Software and Computational Systems Lab

Publications of Marek Jankola

1. Dirk Beyer, Po-Chun Chien, Marek Jankola, and Nian-Ze Lee. A Transferability Study of Interpolation-Based Hardware Model Checking to Software Verification. Proc. ACM Softw. Eng.(FSE), 2024. Keyword(s): CPAchecker, Software Model Checking Funding: DFG-CONVEY
   BibTeX Entry

   @article{ItpTransfer-PACMSE, author = {Dirk Beyer and Po-Chun Chien and Marek Jankola and Nian-Ze Lee}, title = {A Transferability Study of Interpolation-Based Hardware Model Checking to Software Verification}, journal = {Proc. ACM Softw. Eng.}, volume = {}, number = {FSE}, pages = {}, year = {2024}, doi = {}, url = {}, pdf = {}, presentation = {}, abstract = {}, keyword = {CPAchecker,Software Model Checking}, artifact = {}, funding = {DFG-CONVEY}, }

1. Daniel Baier, Dirk Beyer, Po-Chun Chien, Marek Jankola, Matthias Kettl, Nian-Ze Lee, Thomas Lemberger, Marian Lingsch-Rosenfeld, Martin Spiessl, Henrik Wachowitz, and Philipp Wendler. CPAchecker 2.3 with Strategy Selection (Competition Contribution). In Proc. TACAS (3), LNCS 14572, pages 359-364, 2024. Springer. doi:10.1007/978-3-031-57256-2_21 Keyword(s): Software Model Checking, Witness-Based Validation, CPAchecker Funding: DFG-CONVEY, DFG-IDEFIX Publisher's Version PDF Supplement
   Artifact(s)

   1. doi:10.5281/zenodo.10203297
   Abstract

   CPAchecker is a versatile framework for software verification, rooted in the established concept of configurable program analysis. Compared to the last published system description at SV-COMP 2015, the CPAchecker submission to SV-COMP 2024 incorporates new analyses for reachability safety, memory safety, termination, overflows, and data races. To combine forces of the available analyses in CPAchecker and cover the full spectrum of the diverse program characteristics and specifications in the competition, we use strategy selection to predict a sequential portfolio of analyses that is suitable for a given verification task. The prediction is guided by a set of carefully picked program features. The sequential portfolios are composed based on expert knowledge and consist of bit-precise analyses using k-induction, data-flow analysis, SMT solving, Craig interpolation, lazy abstraction, and block-abstraction memoization. The synergy of various algorithms in CPAchecker enables support for all properties and categories of C programs in SV-COMP 2024 and contributes to its success in many categories. CPAchecker also generates verification witnesses in the new YAML format.
   BibTeX Entry

   @inproceedings{TACAS24c, author = {Daniel Baier and Dirk Beyer and Po-Chun Chien and Marek Jankola and Matthias Kettl and Nian-Ze Lee and Thomas Lemberger and Marian Lingsch-Rosenfeld and Martin Spiessl and Henrik Wachowitz and Philipp Wendler}, title = {{CPAchecker} 2.3 with Strategy Selection (Competition Contribution)}, booktitle = {Proc.\ TACAS~(3)}, pages = {359-364}, year = {2024}, series = {LNCS~14572}, publisher = {Springer}, doi = {10.1007/978-3-031-57256-2_21}, url = {https://cpachecker.sosy-lab.org/}, pdf = {https://www.sosy-lab.org/research/pub/2024-TACAS.CPAchecker_2.3_with_Strategy_Selection_Competition_Contribution.pdf}, abstract = {CPAchecker is a versatile framework for software verification, rooted in the established concept of configurable program analysis. Compared to the last published system description at SV-COMP 2015, the CPAchecker submission to SV-COMP 2024 incorporates new analyses for reachability safety, memory safety, termination, overflows, and data races. To combine forces of the available analyses in CPAchecker and cover the full spectrum of the diverse program characteristics and specifications in the competition, we use strategy selection to predict a sequential portfolio of analyses that is suitable for a given verification task. The prediction is guided by a set of carefully picked program features. The sequential portfolios are composed based on expert knowledge and consist of bit-precise analyses using <i>k</i>-induction, data-flow analysis, SMT solving, Craig interpolation, lazy abstraction, and block-abstraction memoization. The synergy of various algorithms in CPAchecker enables support for all properties and categories of C programs in SV-COMP 2024 and contributes to its success in many categories. CPAchecker also generates verification witnesses in the new YAML format.}, keyword = {Software Model Checking, Witness-Based Validation, CPAchecker}, artifact = {10.5281/zenodo.10203297}, funding = {DFG-CONVEY, DFG-IDEFIX}, }
2. Marek Jankola and Jan Strejček. Tighter Construction of Tight Büchi Automata. 2024. Springer. Keyword(s): Büchi Automata PDF
   Abstract

   Tight automata are useful in providing the shortest coun- terexample in LTL model checking and also in constructing a maximally satisfying strategy in LTL strategy synthesis. There exists a translation of LTL formulas to tight Büchi automata and several translations of Büchi automata to equivalent tight Büchi automata. This paper presents an- other translation of Büchi automata to equivalent tight Büchi automata. The translation is designed to produce smaller tight automata and it asymptotically improves the best-known upper bound on the size of a tight Büchi automaton equivalent to a given Büchi automaton. We also provide a lower bound, which is more precise than the previously known one. Further, we show that automata reduction methods based on quo- tienting preserve tightness. Our translation was implemented in a tool called Tightener. Experimental evaluation shows that Tightener usually produces smaller tight automata than the translation from LTL to tight automata known as CGH.
   BibTeX Entry

   @inproceedings{JankolaFOSSACS2024, author = {Marek Jankola and Jan Strejček}, title = {Tighter Construction of Tight Büchi Automata}, year = {2024}, publisher = {Springer}, pdf = {https://www.sosy-lab.org/research/pub/2024-FOSSACS.Tighter_Construction_of_Tight_Buchi_Automata.pdf}, abstract = {Tight automata are useful in providing the shortest coun- terexample in LTL model checking and also in constructing a maximally satisfying strategy in LTL strategy synthesis. There exists a translation of LTL formulas to tight Büchi automata and several translations of Büchi automata to equivalent tight Büchi automata. This paper presents an- other translation of Büchi automata to equivalent tight Büchi automata. The translation is designed to produce smaller tight automata and it asymptotically improves the best-known upper bound on the size of a tight Büchi automaton equivalent to a given Büchi automaton. We also provide a lower bound, which is more precise than the previously known one. Further, we show that automata reduction methods based on quo- tienting preserve tightness. Our translation was implemented in a tool called Tightener. Experimental evaluation shows that Tightener usually produces smaller tight automata than the translation from LTL to tight automata known as CGH.}, keyword = {Büchi Automata}, }