From 5af0f3c193ee1d2fd623ebf66a8a4b4c3f11bb48 Mon Sep 17 00:00:00 2001 From: Amir Date: Tue, 26 Mar 2024 16:42:01 +0100 Subject: [PATCH] Update - March'24 --- README.md | 268 +++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 236 insertions(+), 32 deletions(-) diff --git a/README.md b/README.md index 378a02e..95d7799 100644 --- a/README.md +++ b/README.md @@ -6,48 +6,48 @@ This repository contains a curated list of papers, PhD theses, datasets, and too Please feel free to send a pull request to add papers and relevant content that are not listed here. -> Note: to quickly access this page, use [ml4se.dev](https://ml4se.dev/) - ## Content - [Papers](#papers) - - [Type Inference](#type-inference) - - [Code Completion](#code-completion) - - [Code Generation](#code-generation) - - [Code Summarization](#code-summarization) - - [Code Embeddings/Representation](#code-embeddingsrepresentation) - - [Code Changes/Editing](#code-changesediting) - - [Code Comments](#code-comments) - - [Bug/Vulnerability Detection](#bugvulnerability-detection) - - [Source Code Modeling](#source-code-modeling) - - [Program Repair](#program-repair) - - [Program Translation](#program-translation) - - [Program Analysis](#program-analysis) - - [Software Testing](#software-testing) - - [Code Clone Detection](#code-clone-detection) - - [Code Search](#code-search) - - [Code Language Models](#code-language-models) - - [Code Review](#code-review) - - [Code Documentation](#code-documentation) - - [Empirical Studies](#empirical-studies) - - [Surveys](#surveys) - - [Misc](#misc) + - [Type Inference](#type-inference) + - [Code Completion](#code-completion) + - [Code Generation](#code-generation) + - [Code Summarization](#code-summarization) + - [Code Embeddings/Representation](#code-embeddingsrepresentation) + - [Code Changes/Editing](#code-changesediting) + - [Code Comments](#code-comments) + - [Bug/Vulnerability Detection](#bugvulnerability-detection) + - [Source Code Modeling](#source-code-modeling) + - [Program Repair](#program-repair) + - [Program Translation](#program-translation) + - [Program Analysis](#program-analysis) + - [Software Testing](#software-testing) + - [Code Clone Detection](#code-clone-detection) + - [Code Search](#code-search) + - [Code Language Models](#code-language-models) + - [Code Review](#code-review) + - [Code Documentation](#code-documentation) + - [Empirical Studies](#empirical-studies) + - [Surveys](#surveys) + - [Misc](#misc) - [PhD Theses](#phd-theses) - [Talks](#talks) - [Datasets](#datasets) - [Tools](#tools) - - [Source Code Analysis \& Processing](#source-code-analysis--processing) - - [Machine Learning](#machine-learning) - - [Code de-duplication](#code-de-duplication) - - [Misc](#misc-1) + - [Source Code Analysis \& Processing](#source-code-analysis--processing) + - [Machine Learning](#machine-learning) + - [Code de-duplication](#code-de-duplication) + - [Misc](#misc-1) - [Research Groups](#research-groups) - [Venues](#venues) - - [Conferences](#conferences) - - [Journals](#journals) + - [Conferences](#conferences) + - [Journals](#journals) # Papers ## Type Inference +- **Concrete Type Inference for Code Optimization using Machine Learning with SMT Solving** (2023), OOPSLA'23, Ye, Fangke, et al. [[pdf]](https://dl.acm.org/doi/pdf/10.1145/3622825) +- **Learning Type Inference for Enhanced Dataflow Analysis** (2023), ESORICS'23, Seidel, Lukas, et al. [[pdf]](https://arxiv.org/pdf/2310.00673) - **Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors** (2023), ICSE'24, Peng, Yun, et al. [[pdf]](https://arxiv.org/pdf/2306.01394) - **DeepInfer: Deep Type Inference from Smart Contract Bytecode** (2023), ESEC/FSE '23, Zhao, Kunsong, et al. [[pdf]](https://dl.acm.org/doi/pdf/10.1145/3611643.3616343) - **Statistical Type Inference for Incomplete Programs** (2023), ESEC/FSE '23, Peng, Yaohui, et al. [[pdf]](https://dl.acm.org/doi/pdf/10.1145/3611643.3616283) @@ -81,6 +81,13 @@ Please feel free to send a pull request to add papers and relevant content that ## Code Completion +- **REPOFUSE: Repository-Level Code Completion with Fused Dual Context** (2024), arxiv, Liang, Ming, et al. [[pdf]](https://arxiv.org/pdf/2402.14323) +- **Non-Autoregressive Line-Level Code Completion** (2024), TOSEM, Liu, Fang, et al. +- **IRCoCo: Immediate Rewards-Guided Deep Reinforcement Learning for Code Completion** (2024), arxiv, Li, Bolun, et al. [[pdf]](https://arxiv.org/pdf/2401.16637) +- **Language Models for Code Completion: A Practical Evaluation** (2024), ICSE'24, Izadi et al. [[pdf]](https://arxiv.org/pdf/2402.16197) +- **Context Composing for Full Line Code Completion** (2024), IDE'24, Semenkin et al. [[pdf]](https://arxiv.org/pdf/2402.09230) +- **De-Hallucinator: Iterative Grounding for LLM-Based Code Completion** (2024), arxiv, Eghbali, A., & Pradel, M. [[pdf]](https://arxiv.org/pdf/2401.01701) +- **When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference** (2024), ICSE'24, Sun, Zhensu, et al. [[pdf]](https://arxiv.org/abs/2401.09964v1) - **CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion** (2023), NeurIPS'23, Ding, Yangruibo, et al. [[pdf]](https://arxiv.org/abs/2310.11248) - **Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context** (2023), NeurIPS'23, Agrawal, Lakshya A., et al. [[pdf]](https://openreview.net/pdf?id=qPUbKxKvXq) - **Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation** (2023), NeurIPS'23, Liu, Jiawei, et al. [[pdf]](https://arxiv.org/abs/2305.01210) @@ -108,6 +115,40 @@ Please feel free to send a pull request to add papers and relevant content that ## Code Generation +- **Knowledge-Aware Code Generation with Large Language Models** (2024), ICPC'24, Huang et al. [[pdf]](https://arxiv.org/pdf/2401.15940.pdf) +- **PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models** (2024), arxiv, Chen, Simin, et al. [[pdf]](https://arxiv.org/pdf/2401.15545) +- **Ocassionally Secure: A Comparative Analysis of Code Generation Assistants** (2024), arxiv, Elgedawy et al. [[pdf]](https://arxiv.org/pdf/2402.00689.pdf) +- **StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback** (2024), arxiv, [[pdf]](https://arxiv.org/pdf/2402.01391v1.pdf) +- **Grounding Data Science Code Generation with Input-Output Specifications** (2024), arxiv, Wen, Yeming, et