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Dataflow analysis is a fundamental code analysis technique that identifies dependencies between program values.
An efficient data dependence analysis for parallelizing compilers
Zhiyuan Li, Pen-Chung Yew, and Chuan-Qi Zhu · 1990
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Program analysis and specialization for the c programming language
Lars Ole Andersen · 1994
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Precise interprocedural dataflow analysis via graph reachability
Thomas W. Reps, Susan Horwitz, and Shmuel Sagiv · 1995
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Points-to analysis in almost linear time
Bjarne Steensgaard · 1996
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Soot - a java bytecode optimization framework
Raja Vallée-Rai, Phong Co, Etienne Gagnon, Laurie J. Hendren, Patrick Lam, and Vijay Sundaresan · 1999
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LLVM: A compilation framework for lifelong program analysis & transformation
Chris Lattner and Vikram S. Adve · 2004
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The Daikon system for dynamic detection of likely invariants
Michael D. Ernst, Jeff H. Perkins, Philip J. Guo, Stephen McCamant, Carlos Pacheco, Matthew S. Tschantz, and Chen Xiao · 2007
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KLEE: unassisted and automatic generation of high-coverage tests for complex systems programs
Cristian Cadar, Daniel Dunbar, and Dawson R. Engler · 2008
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Z3: an efficient SMT solver
Leonardo Mendonça de Moura and Nikolaj Bjørner · 2008
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Compositional shape analysis by means of bi-abduction
Cristiano Calcagno, Dino Distefano, Peter W. O’Hearn, and Hongseok Yang · 2009
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Juliet 1.1 C/C++ and java test suite
Tim Boland and Paul E. Black · 2012
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Static program analysis
Anders Møller and Michael I Schwartzbach · 2012
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Flowdroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps
Steven Arzt, Siegfried Rasthofer, Christian Fritz, Eric Bodden, Alexandre Bartel, Jacques Klein, Yves Le Traon, Damien Octeau, and Patrick D. McDaniel · 2014
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A machine-learning approach for classifying and categorizing android sources and sinks
Siegfried Rasthofer, Steven Arzt, and Eric Bodden · 2014
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What developers want and need from program analysis: an empirical study
Maria Christakis and Christian Bird · 2016
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SVF: interprocedural static value-flow analysis in LLVM
Yulei Sui and Jingling Xue · 2016
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Tree-sitter-a new parsing system for programming tools
Max Brunsfeld · 2018
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Pinpoint: fast and precise sparse value flow analysis for million lines of code
Qingkai Shi, Xiao Xiao, Rongxin Wu, Jinguo Zhou, Gang Fan, and Charles Zhang · 2018
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Learning loop invariants for program verification
Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, and Le Song · 2018
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Unsupervised learning of API alias specifications
Jan Eberhardt, Samuel Steffen, Veselin Raychev, and Martin T. Vechev · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Hoppity: Learning graph transformations to detect and fix bugs in programs
Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, and Ke Wang · 2020
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Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi · 2021
Can chatgpt support software verification?
Christian Janßen, Cedric Richter, and Heike Wehrheim · 2023
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Towards mitigating LLM hallucination via self reflection
Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, and Pascale Fung · 2023
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Swe-bench: Can language models resolve real-world github issues?
Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan · 2023
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Finding inductive loop invariants using large language models
Adharsh Kamath, Aditya Senthilnathan, Saikat Chakraborty, Pantazis Deligiannis, Shuvendu K. Lahiri, Akash Lal, Aseem Rastogi, Subhajit Roy, and Rahul Sharma · 2023
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The hitchhiker’s guide to program analysis: A journey with large language models
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Program analysis via efficient symbolic abstraction
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Optimal neural program synthesis from multimodal specifications
Xi Ye, Qiaochu Chen, Isil Dillig, and Greg Durrett · 2021
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Vulberta: Simplified source code pre-training for vulnerability detection
Hazim Hanif and Sergio Maffeis · 2022
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Linevd: Statement-level vulnerability detection using graph neural networks
David Hin, Andrey Kan, Huaming Chen, and Muhammad Ali Babar · 2022
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Taintbench: Automatic real-world malware benchmarking of android taint analyses
Linghui Luo, Felix Pauck, Goran Piskachev, Manuel Benz, Ivan Pashchenko, Martin Mory, Eric Bodden, Ben Hermann, and Fabio Massacci · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou · 2022
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Ranking llm-generated loop invariants for program verification
Saikat Chakraborty, Shuvendu K. Lahiri, Sarah Fakhoury, Akash Lal, Madanlal Musuvathi, Aseem Rastogi, Aditya Senthilnathan, Rahul Sharma, and Nikhil Swamy · 2023
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Haonan Li, Yu Hao, Yizhuo Zhai, and Zhiyun Qian · 2023
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Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation
Niels Mündler, Jingxuan He, Slobodan Jenko, and Martin T. Vechev · 2023
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OpenAI · 2023
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Can large language models reason about program invariants?
Kexin Pei, David Bieber, Kensen Shi, Charles Sutton, and Pengcheng Yin · 2023
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Code llama: Open foundation models for code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton-Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve · 2023
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GitHub Code Scanning
Semmle · 2023
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Repository-level prompt generation for large language models of code
Disha Shrivastava, Hugo Larochelle, and Daniel Tarlow · 2023
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Generating variable explanations via zero-shot prompt learning
Chong Wang, Yiling Lou, Junwei Liu, and Xin Peng · 2023
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Copiloting the copilots: Fusing large language models with completion engines for automated program repair
Yuxiang Wei, Chunqiu Steven Xia, and Lingming Zhang · 2023
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Keep the conversation going: Fixing 162 out of 337 bugs for $0.42 each using chatgpt
Chunqiu Steven Xia and Lingming Zhang · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Repocoder: Repository-level code completion through iterative retrieval and generation
Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, and Weizhu Chen · 2023
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Dataflow analysis-inspired deep learning for efficient vulnerability detection
Benjamin Steenhoek, Hongyang Gao, and Wei Le · 2024
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Codefuse-query: A data-centric static code analysis system for large-scale organizations
Xiaoheng Xie, Gang Fan, Xiaojun Lin, Ang Zhou, Shijie Li, Xunjin Zheng, Yinan Liang, Yu Zhang, Na Yu, Haokun Li, Xinyu Chen, Yingzhuang Chen, Yi Zhen, Dejun Dong, Xianjin Fu, Jinzhou Su, Fuxiong Pan, Pengshuai Luo, Youzheng Feng, Ruoxiang Hu, Jing Fan, Jinguo Zhou, Xiao Xiao, and Peng Di · 2024
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