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 for Software Verification. Proc. ACM Softw. Eng., 1(FSE), 2024. ACM. doi:10.1145/3660797 Keyword(s): CPAchecker, Software Model Checking Funding: DFG-CONVEY Publisher's Version PDF Presentation Supplement
   Artifact(s)

   1. doi:10.5281/zenodo.11070973
   Abstract

   Assuring the correctness of computing systems is fundamental to our society and economy, and formal verification is a class of techniques approaching this issue with mathematical rigor. Researchers have invented numerous algorithms to automatically prove whether a computational model, e.g., a software program or a hardware digital circuit, satisfies its specification. In the past two decades, Craig interpolation has been widely used in both hardware and software verification. Despite the similarities in the theoretical foundation between hardware and software verification, previous works usually evaluate interpolation-based algorithms on only one type of verification tasks (e.g., either circuits or programs), so the conclusions of these studies do not necessarily transfer to different types of verification tasks. To investigate the transferability of research conclusions from hardware to software, we adopt two performant approaches of interpolation-based hardware model checking, (1) Interpolation-Sequence-Based Model Checking (Vizel and Grumberg, 2009) and (2) Intertwined Forward-Backward Reachability Analysis Using Interpolants (Vizel, Grumberg, and Shoham, 2013), for software verification. We implement the algorithms proposed by the two publications in the software verifier CPAchecker because it has a software-verification adoption of the first interpolation-based algorithm for hardware model checking from 2003, which the two publications use as a comparison baseline. To assess whether the claims in the two publications transfer to software verification, we conduct an extensive experiment on the largest publicly available suite of safety-verification tasks for the programming language C. Our experimental results show that the important characteristics of the two approaches for hardware model checking are transferable to software verification, and that the cross-disciplinary algorithm adoption is beneficial, as the approaches adopted from hardware model checking were able to tackle tasks unsolvable by existing methods. This work consolidates the knowledge in hardware and software verification and provides open-source implementations to improve the understanding of the compared interpolation-based algorithms.
   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 for Software Verification}, journal = {Proc. ACM Softw. Eng.}, volume = {1}, number = {FSE}, year = {2024}, publisher = {ACM}, doi = {10.1145/3660797}, url = {https://www.sosy-lab.org/research/dar-ismc-transferability/}, presentation = {https://www.sosy-lab.org/research/prs/2024-07-18_FSE_A_Transferability_Study_of_Interpolation-Based_HWMC_for_SV_Marek.pdf}, abstract = {Assuring the correctness of computing systems is fundamental to our society and economy, and <em>formal verification</em> is a class of techniques approaching this issue with mathematical rigor. Researchers have invented numerous algorithms to automatically prove whether a computational model, e.g., a software program or a hardware digital circuit, satisfies its specification. In the past two decades, <em>Craig interpolation</em> has been widely used in both hardware and software verification. Despite the similarities in the theoretical foundation between hardware and software verification, previous works usually evaluate interpolation-based algorithms on only one type of verification tasks (e.g., either circuits or programs), so the conclusions of these studies do not necessarily transfer to different types of verification tasks. To investigate the transferability of research conclusions from hardware to software, we adopt two performant approaches of interpolation-based hardware model checking, (1) <em>Interpolation-Sequence-Based Model Checking</em> (<a href="https://doi.org/10.1109/FMCAD.2009.5351148">Vizel and Grumberg, 2009</a>) and (2) <em>Intertwined Forward-Backward Reachability Analysis Using Interpolants</em> (<a href="https://doi.org/10.1007/978-3-642-36742-7_22">Vizel, Grumberg, and Shoham, 2013</a>), for software verification. We implement the algorithms proposed by the two publications in the software verifier CPAchecker because it has a software-verification adoption of the first interpolation-based algorithm for hardware model checking from 2003, which the two publications use as a comparison baseline. To assess whether the claims in the two publications transfer to software verification, we conduct an extensive experiment on the largest publicly available suite of safety-verification tasks for the programming language C. Our experimental results show that the important characteristics of the two approaches for hardware model checking are transferable to software verification, and that the cross-disciplinary algorithm adoption is beneficial, as the approaches adopted from hardware model checking were able to tackle tasks unsolvable by existing methods. This work consolidates the knowledge in hardware and software verification and provides open-source implementations to improve the understanding of the compared interpolation-based algorithms.}, keyword = {CPAchecker,Software Model Checking}, _pdf = {https://www.sosy-lab.org/research/pub/2024-FSE.A_Transferability_Study_of_Interpolation-Based_Hardware_Model_Checking_for_Software_Verification.pdf}, annote = {This article received the "ACM SIGSOFT Distinguished Paper Award" and its reproduction artifact received the "ACM SIGSOFT Best Artifact Award" at <a href="https://2024.esec-fse.org/info/awards">FSE 2024</a>!}, articleno = {90}, artifact = {10.5281/zenodo.11070973}, funding = {DFG-CONVEY}, issue_date = {July 2024}, numpages = {23}, }
   Additional Infos