al. [[pdf]](https://arxiv.org/pdf/2402.08073) +- **MPIrigen: MPI Code Generation through Domain-Specific Language Models** (2024), arxiv, Schneider, Nadav, et al. [[pdf]](https://arxiv.org/pdf/2402.09126) +- **Instruction Tuning for Secure Code Generation** (2024), arxiv, He, Jingxuan, et al. [[pdf]](https://arxiv.org/pdf/2402.09497) +- **Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS** (2024), arxiv, DeLorenzo, Matthew, et al. [[pdf]](https://arxiv.org/pdf/2402.03289) +- **ARKS: Active Retrieval in Knowledge Soup for Code Generation** (2024), arxiv, Su, Hongjin, et al. [[pdf]](https://arxiv.org/pdf/2402.12317) +- **Test-Driven Development for Code Generation** (2024), arxiv, Mathews, N. S., & M. Nagappan [[pdf]](https://arxiv.org/pdf/2402.13521) +- **RRGcode: Deep hierarchical search-based code generation** (2024), JSS, Gou, Qianwen, et al. +- **LDB: A Large Language Model Debugger via Verifying Runtime Execution Step by Step** (2024), arxiv, Zhong et al. [[pdf]](https://arxiv.org/pdf/2402.16906) +- **Ansible Lightspeed: A Code Generation Service for IT Automation** (2024), arxiv, Sahoo, Priyam, et al. [[pdf]](https://arxiv.org/pdf/2402.17442) +- **DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions** (2024), arxiv, Wu et al. [[pdf]](https://arxiv.org/pdf/2312.04730) +- **Chain-of-Thought in Neural Code Generation: From and For Lightweight Language Models** (2024), arxiv, Yang, Guang, et al. [[pdf]](https://arxiv.org/pdf/2312.05562) +- **DevEval: Evaluating Code Generation in Practical Software Projects** (2024), arxiv, Li, Jia, et al. [[pdf]](https://arxiv.org/pdf/2401.06401.pdf) +- **Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation** (2024), arxiv, Wang, Chong, et al. [[pdf]](https://arxiv.org/pdf/2401.06391v1.pdf) +- **CODEAGENT: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges** (2024), arxiv, Zhang, Kechi, et al. [[pdf]](https://arxiv.org/pdf/2401.07339.pdf) +- **On the Reliability and Explainability of Language Models for Program Generation** (2024), TOSEM, Liu, Yue, et al. [[pdf]](https://arxiv.org/abs/2302.09587) +- **AgentCoder: Multiagent-Code Generation with Iterative Testing and Optimisation** (2024), arxiv, Huang, Dong, et al. [[pdf]](https://arxiv.org/pdf/2312.13010) +- **Dynamic Retrieval-Augmented Generation** (2024), arxiv, Shapkin et al. [[pdf]](https://arxiv.org/pdf/2312.08976.pdf) +- **Test-Case-Driven Programming Understanding in Large Language Models for Better Code Generation** (2024), arxiv, Tian, Z., & Chen, J. [[pdf]](https://arxiv.org/pdf/2309.16120) +- **Context-Aware Code Generation Framework for Code Repositories: Local, Global, and Third-Party Library Awareness** (2023), arxiv, Liao, Dianshu, et al. [[pdf]](https://arxiv.org/pdf/2312.05772) +- **CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules** (2024), ICLR'24, Le, Hung, et al. [[pdf]](https://arxiv.org/pdf/2310.08992) +- **Bias Testing and Mitigation in LLM-based Code Generation** (2024), arxiv, Huang, Dong, et al. [[pdf]](https://arxiv.org/pdf/2309.14345) +- **Magicoder: Source Code Is All You Need** (2023), arxiv, Wei, Yuxiang, et al. [[pdf]](https://arxiv.org/pdf/2312.02120.pdf) +- **Structured Chain-of-Thought Prompting for Code Generation** (2023), arxiv, Li, Jia, et al. [[pdf]](https://lj2lijia.github.io/papers/SCoT_Preprint.pdf) +- **Evaluating In-Context Learning of Libraries for Code Generation** (2023), arxiv, Patel, Arkil, et al. [[pdf]](https://arxiv.org/pdf/2311.09635) +- **Neural Rankers for Code Generation via Inter-Cluster Modeling** (2023), arxiv, To, Hung Quoc et al. [[pdf]](https://arxiv.org/pdf/2311.03366) +- **Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation** (2023), ICSE'24, Wang, Jiexin, et al. [[pdf]](https://arxiv.org/pdf/2310.16263) +- **Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis** (2023), arxiv, Gorinski, P. J., et al. [[pdf]](https://arxiv.org/pdf/2310.13669) +- **ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarification** (2023), arxiv, Mu, Fangwen, et al. [[pdf]](https://arxiv.org/pdf/2310.10996) +- **Large Language Model-Aware In-Context Learning for Code Generation** (2023), arxiv, Li, Jia, et al. [[pdf]](https://arxiv.org/pdf/2310.09748) +- **From Misuse to Mastery: Enhancing Code Generation with Knowledge-Driven AI Chaining** (2023), ASE'23, Ren, Xiaoxue, et al. [[pdf]](https://arxiv.org/pdf/2309.15606) - **Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models** (2023), arxiv, Weyssow, Martin, et al. [[pdf]](https://arxiv.org/pdf/2308.10462) - **CodeGen4Libs: A Two-Stage Approach for Library-Oriented Code Generation** (2023), arxiv, Liu, Mingwei, et al. [[pdf]](https://www.researchgate.net/profile/Mingwei-Liu-4/publication/373192571_CodeGen4Libs_A_Two-Stage_Approach_for_Library-Oriented_Code_Generation/links/64ded6fbcaf5ff5cd0c39162/CodeGen4Libs-A-Two-Stage-Approach-for-Library-Oriented-Code-Generation.pdf) - **Is Model Attention Aligned with Human Attention?: An Empirical Study on LLMs for Code Generation** (2023), arxiv, Kou, Bonan, et al. [[pdf]](https://arxiv.org/pdf/2306.01220) @@ -172,6 +213,15 @@ Please feel free to send a pull request to add papers and relevant content that ## Code Summarization +- **A Prompt Learning Framework for Source Code Summarization** (2024), TOSEM, Sun et al. +- **Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization** (2024), arxiv, Mastropaolo, Antonio, et al. [[pdf]](https://arxiv.org/pdf/2312.15475) +- **SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization** (2024), arxiv, Wang et al. [[pdf]](https://arxiv.org/pdf/2401.14727.pdf) +- **Towards Summarizing Code Snippets Using Pre-Trained Transformers** (2024), ICPC'24, Mastropaolo et al. [[pdf]](https://arxiv.org/pdf/2402.00519.pdf) +- **Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization** (2024), ICPC'24, Li, Jiliang, et al. [[pdf]](https://arxiv.org/pdf/2402.14182) +- **EyeTrans: Merging Human and Machine Attention for Neural Code Summarization** (2024), arxiv, Zhang, Yifan, et al. [[pdf]](https://arxiv.org/pdf/2402.14096) +- **Deep Is Better? An Empirical Comparison of Information Retrieval and Deep Learning Approaches to Code Summarization** (2024), TOSEM, Zhu, Tingwei, et al. +- **Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models** (2023), arxiv, Jin, Xin, et al. [[pdf]](https://arxiv.org/pdf/2312.09601) +- **Revisiting