Fetching the paper…
Reading the bibliography…
We introduce SkipAnalyzer, a large language model (LLM)-powered tool for static code analysis.
D. Hovemeyer and W. Pugh, “Finding bugs is easy,” SIGPLAN Not. , vol. 39, no. 12, p. 92–106, dec 2004. [Online]. Available: https://doi.org/10.1145/1052883.1052895
2004
Earlier work this paper cites.
B. Cole, D. Hakim, D. Hovemeyer, R. Lazarus, W. Pugh, and K. Stephens, “Improving your software using static analysis to find bugs,” in Companion to the 21st ACM SIGPLAN Symposium on Object-Oriented Programming Systems, Languages, and Applications , ser. OOPSLA ’06. New York, NY, USA: Association for Computing Machinery, 2006, p. 673–674. [Online]. Available: https://doi.org/10.1145/1176617.1176667
2006
Earlier work this paper cites.
A. Arcuri, “On the automation of fixing software bugs,” in Companion of the 30th International Conference on Software Engineering , ser. ICSE Companion ’08. New York, NY, USA: Association for Computing Machinery, 2008, p. 1003–1006. [Online]. Available: https://doi.org/10.1145/1370175.1370223
2008
Earlier work this paper cites.
W. Weimer, T. Nguyen, C. Le Goues, and S. Forrest, “Automatically finding patches using genetic programming,” in 2009 IEEE 31st International Conference on Software Engineering . IEEE, 2009, pp. 364–374
2009
Earlier work this paper cites.
H. Shen, J. Fang, and J. Zhao, “Efindbugs: Effective error ranking for findbugs,” in 2011 Fourth IEEE International Conference on Software Testing, Verification and Validation , 2011, pp. 299–308
2011
Earlier work this paper cites.
S. Heckman and L. Williams, “A systematic literature review of actionable alert identification techniques for automated static code analysis,” Information and Software Technology , vol. 53, no. 4, pp. 363–387, 2011
2011
Earlier work this paper cites.
M. Junker, R. Huuck, A. Fehnker, and A. Knapp, “Smt-based false positive elimination in static program analysis,” in Formal Methods and Software Engineering: 14th International Conference on Formal Engineering Methods, ICFEM 2012, Kyoto, Japan, November 12-16, 2012. Proceedings 14 . Springer, 2012, pp. 316–331
2012
Earlier work this paper cites.
J. Berdine, A. Cox, S. Ishtiaq, and C. M. Wintersteiger, “Diagnosing abstraction failure for separation logic–based analyses,” in Computer Aided Verification , P. Madhusudan and S. A. Seshia, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 155–173
2012
Earlier work this paper cites.
B. Johnson, Y. Song, E. Murphy-Hill, and R. Bowdidge, “Why don’t software developers use static analysis tools to find bugs?” in 2013 35th International Conference on Software Engineering (ICSE) , 2013, pp. 672–681
2013
Earlier work this paper cites.
Q. Hanam, L. Tan, R. Holmes, and P. Lam, “Finding patterns in static analysis alerts: Improving actionable alert ranking,” in Proceedings of the 11th Working Conference on Mining Software Repositories , ser. MSR 2014. New York, NY, USA: Association for Computing Machinery, 2014, p. 152–161. [Online]. Available: https://doi.org/10.1145/2597073.2597100
2014
Earlier work this paper cites.
M. Christakis and C. Bird, “What developers want and need from program analysis: an empirical study,” in Proceedings of the 31st IEEE/ACM international conference on automated software engineering , 2016, pp. 332–343
2016
Earlier work this paper cites.
L. Li, T. F. Bissyandé, M. Papadakis, S. Rasthofer, A. Bartel, D. Octeau, J. Klein, and L. Traon, “Static analysis of android apps: A systematic literature review,” Information and Software Technology , vol. 88, pp. 67–95, 2017
2017
Earlier work this paper cites.
Z. P. Reynolds, A. B. Jayanth, U. Koc, A. A. Porter, R. R. Raje, and J. H. Hill, “Identifying and documenting false positive patterns generated by static code analysis tools,” in 2017 IEEE/ACM 4th International Workshop on Software Engineering Research and Industrial Practice (SER&IP) , 2017, pp. 55–61
2017
Earlier work this paper cites.
