constrained interaction testing: A systematic literature study;Ahmed Bestoun S.;IEEE Access,2017
4. Bad smell detection using machine learning techniques: A systematic literature review;Al-Shaaby Ahmed;Arab. J. Sci. Eng.,2020
5. A systematic literature review of software effort prediction using machine learning methods;Ali Asad;J. Softw.: Evol. Process,2019
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