File Context for Source Code Summarization** (2023), arxiv, Bansal, Aakash, et al. [[pdf]](https://arxiv.org/pdf/2309.02326) - **Distilled GPT for Source Code Summarization** (2023), arxiv, Su, C. Y., & McMillan, C. [[pdf]](https://arxiv.org/pdf/2308.14731) - **An data augmentation method for source code summarization** (2023), Journal of Neurocomputing, Song, Zixuan, et al. - **Multilingual Adapter-based Knowledge Aggregation on Code Summarization for Low-Resource Languages** (2023), arxiv, Saberi, Iman et al. [[pdf]](https://arxiv.org/pdf/2307.07854) @@ -205,6 +255,16 @@ Please feel free to send a pull request to add papers and relevant content that ## Code Embeddings/Representation - **CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision** (2024),ISSTA'24, Wang, Hao, et al. [[pdf]](https://arxiv.org/pdf/2402.16928.pdf) [[code]](https://github.com/Hustcw/CLAP) +- **CONCORD: Towards a DSL for Configurable Graph Code Representation** (2024), arxiv, Saad, M., & Sharma, T. [[pdf]](https://arxiv.org/pdf/2401.17967) +- **Code Representation Learning at Scale** (2024), ICLR'24, Zhang et al. [[pdf]](https://arxiv.org/pdf/2402.01935v1.pdf) +- **Structured Code Representations Enable Data-Efficient Adaptation of Code Language Models** (2024), arxiv, Agarwal, Mayank, et al. [[pdf]](https://arxiv.org/pdf/2401.10716) +- **Pass-Tuning: Towards Structure-Aware Parameter-Efficient Tuning for Code Representation Learning** (2023), EMNLP'23, Chen, Nuo, et al. [[pdf]](https://aclanthology.org/2023.findings-emnlp.42.pdf) +- **TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree transformation** (2023), arxiv, Xian, Zixiang, et al. [[pdf]](https://arxiv.org/pdf/2311.08157) +- **CoCoAST: Representing Source Code via Hierarchical Splitting and Reconstruction of Abstract Syntax Trees** (2023), EMSE, Shi, Ensheng, et al. +- **Language Agnostic Code Embeddings** (2023), arxiv, Utpala, Saiteja et al. [[pdf]](https://arxiv.org/pdf/2310.16803) +- **Code Representation Pre-training with Complements from Program Executions** (2023), arxiv, Huang, Jiabo, et al. [[pdf]](https://arxiv.org/pdf/2309.09980) +- **FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations** (2023), ICSE'24, Niu, Changan, et al. [[pdf]](https://arxiv.org/pdf/2309.04828.pdf) +- **CombTransformers: Statement-Wise Transformers for Statement-Wise Representations** (2023), TSE, Bertolotti, F., & Cazzola, W. - **kTrans: Knowledge-Aware Transformer for Binary Code Embedding** (2023), arxiv, Wenyu, Zhu, et al. [[pdf]](https://arxiv.org/pdf/2308.12659.pdf)[[code]](https://github.com/Learner0x5a/kTrans-release) - **TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills** (2023), arxiv, Sun, Qiushi, et al. [[pdf]](https://arxiv.org/pdf/2306.07285) - **CodeGrid: A Grid Representation of Code** (2023), ISSTA'23, Kaboré, Abdoul Kader, et al. @@ -260,7 +320,12 @@ Please feel free to send a pull request to add papers and relevant content that ## Code Changes/Editing +- **Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions** (2023), arxiv, Cassano, Federico, et al. [[pdf]](https://arxiv.org/pdf/2312.12450) +- **Grace: Language Models Meet Code Edits** (2023), FSE'23, Gupta, Priyanshu, et al. +- **AdaptivePaste: Intelligent Copy-Paste in IDE** (2023), FSE'23, Liu, Xiaoyu, et al. - **Learning to Represent Patches** (2023), ICSE'24, Tang, Xunzhu, et al. [[pdf]](https://arxiv.org/pdf/2308.16586) +- **InstructCoder: Empowering Language Models to Edit Code** (2023), arxiv, Hu, Qisheng, et al. [[pdf]](https://openreview.net/pdf?id=islVqaCzfa) +- **CCBERT: Self-Supervised Code Change Representation Learning** (2023), ICSME'23, Zhou, Xin, et al. [[pdf]](https://arxiv.org/pdf/2309.15474) - **Automated Code Editing with Search-Generate-Modify** (2023), arxiv, Liu, Changshu, et al. [[pdf]](https://arxiv.org/pdf/2306.06490) - **Multilingual Code Co-Evolution Using Large Language Models** (2023), arxiv, Zhang, Jiyang, et al. [[pdf]](https://arxiv.org/pdf/2307.14991) - **Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing** (2023), arxiv, Wei, Jiayi, et al. [[pdf]](https://arxiv.org/pdf/2305.18584) @@ -287,6 +352,18 @@ Please feel free to send a pull request to add papers and relevant content that ## Bug/Vulnerability Detection +- **Pre-training by Predicting Program Dependencies for Vulnerability Analysis Tasks** (2024), ICSE'24, Liu et al. [[pdf]](https://arxiv.org/pdf/2402.00657.pdf) +- **JITGNN: A deep graph neural network framework for Just-In-Time bug prediction** (2024), JSS, Keshavarz, H., and G. Rodríguez-Pérez +- **DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models** (2024), arxiv, Berabi, Berkay, et al. [[pdf]](https://arxiv.org/pdf/2402.13291) +- **Analyzing source code vulnerabilities in the D2A dataset with ML ensembles and C-BERT** (2024), EMSE, Pujar, Saurabh, et al. +- **Chain-of-Thought Prompting of Large Language Models for Discovering and Fixing Software Vulnerabilities** (2024), arxiv, Nong, Yu, et al. [[pdf]](https://arxiv.org/pdf/2402.17230) +- **Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models** (2024), arxiv, N. T. Islam & P. Najafirad [[pdf]](https://arxiv.org/pdf/2401.07031.pdf) +- **Vision Transformer Inspired Automated Vulnerability Repair** (2024), TOSEM, Fu, Michael, et al. +- **Can Large Language Models Identify And Reason About Security Vulnerabilities? Not Yet** (2023), arxiv, Ullah, Saad, et al. [[pdf]](https://arxiv.org/pdf/2312.12575) +- **BinGo: Identifying Security Patches in Binary Code with Graph Representation Learning** (2023), ASIACC'24, He, Xu, et al. [[pdf]](https://arxiv.org/pdf/2312.07921) +- **Commit-Level, Neural Vulnerability Detection and Assessment** (2023), FSE'23, Li, Yi, et al. +- **Learning Defect Prediction from Unrealistic Data** (2023), arxiv, Alrashedy, Kamel, et al. [[pdf]](https://arxiv.org/html/2311.00931v2) +- **SparseCoder: Advancing Source Code Analysis with Sparse Attention and Learned Token Pruning** (2023), arxiv, Yang, Xueqi, et al. [[pdf]](https://arxiv.org/pdf/2310.07109) - **How Far Have We Gone in Vulnerability Detection Using Large Language Models** (2023), arxiv, Zeyu, Gao, et al. [[pdf]](https://arxiv.org/pdf/2311.12420.pdf) - **Pre-training Code Representation with Semantic Flow Graph for Effective Bug