Fetching the paper…
Reading the bibliography…
Large language models have become increasingly effective in software engineering tasks such as code generation, debugging and repair.
Clarke, E.M., Kroening, D., Lerda, F.: A tool for checking ANSI-C programs. In: Jensen, K., Podelski, A. (eds.) Tools and Algorithms for the Construction and Analysis of Systems, 10th International Conference, TACAS 2004. Lecture Notes in Computer Science, vol. 2988, pp. 168–176. Springer (2004). https://doi.org/10.1007/978-3-540-24730-2_15, https://doi.org/10.1007/978-3-540-24730-2_15
2004
Earlier work this paper cites.
Ahrendt, W., Baar, T., Beckert, B., Bubel, R., Giese, M., Hähnle, R., Menzel, W., Mostowski, W., Roth, A., Schlager, S., Schmitt, P.H.: The KeY tool. Softw. Syst. Model. 4
2005
Earlier work this paper cites.
Ernst, M.D., Perkins, J.H., Guo, P.J., McCamant, S., Pacheco, C., Tschantz, M.S., Xiao, C.: The daikon system for dynamic detection of likely invariants. Sci. Comput. Program. 69
2007
Earlier work this paper cites.
Leino, K.R.M.: Dafny: An automatic program verifier for functional correctness. In: Clarke, E.M., Voronkov, A. (eds.) Logic for Programming, Artificial Intelligence, and Reasoning - 16th International Conference, LPAR-16, Dakar, Senegal, April 25-May 1, 2010, Revised Selected Papers. Lecture Notes in Computer Science, vol. 6355, pp. 348–370. Springer (2010). https://doi.org/10.1007/978-3-642-17511-4_20, https://doi.org/10.1007/978-3-642-17511-4_20
2010
Earlier work this paper cites.
Beyer, D., Keremoglu, M.E.: CPAchecker: A tool for configurable software verification. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV. Lecture Notes in Computer Science, vol. 6806, pp. 184–190. Springer (2011). https://doi.org/10.1007/978-3-642-22110-1_16, https://doi.org/10.1007/978-3-642-22110-1_16
2011
Earlier work this paper cites.
Jacobs, B., Smans, J., Philippaerts, P., Vogels, F., Penninckx, W., Piessens, F.: Verifast: A powerful, sound, predictable, fast verifier for C and java. In: Bobaru, M.G., Havelund, K., Holzmann, G.J., Joshi, R. (eds.) NASA Formal Methods - Third International Symposium, NFM 2011, Pasadena, CA, USA, April 18-20, 2011. Proceedings. Lecture Notes in Computer Science, vol. 6617, pp. 41–55. Springer (2011). https://doi.org/10.1007/978-3-642-20398-5_4, https://doi.org/10.1007/978-3-642-20398-5_4
2011
Earlier work this paper cites.
Heizmann, M., Hoenicke, J., Podelski, A.: Software model checking for people who love automata. In: Sharygina, N., Veith, H. (eds.) Computer Aided Verification - 25th International Conference, CAV 2013, Saint Petersburg, Russia, July 13-19, 2013. Proceedings. Lecture Notes in Computer Science, vol. 8044, pp. 36–52. Springer (2013). https://doi.org/10.1007/978-3-642-39799-8_2, https://doi.org/10.1007/978-3-642-39799-8_2
2013
Earlier work this paper cites.
Ernst, G., Pfähler, J., Schellhorn, G., Haneberg, D., Reif, W.: KIV: overview and verifythis competition. Int. J. Softw. Tools Technol. Transf. 17
2015
Earlier work this paper cites.
Beyer, D.: Reliable and reproducible competition results with benchexec and witnesses (report on SV-COMP 2016). In: Chechik, M., Raskin, J. (eds.) Tools and Algorithms for the Construction and Analysis of Systems - 22nd International Conference, TACAS 2016. Lecture Notes in Computer Science, vol. 9636, pp. 887–904. Springer (2016). https://doi.org/10.1007/978-3-662-49674-9_55, https://doi.org/10.1007/978-3-662-49674-9_55
2016
Earlier work this paper cites.
Garg, P., Neider, D., Madhusudan, P., Roth, D.: Learning invariants using decision trees and implication counterexamples. In: Bodík, R., Majumdar, R. (eds.) Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 2016, St. Petersburg, FL, USA, January 20 - 22, 2016. pp. 499–512. ACM (2016). https://doi.org/10.1145/2837614.2837664, https://doi.org/10.1145/2837614.2837664
2016
Earlier work this paper cites.
Padhi, S., Sharma, R., Millstein, T.D.: Data-driven precondition inference with learned features. In: Krintz, C., Berger, E.D. (eds.) Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2016, Santa Barbara, CA, USA, June 13-17, 2016. pp. 42–56. ACM (2016). https://doi.org/10.1145/2908080.2908099, https://doi.org/10.1145/2908080.2908099
2016
Earlier work this paper cites.
Si, X., Dai, H., Raghothaman, M., Naik, M., Song, L.: Learning loop invariants for program verification. In: Bengio, S., Wallach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada. pp. 7762–7773 (2018), https://proceedings.neurips.cc/paper/2018/hash/65b1e92c585fd4c2159d5f33b5030ff2-Abstract.html
2018
Earlier work this paper cites.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual (2020), https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
2020
Cited alongside, same era.
