Fetching the paper…
Reading the bibliography…
Executing code is essential for various program analysis tasks, e.g., to detect bugs that manifest through exceptions or to obtain execution traces for further dynamic analysis.
Executability of Python Snippets in Stack Overflow
Md. Monir Hossain, Nima Mahmoudi, Changyuan Lin, Hamzeh Khazaei, and Abram Hindle. 2019 · 1907
Earlier work this paper cites.
QuickCheck: a lightweight tool for random testing of Haskell programs. In ICFP . 268–279
Koen Claessen and John Hughes. 2000 · 2000
Earlier work this paper cites.
Static and dynamic analysis: Synergy and duality. In Workshop on Dynamic Analysis (WODA)
Michael D. Ernst. 2003 · 2003
Earlier work this paper cites.
Sequence Model Design for Code Completion in the Modern IDE
Gareth Ari Aye and Gail E. Kaiser. 2020 · 2004
Earlier work this paper cites.
JCrasher: an automatic robustness tester for Java
Christoph Csallner and Yannis Smaragdakis. 2004 · 2004
Earlier work this paper cites.
DART: directed automated random testing. In PLDI . ACM, 213–223
Patrice Godefroid, Nils Klarlund, and Koushik Sen. 2005 · 2005
Earlier work this paper cites.
CUTE: a concolic unit testing engine for C. In ESEC/FSE
Koushik Sen, Darko Marinov, and Gul Agha. 2005 · 2005
Earlier work this paper cites.
Dytan: a generic dynamic taint analysis framework. In ISSTA . ACM, 196–206
James A. Clause, Wanchun Li, and Alessandro Orso. 2007 · 2007
Earlier work this paper cites.
Feedback-Directed Random Test Generation. In ICSE
Carlos Pacheco, Shuvendu K. Lahiri, Michael D. Ernst, and Thomas Ball. 2007 · 2007
Earlier work this paper cites.
Combining Static and Dynamic Reasoning for Bug Detection. In TAP
Yannis Smaragdakis and Christoph Csallner. 2007 · 2007
Earlier work this paper cites.
KLEE: Unassisted and Automatic Generation of High-Coverage Tests for Complex Systems Programs. In OSDI . USENIX
Cristian Cadar, Daniel Dunbar, and Dawson R. Engler. 2008 · 2008
Earlier work this paper cites.
Enabling static analysis for partial java programs. In OOPSLA . 313–328
Barthélémy Dagenais and Laurie J. Hendren. 2008 · 2008
Earlier work this paper cites.
Javert: Fully Automatic Mining of General Temporal Properties from Dynamic Traces. In FSE . 339–349
Mark Gabel and Zhendong Su. 2008 · 2008
Earlier work this paper cites.
Automatic Generation of Object Usage Specifications from Large Method Traces. In ASE
Michael Pradel and Thomas R. Gross. 2009 · 2009
Earlier work this paper cites.
All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask). In IEEE S&P
Edward J. Schwartz, Thanassis Avgerinos, and David Brumley. 2010 · 2010
Earlier work this paper cites.
Dynamic inference of static types for Ruby.. In POPL . 459–472
Jong-hoon (David) An, Avik Chaudhuri, Jeffrey S. Foster, and Michael Hicks. 2011 · 2011
Earlier work this paper cites.
EvoSuite: automatic test suite generation for object-oriented software. In SIGSOFT/FSE’11 19th ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE-19) and ESEC’11 . 416–419
Gordon Fraser and Andrea Arcuri. 2011 · 2011
Earlier work this paper cites.
Automated API Property Inference Techniques
Martin P. Robillard et al. 2013 · 2012
Earlier work this paper cites.
The ruby type checker. In SAC
Brianna M. Ren, John Toman, T. Stephen Strickland, and Jeffrey S. Foster. 2013 · 2013
Earlier work this paper cites.
Jalangi: A Selective Record-Replay and Dynamic Analysis Framework for JavaScript. In ESEC/FSE
Koushik Sen, Swaroop Kalasapur, Tasneem Brutch, and Simon Gibbs. 2013 · 2013
Earlier work this paper cites.