Localization** (2023), arxiv, Du, Y., & Yu, Z. [[pdf]](https://arxiv.org/pdf/2308.12773) - **PrAIoritize: Learning to Prioritize Smart Contract Bugs and Vulnerabilities** (2023), arxiv, Soud, Majd, et al. [[pdf]](https://arxiv.org/pdf/2308.11082) @@ -333,6 +410,7 @@ Please feel free to send a pull request to add papers and relevant content that ## Source Code Modeling +- **Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models** (2024), ICSE'24, Gao, Shuzheng, et al. [[pdf]](https://arxiv.org/pdf/2401.01060) - **CONCORD: Clone-aware Contrastive Learning for Source Code** (2023), ISSTA'23, Ding, Yangruibo, et al. [[pdf]](https://arxiv.org/pdf/2306.03234) - **TRACED: Execution-aware Pre-training for Source Code** (2023), ICSE'24, Ding, Yangruibo, et al. [[pdf]](https://arxiv.org/pdf/2306.07487) - **ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning** (2023), arxiv, Liu, Shangqing, et al. [[pdf]](https://arxiv.org/pdf/2301.09072) @@ -349,6 +427,14 @@ Please feel free to send a pull request to add papers and relevant content that ## Program Repair +- **RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair** (2024), arxiv, Silva, André et al. [[pdf]](https://arxiv.org/pdf/2312.15698) +- **On Repairing Quantum Programs Using ChatGPT** (2024), Q-SE'24, Guo et al. [[pdf]](https://arxiv.org/pdf/2401.14913.pdf) +- **CigaR: Cost-efficient Program Repair with LLMs** (2024), arxiv, Hidvégi, Dávid, et al. [[pdf]](https://arxiv.org/pdf/2402.06598) +- **PyTy: Repairing Static Type Errors in Python** (2024), ICSE'24, Chow, Yiu W., et al. [[pdf]](https://arxiv.org/pdf/2401.06619.pdf) +- **A Novel Approach for Automated Program Repair using Round-Trip Translation with Large Language Models** (2024), arxiv, Ruiz, F. Vallecillos, et al. [[pdf]](https://arxiv.org/pdf/2401.07994.pdf) +- **APPT: Boosting Automated Patch Correctness Prediction via Fine-tuning Pre-trained Models** (2024), TSE, Zhang, Quanjun, et al. +- **Towards Low-Resource Automatic Program Repair with Meta-Learning and Pretrained Language Models** (2023), EMNLP'23, Wang, Weishi, et al. [[pdf]](https://openreview.net/pdf?id=aLkknJNdl6) +- **GPT-3-Powered Type Error Debugging: Investigating the Use of Large Language Models for Code Repair** (2023), SLE'23, Ribeiro, Francisco, et al. - **Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering** (2023), arxiv, Paul, Rishov, et al. [[pdf]](https://lsiddiqsunny.github.io/public/2304.07840.pdf) - **Code Similarity and Location-Awareness Automatic Program Repair** (2023), Applied Sciences, Cao, Heling, et al. [[pdf]](https://www.mdpi.com/2076-3417/13/14/8519/pdf) - **The Future Can’t Help Fix The Past: Assessing Program Repair In The Wild** (2023), RG, Kabadi, Vinay, et al. [[pdf]](https://www.researchgate.net/profile/Xuan-Bach-D-Le/publication/372788577_The_Future_Can't_Help_Fix_The_Past_Assessing_Program_Repair_In_The_Wild/links/64c8d8ff862f8d2999875f1e/The-Future-Cant-Help-Fix-The-Past-Assessing-Program-Repair-In-The-Wild.pdf) @@ -395,6 +481,9 @@ Please feel free to send a pull request to add papers and relevant content that ## Program Translation +- **Few-shot code translation via task-adapted prompt learning** (2024), JSS, Li, Xuan, et al. +- **Unsupervised Binary Code Translation with Application to Code Similarity Detection and Vulnerability Discovery** (2023), EMNLP'23, Ahmad, I., & Luo, L. [[pdf]](https://openreview.net/pdf?id=5EHI2FGf1D) +- **TransMap: Pinpointing Mistakes in Neural Code Translation** (2023), FSE'23, Wang, Bo, et al. - **On the Evaluation of Neural Code Translation: Taxonomy and Benchmark** (2023), arxiv, Jiao, Mingsheng, et al. [[pdf]](https://arxiv.org/pdf/2308.08961) - **Attention, Compilation, and Solver-based Symbolic Analysis are All You Need** (2023), arxiv, Jana, Prithwish, et al. [[pdf]](https://arxiv.org/pdf/2306.06755) - **Understanding the Effectiveness of Large Language Models in Code Translation** (2023), arxiv, Pan, Rangeet, et al. [[pdf]](https://arxiv.org/pdf/2308.03109) @@ -409,6 +498,8 @@ Please feel free to send a pull request to add papers and relevant content that ## Program Analysis +- **On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study** (2024), MSR'24, Mir, Amir et al. [[pdf]](https://arxiv.org/pdf/2402.07294) +- **Static Code Analysis in the AI Era: An In-depth Exploration of the Concept, Function, and Potential of Intelligent Code Analysis** (2023), arxiv, Fan, Gang, et al. [[pdf]](https://arxiv.org/pdf/2310.08837) - **(Partial) Program Dependence Analysis** (2023), ICSE'23, Yadavally, Aashish, et al. [[pdf]](https://aashishyadavally.github.io/files/C5.pdf)[[code]](https://github.com/aashishyadavally/NeuralPDA/) - **Precise Data-Driven Approximation for Program Analysis via Fuzzing** (2023), ASE'23, Parasaram, Nikhil, et al. [[pdf]](https://mechtaev.com/files/ase23.pdf) - **The Hitchhiker’s Guide to Program Analysis: A Journey with Large Language Models** (2023), arxiv, Li, Haonan, et al. [[pdf]](https://arxiv.org/pdf/2308.00245) @@ -417,6 +508,26 @@ Please feel free to send a pull request to add papers and relevant content that ## Software Testing +- **Automated Test Case Repair Using Language Models** (2024), arxiv, Yaraghi, A. S., et al. [[pdf]](https://arxiv.org/pdf/2401.06765) +- **Using GitHub Copilot for Test Generation in Python: An Empirical Study** (2024), AST'24, El Haji, Khalid et al. [[pdf]](https://azaidman.github.io/publications/elhajiAST2024.pdf) +- **Intent-Driven Mobile GUI Testing with Autonomous Large Language Model Agents** (2024), arxiv, Yoon, Juyeon et al. [[pdf]](https://coinse.github.io/publications/pdfs/Yoon2024aa.pdf) +- **Enhancing Large Language Models for Text-to-Testcase Generation** (2024), arxiv, Alagarsamy, Saranya, et al. [[pdf]](https://arxiv.org/pdf/2402.11910) +- **CovRL: Fuzzing JavaScript Engines with Coverage-Guided Reinforcement Learning for LLM-based Mutation** (2024), arxiv, Eom, Jueon et al. [[pdf]](https://arxiv.org/pdf/2402.12222) +- **Code-Aware Prompting: A study of Coverage guided Test Generation in Regression Setting using LLM** (2024), arxiv, Ryan, Gabriel, et al. [[pdf]](https://arxiv.org/pdf/2402.00097) +- **LLM4FUZZ: Guided Fuzzing of Smart Contracts with Large Language Models** (2024), arxiv, Shou, Chaofan, et al. [[pdf]](https://arxiv.org/pdf/2401.11108.pdf) +- **Automated Test Case