Problem
Research questions and friction points this paper is trying to address.
Mining Software Repositories
Language Models
Software Repository Data
Heterogeneous Data Analysis
Actionable Insights
The Rise of Language Models in Mining Software Repositories: A Survey
🤖 AI Summary
This study addresses the challenge of effectively leveraging language models to mine large-scale heterogeneous data from software repositories for actionable software engineering insights. Through a systematic literature review of 85 studies, it proposes the first taxonomy specifically designed for language model applications in software repository mining. Integrating Transformer model analysis, systematic review methodology, and reproducibility assessment, the work uncovers prevailing practices and key challenges in model selection, task adaptation, and reproducibility. It further identifies critical evolutionary trends in the field and offers concrete, actionable directions to guide future research endeavors.
📝 Abstract
The Mining Software Repositories (MSR) field focuses on analysing 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 85 papers. We examine how LMs are applied, the types of artefacts analysed, which models are used, how their adoption has evolved over time, and the extent to which studies support 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Language Models
Mining Software Repositories
Transformer-based architectures
Taxonomy
Reproducibility
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