American Fuzzy Lop (AFL)
Michal Zalewski. 2013 · 2013
Earlier work this paper cites.
Micro execution. In ICSE . 539–549
Patrice Godefroid. 2014 · 2014
Earlier work this paper cites.
Wojciech Zaremba and Ilya Sutskever. 2014 · 2014
Earlier work this paper cites.
Suggesting accurate method and class names. In ESEC/FSE . 38–49
Miltiadis Allamanis, Earl T. Barr, Christian Bird, and Charles A. Sutton. 2015 · 2015
Earlier work this paper cites.
Under-Constrained Symbolic Execution: Correctness Checking for Real Code. In USENIX
David A. Ramos and Dawson R. Engler. 2015 · 2015
Earlier work this paper cites.
Predicting Program Properties from "Big Code".. In POPL
Veselin Raychev, Martin T. Vechev, and Andreas Krause. 2015 · 2015
Earlier work this paper cites.
Convolutional Neural Networks over Tree Structures for Programming Language Processing. In AAAI
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. 2016 · 2016
Earlier work this paper cites.
Coverage-Based Greybox Fuzzing as Markov Chain
Marcel Böhme, Van-Thuan Pham, and Abhik Roychoudhury. 2019 · 2017
Earlier work this paper cites.
DeepFix: Fixing Common C Language Errors by Deep Learning. In AAAI
Rahul Gupta et al. 2017 · 2017
Earlier work this paper cites.
J-Force: Forced Execution on JavaScript. In WWW
Kyungtae Kim, I Luk Kim, Chung Hwan Kim, Yonghwi Kwon, Yunhui Zheng, Xiangyu Zhang, and Dongyan Xu. 2017 · 2017
Cited alongside, same era.
Learning to Represent Programs with Graphs. In ICLR
Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Deep code search. In ICSE , Michel Chaudron, Ivica Crnkovic, Marsha Chechik, and Mark Harman (Eds.). ACM, 933–944
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018 · 2018
Cited alongside, same era.
Deep learning type inference. In ESEC/FSE . 152–162
Vincent J. Hellendoorn, Christian Bird, Earl T. Barr, and Miltiadis Allamanis. 2018 · 2018
Cited alongside, same era.
Learning input tokens for effective fuzzing. In ISSTA
Björn Mathis, Rahul Gopinath, and Andreas Zeller. 2020 · 2020
Later among the works it cites.
TypeWriter: Neural Type Prediction with Search-based Validation. In ESEC/FSE
Michael Pradel, Georgios Gousios, Jason Liu, and Satish Chandra. 2020 · 2020
Later among the works it cites.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Later among the works it cites.
Blended, precise semantic program embeddings. In PLDI
Ke Wang and Zhendong Su. 2020 · 2020
Later among the works it cites.
Retrieval-based Neural Source Code Summarization. In ICSE
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, and Xudong Liu. 2020 · 2020
Later among the works it cites.
On Multi-Modal Learning of Editing Source Code. In ASE . IEEE, 443–455
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. In NDSS
Zhen Li, Shouhuai Xu Deqing Zou and, Xinyu Ou, Hai Jin, Sujuan Wang, Zhijun Deng, and Yuyi Zhong. 2018 · 2018
Cited alongside, same era.
Inference of static semantics for incomplete C programs
Leandro T. C. Melo, Rodrigo Geraldo Ribeiro, Marcus R. de Araújo, and Fernando Magno Quintão Pereira. 2018 · 2018
Cited alongside, same era.
DeepBugs: A learning approach to name-based bug detection
Michael Pradel and Koushik Sen. 2018 · 2018
Cited alongside, same era.
Retrieval on source code: a neural code search. In MAPL
Saksham Sachdev, Hongyu Li, Sifei Luan, Seohyun Kim, Koushik Sen, and Satish Chandra. 2018 · 2018
Cited alongside, same era.
Test Generation for Higher-Order Functions in Dynamic Languages. In OOPSLA
Marija Selakovic, Michael Pradel, Rezwana Karim Nawrin, and Frank Tip. 2018 · 2018
Cited alongside, same era.
code2vec: learning distributed representations of code
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019b · 2019
Cited alongside, same era.