Repair Using Language Models** (2024), arxiv, Yaraghi, A. S., et al. [[pdf]](https://arxiv.org/pdf/2401.06765.pdf) +- **Fuzz4All: Universal Fuzzing with Large Language Models** (2024), ICSE'24, Xia, C., et al. [[pdf]](https://www.software-lab.org/publications/icse2024_Fuzz4All.pdf) +- **TDD Without Tears: Towards Test Case Generation from Requirements through Deep Reinforcement Learning** (2024), arxiv, Takerngsaksiri, Wannita, et al. [[pdf]](https://arxiv.org/html/2401.07576v1) +- **Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools** (2024), arxiv, Bhatia, Shreya, et al. [[pdf]](https://arxiv.org/pdf/2312.10622) +- **CAT-LM: Training Language Models on Aligned Code And Tests**, ASE'23, Rao, Nikitha, et al. [[pdf]](https://arxiv.org/pdf/2310.01602) +- **LLM4TDD: Best Practices for Test Driven Development Using Large Language Models** (2023), arxiv, Piya, S., & Sullivan, A. [[pdf]](https://arxiv.org/pdf/2312.04687) +- **Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing** (2023), arxiv, Yoon, Juyeon, et al. [[pdf]](https://arxiv.org/pdf/2311.08649) +- **White-box Compiler Fuzzing Empowered by Large Language Models** (2023), arxiv, Yang, Chenyuan, et al. [[pdf]](https://arxiv.org/pdf/2310.15991) +- **Test Case Recommendations with Distributed Representation of Code Syntactic Features** (2023), ASEW'23, Rezaei, M. et al. [[pdf]](https://arxiv.org/pdf/2310.03174) +- **Automatic Generation of Test Cases based on Bug Reports: a Feasibility Study with Large Language Models** (2023), arxiv, Plein, Laura, et al. [[pdf]](https://arxiv.org/pdf/2310.06320) +- **The Program Testing Ability of Large Language Models for Code** (2023), arxiv, Xiong, W. et al. [[pdf]](https://arxiv.org/pdf/2310.05727) +- **Revisiting Neural Program Smoothing for Fuzzing** (2023), FSE'23, Bansal, Aakash et al. [[pdf]](https://arxiv.org/pdf/2309.02326) +- **An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation** (2023), arxiv, Schäfer, Max, et al. [[pdf]](https://arxiv.org/pdf/2302.06527) - **Automated Test Case Generation Using Code Models and Domain Adaptation** (2023), arxiv, Hashtroudi, Sepehr, et al. [[pdf]](https://arxiv.org/pdf/2308.08033.pdf) - **Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing** (2023), arxiv, Dakhel, A. M., et al. [[pdf]](https://arxiv.org/pdf/2308.16557) - **Automatic Unit Test Generation for Deep Learning Frameworks based on API Knowledge** (2023), arxiv, Narayanan, A., et al. [[pdf]](https://arxiv.org/pdf/2307.00404) @@ -439,8 +550,9 @@ Please feel free to send a pull request to add papers and relevant content that ## Code Clone Detection - **CEBin: A Cost-Effective Framework for Large-Scale Binary Code Similarity Detection** (2024),ISSTA'24, Wang, Hao, et al. [[pdf]](https://arxiv.org/pdf/2402.18818.pdf) [[code]](https://github.com/Hustcw/CEBin) +- **Investigating the Efficacy of Large Language Models for Code Clone Detection** , ICPC'24, Khajezade, Mohamad, et al. [[pdf]](https://arxiv.org/pdf/2401.13802) +- **Improving Cross-Language Code Clone Detection via Code Representation Learning and Graph Neural Networks** (2023), arxiv, Mehrotra, Nikita, et al. - **ZC3: Zero-Shot Cross-Language Code Clone Detection** (2023), arxiv, Li, Jia, et al. [[pdf]](https://arxiv.org/pdf/2308.13754) -- **Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey** (2023), arxiv, Dou, Shihan, et al. [[pdf]](https://arxiv.org/pdf/2308.01191) - **Comparison and Evaluation of Clone Detection Techniques with Different Code Representations** (2023), ICSE'23, Wang, Yuekun, et al. [[pdf]](https://wu-yueming.github.io/Files/ICSE2023_TACC.pdf) - **Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey** (2023), arxiv, Dou, Shihan, et al. [[pdf]](https://arxiv.org/pdf/2308.01191) - **CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search** (2023), arxiv, Sorokin, Nikita, et al. [[pdf]](https://arxiv.org/pdf/2305.11626.pdf) @@ -456,7 +568,17 @@ Please feel free to send a pull request to add papers and relevant content that ## Code Search +- **Rapid: Zero-shot Domain Adaptation for Code Search with Pre-trained Models** (2024), TOSEM, Fan et al. +- **Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search** (2024), arxiv, Li, Haochen et al. [[pdf]](https://arxiv.org/pdf/2401.04514) +- **Rapid: Zero-shot Domain Adaptation for Code Search with Pre-trained Models** (2024), TOSEM, Fan, Guodong, et al. - **Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search** (2024), arxiv, Li, Haochen, et al. [[pdf]](https://arxiv.org/pdf/2401.04514.pdf) +- **Intervention-Based Alignment of Code Search with Execution Feedback** (2023), EMNLP'23, Han, Hojae, et al. [[pdf]](https://aclanthology.org/2023.findings-emnlp.148.pdf) +- **You Augment Me: Exploring ChatGPT-based Data Augmentation for Semantic Code Search** (2023), ICSME'23, Wang, Yanlin, et al. [[pdf]](https://yanlin.info/papers/ChatDance-icsme23.pdf) +- **Efficient Text-to-Code Retrieval with Cascaded Fast and Slow Transformer Models** (2023), FSE'23, Gotmare, A., et al. +- **GraphSearchNet: Enhancing GNNs via capturing global dependencies for semantic code search** (2023), TSE, Liu, Shangqing, et al. [[pdf]](https://arxiv.org/pdf/2111.02671) +- **KAPE: kNN-based Performance Testing for Deep Code Search** (2023), TOSEM, uo, Yuejun, et al. [[pdf]](https://dl.acm.org/doi/pdf/10.1145/3624735) +- **Two Birds with One Stone: Boosting Code Generation and Code Search via a Generative Adversarial Network** (2023), OOPSLA'23, Wang, Shangwen, et al. [[pdf]](https://dl.acm.org/doi/pdf/10.1145/3622815) +- **Hyperbolic Code Retrieval: A Novel Approach for Efficient Code Search Using Hyperbolic Space Embeddings** (2023), arxiv, Tang, Xunzhu, et al. [[pdf]](https://arxiv.org/pdf/2308.15234) - **Rethinking Negative Pairs in Code Search** (2023), EMNLP'23, Li, Haochen, et al. [[pdf]](https://arxiv.org/abs/2310.08069)[[code]](https://github.com/Alex-HaochenLi/Soft-InfoNCE) - **Hyperbolic Code Retrieval: A Novel Approach for Efficient Code Search Using Hyperbolic Space Embeddings** (2023), AAAI'24, Tang, Xunzhu, et al. [[pdf]](https://arxiv.org/pdf/2308.15234) - **Self-Supervised Query Reformulation for Code Search** (2023), FSE'23, Mao, Yuetian, et al. [[pdf]](https://arxiv.org/pdf/2307.00267) @@ -473,6 +595,7 @@ Please feel free to send a pull request to add