   This article received the "ACM SIGSOFT Distinguished Paper Award" and its reproduction artifact received the "ACM SIGSOFT Best Artifact Award" at FSE 2024!

2. Dirk Beyer, Po-Chun Chien, and Marek Jankola. BenchCloud: A Platform for Scalable Performance Benchmarking. In Proc. ASE, pages 2386-2389, 2024. ACM. doi:10.1145/3691620.3695358 Keyword(s): Benchmarking, Competition on Software Verification (SV-COMP), Competition on Software Testing (Test-Comp) Funding: DFG-CONVEY, DFG-COOP Publisher's Version PDF Presentation Video Supplement
   Artifact(s)

   1. doi:10.5281/zenodo.13742756
   Abstract

   Performance evaluation is a crucial method for assessing automated-reasoning tools. Evaluating automated tools requires rigorous benchmarking to accurately measure resource consumption, including time and memory, which are essential for understanding the tools' capabilities. BenchExec, a widely used benchmarking framework, reliably measures resource usage for tools executed locally on a single node. This paper describes BenchCloud, a solution for elastic and scalable job distribution across hundreds of nodes, enabling large-scale experiments on distributed and heterogeneous computing environments. BenchCloud seamlessly integrates with BenchExec, allowing BenchExec to delegate the actual execution to BenchCloud. The system has been employed in several prominent international competitions in automated reasoning, including SMT-COMP, SV-COMP, and Test-Comp, underscoring its importance in rigorous tool evaluation across various research domains. It helps to ensure both internal and external validity of the experimental results. This paper presents an overview of BenchCloud's architecture and high- lights its primary use cases in facilitating scalable benchmarking.
   Demonstration video: https://youtu.be/aBfQytqPm0U
   Running system: https://benchcloud.sosy-lab.org/
   BibTeX Entry

   @inproceedings{ASE24a, author = {Dirk Beyer and Po-Chun Chien and Marek Jankola}, title = {{BenchCloud}: {A} Platform for Scalable Performance Benchmarking}, booktitle = {Proc.\ ASE}, pages = {2386-2389}, year = {2024}, series = {}, publisher = {ACM}, doi = {10.1145/3691620.3695358}, url = {https://benchcloud.sosy-lab.org/}, pdf = {https://www.sosy-lab.org/research/pub/2024-ASE24.BenchCloud_A_Platform_for_Scalable_Performance_Benchmarking.pdf}, presentation = {https://www.sosy-lab.org/research/prs/2024-10-30_ASE_BenchCloud_A_Platform_for_Scalable_Performance_Benchmarking_Po-Chun.pdf}, abstract = {Performance evaluation is a crucial method for assessing automated-reasoning tools. Evaluating automated tools requires rigorous benchmarking to accurately measure resource consumption, including time and memory, which are essential for understanding the tools' capabilities. BenchExec, a widely used benchmarking framework, reliably measures resource usage for tools executed locally on a single node. This paper describes BenchCloud, a solution for elastic and scalable job distribution across hundreds of nodes, enabling large-scale experiments on distributed and heterogeneous computing environments. BenchCloud seamlessly integrates with BenchExec, allowing BenchExec to delegate the actual execution to BenchCloud. The system has been employed in several prominent international competitions in automated reasoning, including SMT-COMP, SV-COMP, and Test-Comp, underscoring its importance in rigorous tool evaluation across various research domains. It helps to ensure both internal and external validity of the experimental results. This paper presents an overview of BenchCloud's architecture and high- lights its primary use cases in facilitating scalable benchmarking. <br> Demonstration video: <a href="https://youtu.be/aBfQytqPm0U">https://youtu.be/aBfQytqPm0U</a> <br> Running system: <a href="https://benchcloud.sosy-lab.org/">https://benchcloud.sosy-lab.org/</a>}, keyword = {Benchmarking, Competition on Software Verification (SV-COMP), Competition on Software Testing (Test-Comp)}, artifact = {10.5281/zenodo.13742756}, funding = {DFG-CONVEY, DFG-COOP}, video = {https://youtu.be/aBfQytqPm0U}, }
3. Daniel Baier, Dirk Beyer, Po-Chun Chien, Marie-Christine Jakobs, Marek Jankola, Matthias Kettl, Nian-Ze Lee, Thomas Lemberger, Marian Lingsch-Rosenfeld, Henrik Wachowitz, and Philipp Wendler. Software Verification with CPAchecker 3.0: Tutorial and User Guide. In Proceedings of the 26th International Symposium on Formal Methods (FM 2024, Milan, Italy, September 9-13), LNCS 14934, pages 543-570, 2024. Springer. doi:10.1007/978-3-031-71177-0_30 Keyword(s): CPAchecker, Software Model Checking, Software Testing Funding: DFG-COOP, DFG-CONVEY, DFG-IDEFIX Publisher's Version PDF Presentation Supplement
   Artifact(s)

