Machine Learning for Software Engineering

A Tertiary Study

Journal Article (2023)
Authors

Zoe Kotti (Athens University of Economics and Business)

Rafaila Galanopoulou (Athens University of Economics and Business)

D. Spinellis (Athens University of Economics and Business)

Affiliation
External organisation
To reference this document use:
https://doi.org/10.1145/3572905
More Info
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Publication Year
2023
Language
English
Affiliation
External organisation
Issue number
12
Volume number
55
DOI:
https://doi.org/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 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.

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