R. van Tonder and C. L. Goues, “Static automated program repair for heap properties,” in Proceedings of the 40th International Conference on Software Engineering , 2018, pp. 151–162
2018
Earlier work this paper cites.
L. Gazzola, D. Micucci, and L. Mariani, “Automatic software repair: A survey,” in Proceedings of the 40th International Conference on Software Engineering , 2018, pp. 1219–1219
2018
Earlier work this paper cites.
D. A. Tomassi, “Bugs in the wild: Examining the effectiveness of static analyzers at finding real-world bugs,” in Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , ser. ESEC/FSE 2018. New York, NY, USA: Association for Computing Machinery, 2018, p. 980–982
2018
Earlier work this paper cites.
J. Wang, S. Wang, and Q. Wang, “Is there a ”golden” feature set for static warning identification? an experimental evaluation,” in Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement , ser. ESEM ’18. New York, NY, USA: Association for Computing Machinery, 2018
2018
Earlier work this paper cites.
R. Bavishi, H. Yoshida, and M. R. Prasad, “Phoenix: Automated data-driven synthesis of repairs for static analysis violations,” in Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2019, pp. 613–624
2019
Earlier work this paper cites.
K. Liu, A. Koyuncu, D. Kim, and T. F. Bissyandè, “Avatar: Fixing semantic bugs with fix patterns of static analysis violations,” in 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER) , 2019, pp. 1–12
2019
Earlier work this paper cites.
D. Marcilio, R. Bonifácio, E. Monteiro, E. Canedo, W. Luz, and G. Pinto, “Are static analysis violations really fixed? a closer look at realistic usage of sonarqube,” in 2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) , 2019, pp. 209–219
2019
Earlier work this paper cites.
Q. Ashfaq, R. Khan, and S. Farooq, “A comparative analysis of static code analysis tools that check java code adherence to java coding standards,” in 2019 2nd International Conference on Communication, Computing and Digital systems (C-CODE) . IEEE, 2019, pp. 98–103
2019
Earlier work this paper cites.
U. Koc, S. Wei, J. S. Foster, M. Carpuat, and A. A. Porter, “An empirical assessment of machine learning approaches for triaging reports of a java static analysis tool,” in 2019 12th ieee conference on software testing, validation and verification (icst) . IEEE, 2019, pp. 288–299
2019
Earlier work this paper cites.
A. Carvalho, W. Luz, D. Marcílio, R. Bonifácio, G. Pinto, and E. Dias Canedo, “C-3pr: A bot for fixing static analysis violations via pull requests,” in 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER) , 2020, pp. 161–171
2020
Earlier work this paper cites.
Y. Pan, X. Ge, C. Fang, and Y. Fan, “A systematic literature review of android malware detection using static analysis,” IEEE Access , vol. 8, pp. 116 363–116 379, 2020
2020
Earlier work this paper cites.
C. Vassallo, S. Panichella, F. Palomba, S. Proksch, H. C. Gall, and A. Zaidman, “How developers engage with static analysis tools in different contexts,” Empirical Software Engineering , vol. 25, pp. 1419–1457, 2020
2020
Earlier work this paper cites.
T. Muske and A. Serebrenik, “Techniques for efficient automated elimination of false positives,” in 2020 IEEE 20th International Working Conference on Source Code Analysis and Manipulation (SCAM) , 2020, pp. 259–263
2020
Cited alongside, same era.
J. Fan, Y. Li, S. Wang, and T. N. Nguyen, “A c/c++ code vulnerability dataset with code changes and cve summaries,” in Proceedings of the 17th International Conference on Mining Software Repositories , ser. MSR ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 508–512
2020
Cited alongside, same era.
T.-T. Wong and P.-Y. Yeh, “Reliable accuracy estimates from k-fold cross validation,” IEEE Transactions on Knowledge and Data Engineering , vol. 32, no. 8, pp. 1586–1594, 2020
2020
Cited alongside, same era.
D. A. Tomassi and C. Rubio-González, “On the real-world effectiveness of static bug detectors at finding null pointer exceptions,” in 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) , 2021, pp. 292–303
2023
Closest in time.