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
Zimin Chen et al. 2019b · 2019
Cited alongside, same era.
Saikat Chakraborty and Baishakhi Ray. 2021 · 2021
Later among the works it cites.
Evaluating Large Language Models Trained on Code
Mark Chen et al. 2021c · 2021
Later among the works it cites.
Show Your Work: Scratchpads for Intermediate Computation with Language Models
Maxwell Nye et al. 2021d · 2021
Later among the works it cites.
LooPy: interactive program synthesis with control structures
Kasra Ferdowsifard, Shraddha Barke, Hila Peleg, Sorin Lerner, and Nadia Polikarpova. 2021 · 2021
Later among the works it cites.
Code Prediction by Feeding Trees to Transformers. In ICSE
Seohyun Kim, Jinman Zhao, Yuchi Tian, and Satish Chandra. 2021 · 2021
Later among the works it cites.
Retrieval-Augmented Generation for Code Summarization via Hybrid GNN. In ICLR . OpenReview.net
Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, and Yang Liu. 2021 · 2021
Later among the works it cites.
IdBench: Evaluating Semantic Representations of Identifier Names in Source Code. In ICSE
Yaza Wainakh, Moiz Rauf, and Michael Pradel. 2021 · 2021
Later among the works it cites.
Restoring Execution Environments of Jupyter Notebooks. In ICSE
Jiawei Wang, Li Li, and Andreas Zeller. 2021a · 2021
Later among the works it cites.
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In EMNLP
Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi. 2021b · 2021
Later among the works it cites.
Break-It-Fix-It: Unsupervised Learning for Program Repair. In ICML
Michihiro Yasunaga and Percy Liang. 2021 · 2021
Later among the works it cites.
Nessie: Automatically Testing JavaScript APIs with Asynchronous Callbacks. In ICSE
Ellen Arteca, Sebastian Harner, Michael Pradel, and Frank Tip. 2022 · 2022
Later among the works it cites.
Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code
Patrick Bareiß, Beatriz Souza, Marcelo d’Amorim, and Michael Pradel. 2022 · 2022
Later among the works it cites.
David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, and Daniel Tarlow. 2022 · 2022
Later among the works it cites.
VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning. In ICSE
Qibin Chen, Jeremy Lacomis, Edward J. Schwartz, Graham Neubig, Bogdan Vasilescu, and Claire Le Goues. 2022 · 2022
Later among the works it cites.
DynaPyt: A Dynamic Analysis Framework for Python. In ESEC/FSE . ACM
Aryaz Eghbali and Michael Pradel. 2022 · 2022
Later among the works it cites.
Type4Py: Practical deep similarity learning-based type inference for Python. In ICSE
Amir M Mir, Evaldas Latoškinas, Sebastian Proksch, and Georgios Gousios. 2022 · 2022
Later among the works it cites.
Nalin: Learning from Runtime Behavior to Find Name-Value Inconsistencies in Jupyter Notebooks. In ICSE
Jibesh Patra and Michael Pradel. 2022 · 2022
Later among the works it cites.
Static inference meets deep learning: a hybrid type inference approach for python. In ICSE
Yun Peng, Cuiyun Gao, Zongjie Li, Bowei Gao, David Lo, Qirun Zhang, and Michael Lyu. 2022 · 2022
Later among the works it cites.
Synchromesh: Reliable Code Generation from Pre-trained Language Models. In ICLR
Gabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani. 2022 · 2022
Later among the works it cites.
Neural software analysis
Michael Pradel and Satish Chandra. 2022 · 2022
Later among the works it cites.
Code Search based on Context-aware Code Translation. In ICSE
Weisong Sun, Chunrong Fang, Yuchen Chen, Guanhong Tao, Tingxu Han, and Quanjun Zhang. 2022 · 2022
Later among the works it cites.
A Systematic Evaluation of Large Language Models of Code
Frank F. Xu, Uri Alon, Graham Neubig, and Vincent J. Hellendoorn. 2022 · 2022
Later among the works it cites.
A Convolutional Attention Network for Extreme Summarization of Source Code. In ICML . 2091–2100
Miltiadis Allamanis, Hao Peng, and Charles A. Sutton. 2016 · 2091
Closest in time.