A Systematic Literature Review on Automated Program Repair using Large Language Models

Rethinking Automated Program Repair: The Impact of Bug Complexity, Fault Localization, and LLM Cost-efficiency

A comprehensive empirical analysis of LLM-based APR techniques, focusing on how repair performance is shaped by bug complexity, fault localization, reasoning settings, and costs, reveals a nontrivial trade-off between repair effectiveness and computational cost.

Unreliable in Practice? A Comprehensive Study of Errors in LLM-Generated Code

It is observed that generated code often omits basic input validation or memory-safety checks, which can lead to overflows, resource exhaustion, or other reliability/security issues, and even the largest models frequently make simple mistakes.

Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair

This paper identifies patch verbosity as a major yet overlooked concern in LLM-based APR and proposes RECAP, a lightweight, plug-and-play adapter that attaches to existing repair frameworks after generation that achieves a substantially better size-correctness tradeoff.

Intelligent automated code review and software quality evaluation: a survey of AutoML and large language model techniques

ML-Enhanced Code Refactoring Recommendations for Improving Software Maintainability

Results show that ML-enhanced recommendations outperform traditional methods in accuracy, relevance, and impact on maintainability metrics, and highlight the potential of integrating ML into modern development practices to support developers in producing cleaner, more maintainable software systems.

A comparative analysis of automated techniques for security bug report identification

Timely identification of security-related bug reports is essential to minimize the window of vulnerabilities in software systems. Manually screening incoming bug reports to identify security-related issues is time-consuming, error-prone, and non-scalable for large-scale software systems. Thus, a variety of automatic techniques, including traditional machine learning (ML) techniques and large language models, have been proposed to facilitate this task. However, the literature remains fragmented. Most studies introduce or optimize a particular technique and evaluate it against a limited set of baselines, often under different experimental setups. As a result, it is difficult to compare their results and draw reliable conclusions about the effectiveness of existing approaches, leaving researchers and practitioners without clear guidance on which techniques are most suitable for the task. To address this gap, we conducted a comparative analysis of several promising automated techniques to identify security-related bug reports using benchmark datasets. We evaluated Logistic Regression, Support Vector Machines, Random Forest, OpenAI's GPT-5.2, BERT-base, RoBERTa, and SetFit (a state-of-the-art few-shot learning framework). Our results indicate that SetFit achieves the best overall performance, achieving an F1-score of 0.80 and outperforming other techniques on three of the four datasets. RoBERTa performs competitively and approaches SetFit in some projects, while traditional ML techniques, particularly Logistic Regression, remain a strong baseline in certain contexts. In contrast, GPT-5.2 performs poorly in both zero-shot and few-shot settings. In addition, cross-project experiments demonstrate that transfer learning can improve performance for projects with limited data, but may degrade results for projects with strong project-specific characteristics.