Add forgotten papers.

_publications/gupta2023grace.markdown

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+---
+layout: publication
+title: "Grace: Language Models Meet Code Edits"
+authors: Priyanshu Gupta, Avishree Khare, Yasharth Bajpai, Saikat Chakraborty, Sumit Gulwani, Aditya Kanade, Arjun Radhakrishna, Gustavo Soares, Ashish Tiwari
+conference: FSE
+year: 2023
+additional_links:
+ - {name: "ACM", url: "https://dl.acm.org/doi/abs/10.1145/3611643.3616253"}
+tags: ["editing"]
+---
+Developers spend a significant amount of time in editing code for a variety of reasons such as bug fixing or adding new features. Designing effective methods to predict code edits has been an active yet challenging area of research due to the diversity of code edits and the difficulty of capturing the developer intent. In this work, we address these challenges by endowing pre-trained large language models (LLMs) with the knowledge of relevant prior associated edits, which we call the Grace (Generation conditioned on Associated Code Edits) method. The generative capability of the LLMs helps address the diversity in code changes and conditioning code generation on prior edits helps capture the latent developer intent. We evaluate two well-known LLMs, codex and CodeT5, in zero-shot and fine-tuning settings respectively. In our experiments with two datasets, Grace boosts the performance of the LLMs significantly, enabling them to generate 29% and 54% more correctly edited code in top-1 suggestions relative to the current state-of-the-art symbolic and neural approaches, respectively.

_publications/mohajer2023skipanalyzer.markdown

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+
+---
+layout: publication
+title: "SkipAnalyzer: A Tool for Static Code Analysis with Large Language Models"
+authors: Mohammad Mahdi Mohajer, Reem Aleithan, Nima Shiri Harzevili, Moshi Wei, Alvine Boaye Belle, Hung Viet Pham, Song Wang
+conference:
+year: 2023
+additional_links:
+- {name: "ArXiV", url: "https://arxiv.org/abs/2310.18532"}
+tags: ["repair"]
+---
+We introduce SkipAnalyzer, a large language model (LLM)-powered tool for static code analysis. SkipAnalyzer has three components: 1) an LLM-based static bug detector that scans source code and reports specific types of bugs, 2) an LLM-based false-positive filter that can identify false-positive bugs in the results of static bug detectors (e.g., the result of step 1) to improve detection accuracy, and 3) an LLM-based patch generator that can generate patches for the detected bugs above. As a proof-of-concept, SkipAnalyzer is built on ChatGPT, which has exhibited outstanding performance in various software engineering tasks. To evaluate SkipAnalyzer, we focus on two types of typical and critical bugs that are targeted by static bug detection, i.e., Null Dereference and Resource Leak as subjects. We employ Infer to aid the gathering of these two bug types from 10 open-source projects. Consequently, our experiment dataset contains 222 instances of Null Dereference bugs and 46 instances of Resource Leak bugs. Our study demonstrates that SkipAnalyzer achieves remarkable performance in the mentioned static analysis tasks, including bug detection, false-positive warning removal, and bug repair. In static bug detection, SkipAnalyzer achieves accuracy values of up to 68.37% for detecting Null Dereference bugs and 76.95% for detecting Resource Leak bugs, improving the precision of the current leading bug detector, Infer, by 12.86% and 43.13%, respectively. For removing false-positive warnings, SkipAnalyzer can reach a precision of up to 93.88% for Null Dereference bugs and 63.33% for Resource Leak bugs. Additionally, SkipAnalyzer surpasses state-of-the-art false-positive warning removal tools. Furthermore, in bug repair, SkipAnalyzer can generate syntactically correct patches to fix its detected bugs with a success rate of up to 97.30%.

_publications/silva2023repairllama.markdown

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+
+---
+layout: publication
+title: "RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair"
+authors: André Silva, Sen Fang, Martin Monperrus
+conference:
+year: 2023
+additional_links:
+- {name: "ArXiV", url: "https://arxiv.org/abs/2312.15698"}
+tags: ["repair"]
+---
+Automated Program Repair (APR) has evolved significantly with the advent of Large Language Models (LLMs). Fine-tuning LLMs for program repair is a recent avenue of research, with many dimensions which have not been explored. Existing work mostly fine-tunes LLMs with naive code representations and is fundamentally limited in its ability to fine-tune larger LLMs. To address this problem, we propose RepairLLaMA, a novel program repair approach that combines 1) code representations for APR and 2) the state-of-the-art parameter-efficient LLM fine-tuning technique called LoRA. This results in RepairLLaMA producing a highly effective `program repair adapter' for fixing bugs with language models. Our experiments demonstrate the validity of both concepts. First, fine-tuning adapters with program repair specific code representations enables the model to use meaningful repair signals. Second, parameter-efficient fine-tuning helps fine-tuning to converge and contributes to the effectiveness of the repair adapter to fix data-points outside the fine-tuning data distribution. Overall, RepairLLaMA correctly fixes 125 Defects4J v2 and 82 HumanEval-Java bugs, outperforming all baselines.