research-article

A survey on machine learning techniques applied to source code

Authors:

Maria Kechagia,

Stefanos Georgiou,

Federica SarroAuthors Info & Claims

Volume 209, Issue C

https://doi.org/10.1016/j.jss.2023.111934

Published: 14 March 2024 Publication History

Abstract

The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis, such as testing and vulnerability detection. Such a large number of studies hinders the community from understanding the current research landscape. This paper aims to summarize the current knowledge in applied machine learning for source code analysis. We review studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we conducted an extensive literature search and identified 494 studies. We summarize our observations and findings with the help of the identified studies. Our findings suggest that the use of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task and summarize machine learning techniques employed. We identify a comprehensive list of available datasets and tools useable in this context. Finally, the paper discusses perceived challenges in this area, including the availability of standard datasets, reproducibility and replicability, and hardware resources.

Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Highlights

•

The use of ML techniques is constantly increasing for source code analysis.

•

A wide range SE tasks involving source code analysis use ML.

•

The study identifies challenges in the field and potential mitigations.

•

We identify commonly used datasets and tools used in the field.

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Cited By

Costa CLópez JCuadrado JEgyed AWimmer MChechik MCombemale B(2024)ModelMate: A recommender for textual modeling languages based on pre-trained language modelsProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3640310.3674089(183-194)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3640310.3674089
Guo YBettaieb SCasino F(2024)A comprehensive analysis on software vulnerability detection datasets: trends, challenges, and road aheadInternational Journal of Information Security10.1007/s10207-024-00888-y23:5(3311-3327)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10207-024-00888-y

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cover image Journal of Systems and Software

Journal of Systems and Software Volume 209, Issue C

Mar 2024

313 pages

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Published: 14 March 2024

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Costa CLópez JCuadrado JEgyed AWimmer MChechik MCombemale B(2024)ModelMate: A recommender for textual modeling languages based on pre-trained language modelsProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3640310.3674089(183-194)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3640310.3674089
Guo YBettaieb SCasino F(2024)A comprehensive analysis on software vulnerability detection datasets: trends, challenges, and road aheadInternational Journal of Information Security10.1007/s10207-024-00888-y23:5(3311-3327)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10207-024-00888-y

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