Chalupa, M., Strejcek, J., Vitovská, M.: Joint forces for memory safety checking revisited. Int. J. Softw. Tools Technol. Transf. 22
2020
Cited alongside, same era.
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., Chi, E.H., Hashimoto, T., Vinyals, O., Liang, P., Dean, J., Fedus, W.: Emergent abilities of large language models. Trans. Mach. Learn. Res. 2022
2022
Later among the works it cites.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E.H., Le, Q.V., Zhou, D.: Chain-of-thought prompting elicits reasoning in large language models. In: NeurIPS (2022), http://papers.nips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html
2022
Later among the works it cites.
Beyer, D.: Competition on software verification and witness validation: SV-COMP 2023. In: Sankaranarayanan, S., Sharygina, N. (eds.) TACAS. Lecture Notes in Computer Science, vol. 13994, pp. 495–522. Springer (2023). https://doi.org/10.1007/978-3-031-30820-8_29, https://doi.org/10.1007/978-3-031-30820-8_29
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
Baudin, P., Bobot, F., Bühler, D., Correnson, L., Kirchner, F., Kosmatov, N., Maroneze, A., Perrelle, V., Prevosto, V., Signoles, J., Williams, N.: The dogged pursuit of bug-free C programs: the Frama-C software analysis platform. Commun. ACM 64
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Ahrendt, W., Gurov, D., Johansson, M., Rümmer, P.: Trico - triple co-piloting of implementation, specification and tests. In: Margaria, T., Steffen, B. (eds.) Leveraging Applications of Formal Methods, Verification and Validation. Verification Principles - 11th International Symposium, ISoLA 2022, Rhodes, Greece, October 22-30, 2022, Proceedings, Part I. Lecture Notes in Computer Science, vol. 13701, pp. 174–187. Springer (2022). https://doi.org/10.1007/978-3-031-19849-6_11, https://doi.org/10.1007/978-3-031-19849-6_11
2022
Cited alongside, same era.
Alon, Y., David, C.: Using graph neural networks for program termination. In: Roychoudhury, A., Cadar, C., Kim, M. (eds.) Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2022, Singapore, Singapore, November 14-18, 2022. pp. 910–921. ACM (2022). https://doi.org/10.1145/3540250.3549095, https://doi.org/10.1145/3540250.3549095
2022
Cited alongside, same era.
Beyer, D., Spiessl, M.: The static analyzer Frama-C in SV-COMP (competition contribution). In: Fisman, D., Rosu, G. (eds.) Tools and Algorithms for the Construction and Analysis of Systems - 28th International Conference, TACAS 2022. Lecture Notes in Computer Science, vol. 13244, pp. 429–434. Springer (2022). https://doi.org/10.1007/978-3-030-99527-0_26, https://doi.org/10.1007/978-3-030-99527-0_26
2022
Cited alongside, same era.
Beyer, D., Spiessl, M., Umbricht, S.: Cooperation between automatic and interactive software verifiers. In: Schlingloff, B., Chai, M. (eds.) Software Engineering and Formal Methods - 20th International Conference, SEFM 2022. Lecture Notes in Computer Science, vol. 13550, pp. 111–128. Springer (2022). https://doi.org/10.1007/978-3-031-17108-6_7, https://doi.org/10.1007/978-3-031-17108-6_7
2022
Cited alongside, same era.
Giacobbe, M., Kroening, D., Parsert, J.: Neural termination analysis. In: Roychoudhury, A., Cadar, C., Kim, M. (eds.) Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2022, Singapore, Singapore, November 14-18, 2022. pp. 633–645. ACM (2022). https://doi.org/10.1145/3540250.3549120, https://doi.org/10.1145/3540250.3549120
2022
Cited alongside, same era.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback. In: NeurIPS (2022), http://papers.nips.cc/paper_files/paper/2022/hash/b1efde53be364a73914f58805a001731-Abstract-Conference.html
2022
Cited alongside, same era.
Baudin, P., Filliâtre, J.C., Marché, C., Monate, B., Moy, Y., Prevosto, V.: ACSL: ANSI/ISO C Specification Language, http://frama-c.com/download/acsl.pdf
Cited in the paper.
Chen, B., Zhang, F., Nguyen, A., Zan, D., Lin, Z., Lou, J., Chen, W.: Codet: Code generation with generated tests. In: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net (2023), https://openreview.net/pdf?id=ktrw68Cmu9c
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, S., Zettlemoyer, L., Lewis, M.: Incoder: A generative model for code infilling and synthesis. In: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net (2023), https://openreview.net/pdf?id=hQwb-lbM6EL
2023
Closest in time.
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Madotto, A., Fung, P.: Survey of hallucination in natural language generation. ACM Comput. Surv. 55
2023
Closest in time.
Jiang, N., Liu, K., Lutellier, T., Tan, L.: Impact of code language models on automated program repair. In: 45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20, 2023. pp. 1430–1442. IEEE (2023). https://doi.org/10.1109/ICSE48619.2023.00125, https://doi.org/10.1109/ICSE48619.2023.00125
2023
Closest in time.
Pei, K., Bieber, D., Shi, K., Sutton, C., Yin, P.: Can large language models reason about program invariants? In: Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., Scarlett, J. (eds.) ICML. Proceedings of Machine Learning Research, vol. 202, pp. 27496–27520. PMLR (2023), https://proceedings.mlr.press/v202/pei23a.html
2023
Closest in time.