Pre-trained or fine-tuned on large code corpora, Large Language Models (LLMs) have demonstrated strong performance in code completion tasks. However, their embedded knowledge is constrained by the timeliness of training data, which often includes code using deprecated APIs. Consequently, LLMs frequently generate deprec...
Guancheng Lin, Xiao Yu, Jacky Keung et al.· Proceedings of the ACM on so...· 0 citations
Large language models (LLMs) can generate executable code from natural language descriptions, but the resulting programs frequently contain bugs due to hallucinations. In the absence of formal specifications, existing approaches attempt to assess correctness using LLM-generated proxies such as tests or auto-formalized...
Yihan Dai, Sijie Liang, Haotian Xu et al.· Proceedings of the ACM on Pr...· 0 citations
Code-documentation inconsistencies are common and undesirable: they can lead to developer misunderstandings and software defects. This paper introduces DocPrism, a lightweight multi-language, code-documentation inconsistency detection tool. DocPrism uses a standard large language model (LLM) to analyze and explain inco...
Xiaomeng Xu, Zahin Wahab, Reid Holmes et al.· Proceedings of the ACM on so...· 0 citations
Driven by the advancements of Large Language Models (LLMs), LLM-powered agents are making significant improvements in software engineering tasks, yet struggle with complex, repository-level issue resolution. Existing agent-based methods have two key limitations. First, they lack of procedural knowledge (i.e., how an is...
Yang Xu, Jiayuan Zhou, Michael Pacheco et al.· Proceedings of the ACM on so...· 0 citations
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Information Retrieval-based Bug Localization (IRBL) aims to identify buggy source files for a given bug report. Traditional and deep learning-based IRBL techniques often suffer from vocabulary mismatch and dependence on project-specific metadata. In contrast, recent Large Language Model (LLM)-based approaches struggle...
Moumita Asad, Rafed Muhammad Yasir, Sam Malek· Proceedings of the ACM on so...· 0 citations
Coding agents powered by large language models are becoming central modules of modern IDEs. They help users to perform various complex coding tasks by invoking tools. Although powerful, tool-invocation operation in coding agents opens a substantial attack surface for adversaries. Prior work has demonstrated attacks aga...
Yuchong Xie, Mingyu Luo, Zesen Liu et al.· Proceedings of the ACM on so...· 0 citations
Third-Party Libraries are widely used in modern software development, yet their vulnerabilities pose serious security risks. This issue is particularly severe in the NPM ecosystem, where high-risk 1-day vulnerabilities can remain unpatched for extended periods. Although upgrading to the latest patched version is common...
Zeliang Yu, Ming Wen, Zichao Wei et al.· Proceedings of the ACM on so...· 1 citation
Program migration, which involves translating software systems from one programming language to another, is essential for modernizing legacy systems and improving maintainability. Recent large language models (LLMs) have demonstrated strong performance in code translation; however, existing methods and benchmarks still...
Xitao Li, Xiaofei Xie, Jianmin Wu et al.· Proceedings of the ACM on Pr...· 1 citation
Large Language Models (LLMs) have significantly improved programming efficiency by parsing natural language into code snippets. However, their performance degrades significantly as requirements scale; when faced with multi-modal documents containing hundreds of scenarios, LLMs often produce incorrect implementations or...
Weiyu Kong, Yun Lin, Xiwen Teoh et al.· Proceedings of the ACM on so...· 1 citation
Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational efficiency has become a critical bottleneck. Currently, these models rely on a text-based paradigm that treats source code as a linear sequence of tokens, which leads to a lin...
Yanan Shi, Chaoxiang Xie, Zhensu Sun et al.· Proceedings of the ACM on so...· 1 citation
With the rapid progress of deep learning and large language models (LLMs), companies spend enormous sums executing GPU kernels. These kernels have become prime targets for aggressive optimization. Recent efforts increasingly leverage LLMs to generate GPU kernels, but make no formal guarantees about the generated kernel...
Benjamin Driscoll, Kshitij Dubey, Anjiang Wei et al.· Proceedings of the ACM on Pr...· 1 citation
Recent advances in large language model agents offer the promise of automating end-to-end software development from natural language requirements. However, existing approaches largely adopt linear, waterfall-style pipelines, which oversimplify the iterative nature of real-world development and struggle with complex, la...
Junwei Liu, Xu Chen, Chong Wang et al.· Proceedings of the ACM on so...· 1 citation