papers and relevant content that ## Code Language Models +- **CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model** (2023), arxiv, Di, Peng, et al. [[pdf]](https://arxiv.org/pdf/2310.06266) - **Code Llama: Open Foundation Models for Code** (2023), Meta AI, Rozière et al. [[pdf]](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) - **Gorilla: Large Language Model Connected with Massive APIs** (2023), arxiv, Patil, Shishir G., et al. [[pdf]](https://arxiv.org/pdf/2305.15334) - **CodeT5+: Open Code Large Language Models for Code Understanding and Generation** (2023), arxiv, Wang, Yue, et al. [[pdf]](https://arxiv.org/pdf/2305.07922) @@ -489,6 +612,10 @@ Please feel free to send a pull request to add papers and relevant content that ## Code Review +- **Security Code Review by LLMs: A Deep Dive into Responses** (2024), arxiv, Yu et al. [[pdf]](https://arxiv.org/pdf/2401.16310) +- **GPT-3.5 for Code Review Automation: How Do Few-Shot Learning, Prompt Design, and Model Fine-Tuning Impact Their Performance?** (2024), arxiv, Pornprasit, C., & Tantithamthavorn, C. [[pdf]](https://arxiv.org/pdf/2402.00905) +- **Team-related Features in Code Review Prediction Models** (2023), arxiv, Witter, Eduardo et al. [[pdf]](https://arxiv.org/pdf/2312.06244) +- **Unity is Strength: Cross-Task Knowledge Distillation to Improve Code Review Generation** (2023), arxiv, Sghaier et al. [[pdf]](https://arxiv.org/pdf/2309.03362) - **LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning** (2023), arxiv, Lu, Junyi, et al. [[pdf]](https://arxiv.org/pdf/2308.11148) - **Learning to Predict Code Review Completion Time In Modern Code Review** (2023), EMSE journal, Chouchen, Moataz, et al. - **ReviewRanker: A Semi-Supervised Learning Based Approach for Code Review Quality Estimation** (2023), arxiv, Mahbub, Saifullah, et al. [[pdf]](https://arxiv.org/pdf/2307.03996) @@ -499,6 +626,7 @@ Please feel free to send a pull request to add papers and relevant content that ## Code Documentation +- **APIDocBooster: An Extract-Then-Abstract Framework Leveraging Large Language Models for Augmenting API Documentation** (2024), arxiv, Yang, Chengran, et al. [[pdf]](https://arxiv.org/pdf/2312.10934) - **Evaluating Transfer Learning for Simplifying GitHub READMEs** (2023), FSE'23, Gao, Haoyu, et al. [[pdf]](https://arxiv.org/pdf/2308.09940) - **Too long; didn’t read: Automatic summarization of GitHub README.MD with Transformers** (2023), EASE'23, Doan, Thu TH, et al. [[pdf]](https://dl.acm.org/doi/pdf/10.1145/3593434.3593448) - **HotGPT: How to Make Software Documentation More Useful with a Large Language Model?** (2023), HOTOS'23, Su, Yiming, et al. @@ -507,7 +635,28 @@ Please feel free to send a pull request to add papers and relevant content that ## Empirical Studies +- **Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code** (2024), arxiv, Honarvar, Shahin, et al. [[pdf]](https://arxiv.org/pdf/2312.14856) +- **An Empirical Study on Distilling ChatGPT for Advancing Code Intelligence Tasks** (2024), arxiv, Yang et al. [[pdf]](https://arxiv.org/pdf/2312.15202.pdf) +- **How to Refactor this Code? An Exploratory Study on Developer-ChatGPT Refactoring Conversations** (2024), arxiv, AlOmar, Eman Abdullah, et al. [[pdf]](https://arxiv.org/pdf/2402.06013) +- **Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study** (2024), arxiv, Liu, Shuo, et al. [[pdf]](https://arxiv.org/pdf/2402.06247) +- **Do Large Code Models Understand Programming Concepts? A Black-box Approach** (2024), arxiv, Hooda, Ashish, et al. [[pdf]](https://arxiv.org/pdf/2402.05980) +- **Generating Java Methods: An Empirical Assessment of Four AI-Based Code Assistants** (2024), ICPC'24, Corso, Vincenzo, et al. [[pdf]][https://arxiv.org/pdf/2402.08431] +- **On the Reliability and Explainability of Language Models for Program Generation** (2024), TSE, Liu, Yue, et al. +- **Analyzing Developer Use of ChatGPT Generated Code in Open Source GitHub Projects** (2024), arxiv, Grewal, Balreet, et al. [[pdf]](https://asgaard.ece.ualberta.ca/papers/Conference/MSR_2024_Grewal_Analyzing_Developer_Use_of_ChatGPT_Generated_Code_in_Open_Source_GitHub_Projects.pdf) +- **Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generation** (2024), arxiv, Jin, Kailun, et al. [[pdf]](https://arxiv.org/pdf/2402.11702) +- **Studying LLM Performance on Closed- and Open-source Data** (2024), arxiv, Ahmed, Toufique, et al. [[pdf]](https://arxiv.org/pdf/2402.15100) +- **On Trojan Signatures in Large Language Models of Code** (2024), arxiv, Hussain et al. [[pdf]](https://arxiv.org/pdf/2402.16896) +- **Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?** (2024), arxiv, Velasco, Alejandro, et al. [[pdf]](https://arxiv.org/pdf/2401.01512) +- **An empirical assessment of different word embedding and deep learning models for bug assignment** (2024), JSS, Wang, Rongcun, et al. +- **On Extracting Specialized Code Abilities from Large Language Models: A Feasibility Study** (2024), ICSE'24, Li, Zongjie, et al. [[pdf]](https://daoyuan14.github.io/papers/ICSE24_LLMImitation.pdf) +- **Exploring the Effect of Multiple Natural Languages on Code Suggestion Using GitHub Copilot** (2024), MSR'24, Koyanagi, Kei, et al. +- **Boosting Source Code Learning with Text-Oriented Data Augmentation: An Empirical Study** (2023), QRS-C'23, [[pdf]](https://qrs23.techconf.org/download/webpub/pdfs/QRS-C2023-56EpUKA3a3CGa6xc1KYNzL/593900a373/593900a373.pdf) +- **How to get better embeddings with code pre-trained models? An empirical study** (2023), arxiv, Zhao, Yu, et al.[[pdf]](https://arxiv.org/pdf/2311.08066) +- **Evaluating Pre-trained Language Models for Repairing API Misuses** (2023), arxiv, Zhang, Ting, et al. [[pdf]](https://arxiv.org/pdf/2310.16390) +- **Prompt Engineering or Fine Tuning: An Empirical Assessment of Large Language Models in Automated Software Engineering Tasks** (2023), arxiv, Shin, Jiho, et al. [[pdf]](https://arxiv.org/pdf/2310.10508) - **Natural Language to Code: How Far Are We?** (2023), FSE'23, Wang, Shangwen, et al. [[pdf]](https://www.researchgate.net/profile/Shangwen-Wang/publication/373141125_Natural_Language_to_Code_How_Far_Are_We/links/64dc28d625837316ee1201e5/Natural-Language-to-Code-How-Far-Are-We.pdf) +- **Prompt Tuning in Code Intelligence: An Experimental Evaluation** (2023), TSE, Wang, Chaozheng, et al. +- **Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?