C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2998–3009
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
X. Yang, J. Chen, R. Yedida, Z. Yu, and T. Menzies, “Learning to recognize actionable static code warnings (is intrinsically easy),” Empirical Softw. Engg. , vol. 26, no. 3, may 2021. [Online]. Available: https://doi.org/10.1007/s10664-021-09948-6
2021
Cited alongside, same era.
A. Nilizadeh, G. T. Leavens, X.-B. D. Le, C. S. Păsăreanu, and D. R. Cok, “Exploring true test overfitting in dynamic automated program repair using formal methods,” in 2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST) , 2021, pp. 229–240
2021
Cited alongside, same era.
Y. Wang, W. Wang, S. Joty, and S. C. Hoi, “CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 8696–8708
2021
Cited alongside, same era.
G. Bhandari, A. Naseer, and L. Moonen, “Cvefixes: Automated collection of vulnerabilities and their fixes from open-source software,” in Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering , ser. PROMISE 2021. New York, NY, USA: Association for Computing Machinery, 2021, p. 30–39
2021
Cited alongside, same era.
2021
Cited alongside, same era.
E. Mashhadi and H. Hemmati, “Applying codebert for automated program repair of java simple bugs,” in 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) , 2021, pp. 505–509
2021
Cited alongside, same era.
S. Lipp, S. Banescu, and A. Pretschner, “An empirical study on the effectiveness of static c code analyzers for vulnerability detection,” in Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis , ser. ISSTA 2022. New York, NY, USA: Association for Computing Machinery, 2022, p. 544–555. [Online]. Available: https://doi.org/10.1145/3533767.3534380
2022
Cited alongside, same era.
A. Kharkar, R. Z. Moghaddam, M. Jin, X. Liu, X. Shi, C. Clement, and N. Sundaresan, “Learning to reduce false positives in analytic bug detectors,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 1307–1316
2022
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
Google. (2023) Errorprone. Accessed on Date. [Online]. Available: https://errorprone.info/index
2023
Closest in time.
OpenAI. (2023) Chatgpt. Accessed on Date. [Online]. Available: https://openai.com/blog/chatgpt
2023
Closest in time.
2023
Closest in time.
OpenAI. (2023) Chatgpt-3.5. Accessed on Date. [Online]. Available: https://platform.openai.com/docs/models/gpt-3-5
2023
Closest in time.
2023
Closest in time.
S. Kang, J. Yoon, and S. Yoo, “Large language models are few-shot testers: Exploring llm-based general bug reproduction,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2312–2323
2023
Closest in time.
D. Zan, B. Chen, F. Zhang, D. Lu, B. Wu, B. Guan, W. Yongji, and J.-G. Lou, “Large language models meet NL2Code: A survey,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 7443–7464
2023
Closest in time.
B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz, and D. Roth, “Recent advances in natural language processing via large pre-trained language models: A survey,” ACM Comput. Surv. , vol. 56, no. 2, sep 2023
2023
Closest in time.
J. Wei and et al., “Chain-of-thought prompting elicits reasoning in large language models,” arXiv , 2022, accessed 19 Oct. 2023
2023
Closest in time.
H. Joshi, J. Cambronero Sanchez, S. Gulwani, V. Le, G. Verbruggen, and I. Radiček, “Repair is nearly generation: Multilingual program repair with llms,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 4, pp. 5131–5140, Jun. 2023
2023
Closest in time.
2023
Closest in time.
Z. Fan, X. Gao, M. Mirchev, A. Roychoudhury, and S. H. Tan, “Automated repair of programs from large language models,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) , 2023, pp. 1469–1481
2023
Closest in time.
F. Ribeiro, “Large language models for automated program repair,” in Companion Proceedings of the 2023 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity , ser. SPLASH 2023. New York, NY, USA: Association for Computing Machinery, 2023, p. 7–9
2023
Closest in time.
N. Jiang, K. Liu, T. Lutellier, and L. Tan, “Impact of code language models on automated program repair,” in Proceedings of the 45th International Conference on Software Engineering , ser. ICSE ’23. IEEE Press, 2023, p. 1430–1442
2023
Closest in time.
C. Lemieux, J. P. Inala, S. K. Lahiri, and S. Sen, “Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models,” in Proceedings of the 45th International Conference on Software Engineering , ser. ICSE ’23. IEEE Press, 2023, p. 919–931. [Online]. Available: https://doi.org/10.1109/ICSE48619.2023.00085
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.