   1. doi:10.5281/zenodo.13612338
   Abstract

   This tutorial provides an introduction to CPAchecker for users. CPAchecker is a flexible and configurable framework for software verification and testing. The framework provides many abstract domains, such as BDDs, explicit values, intervals, memory graphs, and predicates, and many program-analysis and model-checking algorithms, such as abstract interpretation, bounded model checking, Impact, interpolation-based model checking, k-induction, PDR, predicate abstraction, and symbolic execution. This tutorial presents basic use cases for CPAchecker in formal software verification, focusing on its main verification techniques with their strengths and weaknesses. An extended version also shows further use cases of CPAchecker for test-case generation and witness-based result validation. The envisioned readers are assumed to possess a background in automatic formal verification and program analysis, but prior knowledge of CPAchecker is not required. This tutorial and user guide is based on CPAchecker in version 3.0. This user guide's latest version and other documentation are available at https://cpachecker.sosy-lab.org/doc.php.
   BibTeX Entry

   @inproceedings{FM24a, author = {Daniel Baier and Dirk Beyer and Po-Chun Chien and Marie-Christine Jakobs and Marek Jankola and Matthias Kettl and Nian-Ze Lee and Thomas Lemberger and Marian Lingsch-Rosenfeld and Henrik Wachowitz and Philipp Wendler}, title = {Software Verification with {CPAchecker} 3.0: {Tutorial} and User Guide}, booktitle = {Proceedings of the 26th International Symposium on Formal Methods (FM~2024, Milan, Italy, September 9-13)}, pages = {543-570}, year = {2024}, series = {LNCS~14934}, publisher = {Springer}, doi = {10.1007/978-3-031-71177-0_30}, url = {https://cpachecker.sosy-lab.org}, pdf = {https://www.sosy-lab.org/research/pub/2024-FM.Software_Verification_with_CPAchecker_3.0_Tutorial_and_User_Guide.pdf}, presentation = {https://www.sosy-lab.org/research/prs/2024-09-10_FM24_CPAchecker_Tutorial.pdf}, abstract = {This tutorial provides an introduction to CPAchecker for users. CPAchecker is a flexible and configurable framework for software verification and testing. The framework provides many abstract domains, such as BDDs, explicit values, intervals, memory graphs, and predicates, and many program-analysis and model-checking algorithms, such as abstract interpretation, bounded model checking, Impact, interpolation-based model checking, <i>k</i>-induction, PDR, predicate abstraction, and symbolic execution. This tutorial presents basic use cases for CPAchecker in formal software verification, focusing on its main verification techniques with their strengths and weaknesses. An extended version also shows further use cases of CPAchecker for test-case generation and witness-based result validation. The envisioned readers are assumed to possess a background in automatic formal verification and program analysis, but prior knowledge of CPAchecker is not required. This tutorial and user guide is based on CPAchecker in version 3.0. This user guide's latest version and other documentation are available at <a href="https://cpachecker.sosy-lab.org/doc.php">https://cpachecker.sosy-lab.org/doc.php</a>.}, keyword = {CPAchecker, Software Model Checking, Software Testing}, annote = {An <a href="https://www.sosy-lab.org/research/bib/All/index.html#TechReport24c">extended version</a> of this article is available on <a href="https://doi.org/10.48550/arXiv.2409.02094">arXiv</a>.}, artifact = {10.5281/zenodo.13612338}, funding = {DFG-COOP, DFG-CONVEY, DFG-IDEFIX}, }
   Additional Infos

