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Loop invariants are fundamental to reasoning about programs with loops.
An axiomatic basis for computer programming
Hoare, C. A. R · 1969
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Cousot, P., and Cousot, R · 1977
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Automatic predicate abstraction of C programs
Ball, T., Majumdar, R., Millstein, T. D., and Rajamani, S. K · 2001
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Houdini, an annotation assistant for esc/java
Flanagan, C., and Leino, K. R. M · 2001
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A static analyzer for large safety-critical software
Blanchet, B., Cousot, P., Cousot, R., Feret, J., Mauborgne, L., Miné, A., Monniaux, D., and Rival, X · 2003
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Counterexample-guided abstraction refinement for symbolic model checking
Clarke, E. M., Grumberg, O., Jha, S., Lu, Y., and Veith, H · 2003
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McMillan, K. L · 2003
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Syntax-guided synthesis
Alur, R., Bodik, R., Juniwal, G., Martin, M. M., Raghothaman, M., Seshia, S. A., Singh, R., Solar-Lezama, A., Torlak, E., and Udupa, A · 2013
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Frama-c: A software analysis perspective
Kirchner, F., Kosmatov, N., Prevosto, V., Signoles, J., and Yakobowski, B · 2015
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Accelerating invariant generation
Madhukar, K., Wachter, B., Kroening, D., Lewis, M., and Srivas, M · 2015
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The Satisfiability Modulo Theories Library (SMT-LIB)
Barrett, C., Fontaine, P., and Tinelli, C · 2016
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Learning invariants using decision trees and implication counterexamples
Garg, P., Neider, D., Madhusudan, P., and Roth, D · 2016
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Data-driven precondition inference with learned features
Padhi, S., Sharma, R., and Millstein, T. D · 2016
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Learning shape analysis
Brockschmidt, M., Chen, Y., Kohli, P., Krishna, S., and Tarlow, D · 2017
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Alt-Ergo 2.2
Conchon, S., Coquereau, A., Iguernlala, M., and Mebsout, A · 2018
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Learning nonlinear loop invariants with gated continuous logic networks
Yao, J., Ryan, G., Wong, J., Jana, S., and Gu, R · 2020
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Evaluating large language models trained on code, 2021
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
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Github copilot
GitHub · 2022
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Amazon codewhisperer
Amazon · 2023
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Competition on software verification and witness validation: Sv-comp 2023
Beyer, D · 2023
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Ultimate automizer and the search for perfect interpolants
Heizmann, M., Chen, Y.-F., Dietsch, D., Greitschus, M., Hoenicke, J., Li, Y., Nutz, A., Musa, B., Schilling, C., Schindler, T., and Podelski, A · 2018
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Learning loop invariants for program verification
Si, X., Dai, H., Raghothaman, M., Naik, M., and Song, L · 2018
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Zhu, H., Magill, S., and Jagannathan, S · 2018
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Cln2inv: Learning loop invariants with continuous logic networks
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Code2inv: A deep learning framework for program verification
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E-ACSL User Manual
Signoles, J., Desloges, B., and Vorobyov, K
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Ranking llm-generated loop invariants for program verification
Chakraborty, S., Lahiri, S. K., Fakhoury, S., Musuvathi, M., Lal, A., Rastogi, A., Senthilnathan, A., Sharma, R., and Swamy, N · 2023
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GPT-4 technical report
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Can large language models reason about program invariants?
Pei, K., Bieber, D., Shi, K., Sutton, C., and Yin, P · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., and et al · 2023
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