The Rise of Language Models in Mining Software Repositories: A Survey

The Mining Software Repositories (MSR) field focuses on analyzing the rich data contained in software repositories to derive actionable insights into software processes and products. Mining repositories at scale requires techniques capable of handling large volumes of heterogeneous data, a challenge for which language models (LMs) are increasingly well-suited. Since the advent of Transformer-based architectures, LMs have been rapidly adopted across a wide range of MSR tasks. This article presents a comprehensive survey of the use of LMs in MSR, based on an analysis of 177 papers. We examine how LMs are applied, the types of artifacts analyzed, which models are used, how their adoption has evolved over time, and the availability of supplementary materials and tools supporting reproducibility and reuse. Building on this analysis, we propose a taxonomy of LM applications in MSR, identify key trends shaping the field, and highlight open challenges alongside actionable directions for future research.

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Publication Details

Journal
ACM Transactions on Software Engineering and Methodology
Published
2026-10-30
DOI
https://doi.org/10.1145/3861019
Primary Topic
Software Engineering Research
Type
article
Field-Weighted Citation Impact
0.00

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article

The Rise of Language Models in Mining Software Repositories: A Survey

Sergio Segura, Saman Barakat, Miguel Romero-Arjona, Ana B. Sánchez
ACM Transactions on Software Engineering and Methodology
Software Engineering Research
article

The Rise of Language Models in Mining Software Repositories: A Survey

Sergio Segura, Saman Barakat, Miguel Romero-Arjona, Ana B. Sánchez
article en

Abstract

The Mining Software Repositories (MSR) field focuses on analyzing the rich data contained in software repositories to derive actionable insights into software processes and products. Mining repositories at scale requires techniques capable of handling large volumes of heterogeneous data, a challenge for which language models (LMs) are increasingly well-suited. Since the advent of Transformer-based architectures, LMs have been rapidly adopted across a wide range of MSR tasks. This article presents a comprehensive survey of the use of LMs in MSR, based on an analysis of 177 papers. We examine how LMs are applied, the types of artifacts analyzed, which models are used, how their adoption has evolved over time, and the availability of supplementary materials and tools supporting reproducibility and reuse. Building on this analysis, we propose a taxonomy of LM applications in MSR, identify key trends shaping the field, and highlight open challenges alongside actionable directions for future research.

ACM Transactions on Software Engineering and Methodology
Universidad de Sevilla (ES)
European Commission, Ministerio de Ciencia e Innovación, Junta de Andalucía, Agencia Estatal de Investigación
Openalex Percentile: Top 80%
Software Engineering Research
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