   An extended version of this article is available on arXiv.
4. 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 Proceedings of the 30th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2024, Luxembourg, Luxembourg, April 6-11), part 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 = {Proceedings of the 30th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS~2024, Luxembourg, Luxembourg, April 6-11), part~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/}, 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}, _pdf = {https://www.sosy-lab.org/research/pub/2024-TACAS.CPAchecker_2.3_with_Strategy_Selection_Competition_Contribution.pdf}, artifact = {10.5281/zenodo.10203297}, funding = {DFG-CONVEY, DFG-IDEFIX}, }
5. 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}, }

6. Daniel Baier, Dirk Beyer, Po-Chun Chien, Marie-Christine Jakobs, Marek Jankola, Matthias Kettl, Nian-Ze Lee, Thomas Lemberger, Marian Lingsch-Rosenfeld, Henrik Wachowitz, and Philipp Wendler. Software Verification with CPAchecker 3.0: Tutorial and User Guide (Extended Version). Technical report 2409.02094, arXiv/CoRR, September 2024. doi:10.48550/arXiv.2409.02094 Keyword(s): CPAchecker, Software Model Checking, Software Testing Funding: DFG-COOP, DFG-CONVEY, DFG-IDEFIX Publisher's Version PDF Presentation Supplement
   Artifact(s)

   1. doi:10.5281/zenodo.13612338
   Abstract

   This tutorial provides an introduction to CPAchecker for users. CPAchecker is a flexible and configurable framework for software verification and testing. The framework provides many abstract domains, such as BDDs, explicit values, intervals, memory graphs, and predicates, and many program-analysis and model-checking algorithms, such as abstract interpretation, bounded model checking, Impact, interpolation-based model checking, k-induction, PDR, predicate abstraction, and symbolic execution. This tutorial presents basic use cases for CPAchecker in formal software verification, focusing on its main verification techniques with their strengths and weaknesses. It also shows further use cases of CPAchecker for test-case generation and witness-based result validation. The envisioned readers are assumed to possess a background in automatic formal verification and program analysis, but prior knowledge of CPAchecker is not required. This tutorial and user guide is based on CPAchecker in version 3.0. This user guide's latest version and other documentation are available at https://cpachecker.sosy-lab.org/doc.php.
   BibTeX Entry

   @techreport{TechReport24c, author = {Daniel Baier and Dirk Beyer and Po-Chun Chien and Marie-Christine Jakobs and Marek Jankola and Matthias Kettl and Nian-Ze Lee and Thomas Lemberger and Marian Lingsch-Rosenfeld and Henrik Wachowitz and Philipp Wendler}, title = {Software Verification with {CPAchecker} 3.0: {Tutorial} and User Guide (Extended Version)}, number = {2409.02094}, year = {2024}, doi = {10.48550/arXiv.2409.02094}, url = {https://cpachecker.sosy-lab.org}, presentation = {https://www.sosy-lab.org/research/prs/2024-09-10_FM24_CPAchecker_Tutorial.pdf}, abstract = {This tutorial provides an introduction to CPAchecker for users. CPAchecker is a flexible and configurable framework for software verification and testing. The framework provides many abstract domains, such as BDDs, explicit values, intervals, memory graphs, and predicates, and many program-analysis and model-checking algorithms, such as abstract interpretation, bounded model checking, Impact, interpolation-based model checking, <i>k</i>-induction, PDR, predicate abstraction, and symbolic execution. This tutorial presents basic use cases for CPAchecker in formal software verification, focusing on its main verification techniques with their strengths and weaknesses. It also shows further use cases of CPAchecker for test-case generation and witness-based result validation. The envisioned readers are assumed to possess a background in automatic formal verification and program analysis, but prior knowledge of CPAchecker is not required. This tutorial and user guide is based on CPAchecker in version 3.0. This user guide's latest version and other documentation are available at <a href="https://cpachecker.sosy-lab.org/doc.php">https://cpachecker.sosy-lab.org/doc.php</a>.}, keyword = {CPAchecker, Software Model Checking, Software Testing}, annote = {This technical report is an extended version of our <a href="https://www.sosy-lab.org/research/bib/All/index.html#FM24a">paper</a> at FM 2024.}, artifact = {10.5281/zenodo.13612338}, funding = {DFG-COOP, DFG-CONVEY, DFG-IDEFIX}, institution = {arXiv/CoRR}, month = {September}, }
   Additional Infos

   This technical report is an extended version of our paper at FM 2024.