** (2023), arxiv, Zhuo, Terry Yue, et al. [[pdf]](https://arxiv.org/pdf/2309.07804) - **How are We Detecting Inconsistent Method Names? An Empirical Study from Code Review Perspective** (2023), arxiv, Kim, Kisub, et al. [[pdf]](https://arxiv.org/pdf/2308.12701) - **Benchmarking Causal Study to Interpret Large Language Models for Source Code** (2023), arxiv, Rodriguez-Cardenas, D., et al. [[pdf]](https://arxiv.org/pdf/2308.12415) - **On the Impact of Language Selection for Training and Evaluating Programming Language Models** (2023), SCAM'23, Katzy, J., et al. [[pdf]](https://arxiv.org/pdf/2308.13354) @@ -571,12 +720,13 @@ Please feel free to send a pull request to add papers and relevant content that ## Surveys +- **A Survey on Machine Learning Techniques Applied to Source Code** (2024), JSS, Sharma, Tushar, et al. [[pdf]](https://arxiv.org/pdf/2110.09610) +- **A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends** (2024), TOSEM, Zheng, Zibin, et al. [[pdf]](https://arxiv.org/pdf/2311.10372.pdf) +- **A Survey on Large Language Models for Software Engineering** (2023), arxiv, Zhang, Quanjun, et al. [[pdf]](https://arxiv.org/pdf/2312.15223) - **Large Language Models for Software Engineering: A Systematic Literature Review** (2023), arxiv, Hou, Xinyi, et al. [[pdf]](https://arxiv.org/pdf/2308.10620) -- **Towards an Understanding of Large Language Models in Software Engineering Tasks** (2023), arxiv, Zheng, Zibin, et al. [[pdf]](https://arxiv.org/pdf/2308.11396) - **When Neural Model Meets NL2Code: A Survey** (2023), ACL'23, Zan, Daoguang, et al. [[pdf]](https://aclanthology.org/2023.acl-long.411.pdf) - **Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code** (2022), arxiv 2022, Niu, Changan, et al. [[pdf]](https://arxiv.org/pdf/2205.11739) - **A Survey of Deep Learning Models for Structural Code Understanding** (2022), arxiv 2022, Wu, Ruoting, et al. [[pdf]](https://arxiv.org/pdf/2205.01293) -- **A Survey on Machine Learning Techniques for Source Code Analysis** (2021), arxiv 2021, Sharma, Tushar, et al. [[pdf]](https://arxiv.org/pdf/2110.09610) - **Deep Learning & Software Engineering: State of Research and Future Directions** (2020), arxiv 2020, Devanbu, Prem, et al. [[pdf]](https://arxiv.org/pdf/2009.08525.pdf) - **A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research** (2020), arxiv 2020, Watson, Cody, et al. [[pdf]](https://arxiv.org/pdf/2009.06520.pdf) - **Machine Learning for Software Engineering: A Systematic Mapping** (2020), arxiv 2020, Shafiq, Saad, et al. [[pdf]](https://arxiv.org/pdf/2005.13299.pdf) @@ -588,6 +738,46 @@ Please feel free to send a pull request to add papers and relevant content that ## Misc +- **CodeScholar: Growing Idiomatic Code Examples** (2024), arxiv, Shetty, Manish et al. [[pdf]](https://arxiv.org/pdf/2312.15157) +- **DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models** (2024), arxiv, Pourreza, M., & Rafiei, D. [[pdf]](https://arxiv.org/pdf/2402.01117) +- **Calibration and Correctness of Language Models for Code** (2024), arxiv, Spiess et al. [[pdf]](https://arxiv.org/pdf/2402.02047.pdf) +- **Pix2Code: Learning to Compose Neural Visual Concepts as Programs** (2024), arxiv, Wüst, Antonia, et al. [[pdf]](https://arxiv.org/pdf/2402.08280) +- **Unsupervised Evaluation of Code LLMs with Round-Trip Correctness** (2024), arxiv, Allamanis, Miltiadis et al. [[pdf]](https://arxiv.org/pdf/2402.08699.pdf) +- **Can Large Language Models Write Parallel Code?** (2024), arxiv, Nichols, Daniel, et al. [[pdf]](https://arxiv.org/pdf/2401.12554) +- **OMPGPT: A Generative Pre-trained Transformer Model for OpenMP** (2024), arxiv, Chen, Le, et al. [[pdf]](https://arxiv.org/pdf/2401.16445) +- **CodeArt: Better Code Models by Attention Regularization When Symbols Are Lacking** (2024), arxiv, Su, Zian, et al. [[pdf]](https://arxiv.org/pdf/2402.11842) +- **ZS4C: Zero-Shot Synthesis of Compilable Code for Incomplete Code Snippets using ChatGPT** (2024), arxiv, Lin, Jiayi, et al. [[pdf]](https://arxiv.org/pdf/2402.12813) +- **Scaling Laws Behind Code Understanding Model** (2024), arxiv, Lin, Jiayi, et al. [[pdf]](https://arxiv.org/pdf/2402.12813) +- **Code Needs Comments: Enhancing Code LLMs with Comment Augmentation** (2024), arxiv, Song, Demin, et al. [[pdf]](https://arxiv.org/pdf/2402.13013) +- **LLM-CompDroid: Repairing Configuration Compatibility Bugs in Android Apps with Pre-trained Large Language Models** (2024), arxiv, Liu, Zhijie, et al. [[pdf]](https://arxiv.org/pdf/2402.15078) +- **NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctness** (2024), arxiv, Singhal, Manav, et al. [[pdf]](https://arxiv.org/pdf/2401.15963.pdf?trk=public_post_comment-text) +- **Importance Guided Data Augmentation for Neural-Based Code Understanding** (2024), arxiv, Dong, Zeming, et al. [[pdf]](https://arxiv.org/pdf/2402.15769) +- **CodeS: Towards Building Open-source Language Models for Text-to-SQL** (2024), arxiv, Li, Haoyang, et al. [[pdf]](https://arxiv.org/pdf/2402.16347) +- **If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents** (2024), arxiv, Yang, Ke, et al. [[pdf]](https://arxiv.org/pdf/2401.00812) +- **Experimenting a New Programming Practice with LLMs** (2024), arxiv, Zhang, Simiao, et al. [[pdf]](https://arxiv.org/pdf/2401.01062) +- **BinaryAI: Binary Software Composition Analysis via Intelligent Binary Source Code Matching** (2024), ICSE'24, Jiang, Ling, et al. [[pdf]](https://arxiv.org/pdf/2401.11161) +- **Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers** (2024), arxiv, Shi, Yuling, et al. [[pdf]](https://arxiv.org/pdf/2401.06461v1.pdf) +- **LILO: Learning Interpretable Libraries by Compressing and Documenting Code** (2024), ICLR'24, Grand, Gabriel, et al. [[pdf]](https://arxiv.org/pdf/2310.19791) +- **Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain** (2024), ICLR'24, Min, Marcus J., et al. [[pdf]](https://arxiv.org/pdf/2310.14053) +- **Large Language Models for Test-Free Fault Localization** (2024), ICSE'24, Yang, Aidan ZH, et al. [[pdf]](https://dl.acm.org/doi/pdf/10.1145/3597503.3623342) +- **A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Tasks** (2023), arxiv, Zou, Wentao, et al. [[pdf]](https://arxiv.org/pdf/2312.15614) +- **Lampr: Boosting the Effectiveness of Language-Generic Program Reduction via Large Language Models** (2023), arxiv, Zhang, Mengxiao, et al. [[pdf]](https://arxiv.org/pdf/2312.13064) +- **Evaluating and Enhancing the Robustness of Code Pre-trained Models through Structure-Aware Adversarial Samples Generation** (2023), EMNLP'23, Chen, Nuo, et al. [[pdf]](https://openreview.net/pdf?id=46WcPRhRwG) +- **Nova+: Generative Language Models for Binaries** (2023), arxiv, Jiang, Nan, et al. [[pdf]](https://arxiv.org/pdf/2311.13721) +- **Naturalness of Attention: Revisiting Attention in Code Language Models** (2023), arxiv, Saad, M., & Sharma, T. [[pdf]](https://arxiv.org/pdf/2311.13508) +- **Refactoring Programs Using Large Language Models with Few-Shot Examples** (2023), arxiv, Shirafuji, Atsushi, et al. [[pdf]](https://arxiv.org/pdf/2311.11690) +- **Learning Transfers over Several Programming Languages** (2023), arxiv, Baltaji, Razan, et al. [[pdf]](https://arxiv.org/pdf/2310.16937) +- **RefactorScore: Evaluating Refactor Prone Code** (2023), TSE, Jesse et al. +- **How Well Can Masked Language Models Spot Identifiers That Violate Naming Guidelines?** (2023), SCAM'23, Villmow, Johannes, et al. [[pdf]](https://www.alexandria.unisg.ch/bitstreams/3c56c6bc-18c5-4228-b782-1bb5e572a38f/download) +- **An Explanation Method for Models of Code** (2023), OOPSLA'23, Wang, Yu, et al. +- **Automated Bug Generation in the era of Large Language Models** (2023), arxiv, Ibrahimzada, A., et al. [[pdf]](https://arxiv.org/pdf/2310.02407) +- **Refining Decompiled C Code with Large Language Models** (2023), arxiv, Wong, Wai Kin, et al. [[pdf]](https://arxiv.org/pdf/2310.06530) +- **SUPERSONIC: Learning to Generate Source Code Optimizations in C/C++** (2023), arxiv, Chen, Z. et al. [[pdf]](https://arxiv.org/pdf/2309.14846) +- **Method-Level Bug Severity Prediction using Source Code Metrics and LLMs** (2023), ISSRE'23, Mashhadi, Ehsan, et al. [[pdf]](https://arxiv.org/pdf/2309.03044) +- **Frustrated with Code Quality Issues? LLMs can Help!** (2023), arxiv, Wadhwa, Nalin, et al. [[pdf]](https://arxiv.org/pdf/2309.12938) +- **Generating Variable Explanations via Zero-shot Prompt Learning** (2023), ASE'23, Wang, Chong, et al. [[pdf]](https://to-d.github.io/papers/ASE23_variable.pdf) +- **Large Language Models for Compiler Optimization** (2023), arxiv, Cummins, Chris, et al. [[pdf]](https://arxiv.org/pdf/2309.07062) +- **Merge Conflict Resolution: Classification or Generation?** (2023), ASE'23, Dong, Jinhao, et al. [[pdf]](https://raw.githubusercontent.com/DJjjjhao/ase-merge/master/Merge%20Conflict%20Resolution-%20Classification%20or%20Generation.pdf) - **EPICURE: Distilling Sequence Model Predictions into Patterns** (2023), arxiv, Allamanis, M., & Barr, E. T. [[pdf]](https://arxiv.org/pdf/2308.08203) - **FunProbe: Probing Functions from Binary Code through Probabilistic Analysis** (2023), FSE'23, Kim, Soomin, et al. [[pdf]](https://softsec.kaist.ac.kr/~sangkilc/papers/kim-fse23.pdf) - **CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models** (2023), FSE'23, Sun, Zhensu, et al. [[pdf]](https://arxiv.org/pdf/2308.14401) @@ -704,6 +894,7 @@ Please feel free to send a pull request to add papers and relevant content that # PhD Theses +- **Beyond Natural Language Processing: Advancing Software Engineering Tasks through Code Structure** (2024), Zishuo Ding, [[pdf]](https://uwspace.uwaterloo.ca/bitstream/handle/10012/20285/Ding_Zishuo.pdf?sequence=3) - **Analyzing and Securing Software via Robust and Generalizable Learning** (2023), Kexin Pei [[pdf]](https://academiccommons.columbia.edu/doi/10.7916/2ynz-v753) - **Deep Language Models for Software Testing and Optimisation** (2023), Foivos Tsimpourlas [[pdf]](https://era.ed.ac.uk/bitstream/handle/1842/40677/Tsimpourlas2023.pdf?sequence=1&isAllowed=y) - **Improving Programming Productivity with Statistical Models** (2022), Tam Nguyen [[pdf]](https://etd.auburn.edu/bitstream/handle/10415/8152/Dissertation_TamNguyen.pdf) @@ -721,6 +912,18 @@ Please feel free to send a pull request to add papers and relevant content that # Datasets +- [TACO](https://arxiv.org/pdf/2312.14852.pdf) - Topics in Algorithmic Code generation dataset +- [GitBug-Java](https://arxiv.org/pdf/2402.02961.pdf) - A Reproducible Benchmark of Recent Java Bugs +- [Archer](https://arxiv.org/pdf/2402.12554.pdf) - A Human-Labeled Text-to-SQL Dataset with Arithmetic, Commonsense and Hypothetical Reasoning +- [CodeLL](https://arxiv.org/pdf/2312.12492.pdf) - A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code +- [CRUXEval](https://arxiv.org/pdf/2401.03065.pdf) - A Benchmark for Code Reasoning, Understanding and Execution +- [CodeComplex](https://arxiv.org/pdf/2401.08719.pdf) - A Time-Complexity Dataset for Bilingual Source Codes +- [BugsPHP](https://arxiv.org/pdf/2401.07356.pdf) - A dataset for Automated Program Repair in PHP +- [GenCodeSearchNet](https://arxiv.org/pdf/2311.09707.pdf) - A Benchmark Test Suite for Evaluating Generalization in Programming Language Understanding +- [CrossCodeEval](https://proceedings.neurips.cc/paper_files/paper/2023/file/920f2dced7d32ab2ba2f1970bc306af6-Paper-Datasets_and_Benchmarks.pdf) - A Diverse and Multilingual Benchmark for Cross-File Code Completion +- [SWE-bench](https://arxiv.org/pdf/2310.06770) - An evaluation framework including software engineering problems drawn from real GitHub issues +- [CodeTransOcean](https://arxiv.org/pdf/2310.04951.pdf) - A Comprehensive Multilingual Benchmark for Code Translation +- [BioCoder](https://arxiv.org/pdf/2308.16458) - A benchmark for bioinformatics code generation with contextual pragmatic knowledge - [VulBench](https://github.com/Hustcw/VulBench) - A benchmark of vulnerability detection with annotations for each vulnerable function detailing the vulnerability type and its root cause - [StudentEval](https://arxiv.org/pdf/2306.04556.pdf) - A Benchmark of Student-Written Prompts for Large Language Models of Code @@ -808,6 +1011,7 @@ Source Code Learning Intelligence - **ACL**, the Association for Computational Linguistics - **OOPSLA**, the ACM Conference on Systems, Programming, Languages, and Applications +- **EMNLP**, the Conference on Empirical Methods in Natural Language Processing ## Journals - **TSE**, the IEEE Transactions on Software Engineering