Software and Computational Systems Lab

Publications about Btor2

1. Salih Ates, Dirk Beyer, Po-Chun Chien, and Nian-Ze Lee. MoXIchecker: An Extensible Model Checker for MoXI. In Proc. VSTTE 2024, LNCS 15525, pages 1-14, 2025. Springer. doi:10.1007/978-3-031-86695-1_1 Keyword(s): SMT, Btor2, MoXIchecker Funding: DFG-CONVEY, DFG-BRIDGE Publisher's Version PDF Supplement
   Artifact(s)

   1. doi:10.5281/zenodo.13895872
   Abstract

   MoXI is a new intermediate verification language introduced in 2024 to promote the standardization and open-source implementations for symbolic model checking by extending the SMT-LIB 2 language with constructs to define state-transition systems. The tool suite of MoXI provides a translator from MoXI to Btor2, which is a lower-level intermediate language for hardware verification, and a translation-based model checker, which invokes mature hardware model checkers for Btor2 to analyze the translated verification tasks. The extensibility of such a translation-based model checker is restricted because more complex theories, such as integer or real arithmetics, cannot be precisely expressed with bit-vectors of fixed lengths in Btor2. We present MoXIchecker, the first model checker that solves MoXI verification tasks directly. Instead of translating MoXI to lower-level languages, MoXIchecker uses the solver-agnostic library PySMT for SMT solvers as backend for its verification algorithms. MoXIchecker is extensible because it accommodates verification tasks involving more complex theories, not limited by lower-level languages, facilitates the implementation of new algorithms, and is solver-agnostic by using the API of PySMT. In our evaluation, MoXIchecker uniquely solved tasks that use integer or real arithmetics, and achieved a comparable performance against the translation-based model checker from the MoXI tool suite.
   BibTeX Entry

   @inproceedings{VSTTE24, author = {Salih Ates and Dirk Beyer and Po-Chun Chien and Nian-Ze Lee}, title = {{MoXIchecker}: {An} Extensible Model Checker for {MoXI}}, booktitle = {Proc.\ VSTTE 2024}, pages = {1-14}, year = {2025}, series = {LNCS~15525}, publisher = {Springer}, doi = {10.1007/978-3-031-86695-1_1}, url = {https://www.sosy-lab.org/research/moxichecker/}, pdf = {https://www.sosy-lab.org/research/pub/2024-VSTTE.MoXIchecker_An_Extensible_Model_Checker_for_MoXI.pdf}, presentation = {}, abstract = {MoXI is a new intermediate verification language introduced in 2024 to promote the standardization and open-source implementations for symbolic model checking by extending the SMT-LIB 2 language with constructs to define state-transition systems. The tool suite of MoXI provides a translator from MoXI to Btor2, which is a lower-level intermediate language for hardware verification, and a translation-based model checker, which invokes mature hardware model checkers for Btor2 to analyze the translated verification tasks. The extensibility of such a translation-based model checker is restricted because more complex theories, such as integer or real arithmetics, cannot be precisely expressed with bit-vectors of fixed lengths in Btor2. We present MoXIchecker, the first model checker that solves MoXI verification tasks directly. Instead of translating MoXI to lower-level languages, MoXIchecker uses the solver-agnostic library PySMT for SMT solvers as backend for its verification algorithms. MoXIchecker is extensible because it accommodates verification tasks involving more complex theories, not limited by lower-level languages, facilitates the implementation of new algorithms, and is solver-agnostic by using the API of PySMT. In our evaluation, MoXIchecker uniquely solved tasks that use integer or real arithmetics, and achieved a comparable performance against the translation-based model checker from the MoXI tool suite.}, keyword = {SMT, Btor2, MoXIchecker}, artifact = {10.5281/zenodo.13895872}, funding = {DFG-CONVEY, DFG-BRIDGE}, }
2. Zhengyang Lu, Po-Chun Chien, Nian-Ze Lee, and Vijay Ganesh. Algorithm Selection for Word-Level Hardware Model Checking (Student Abstract). In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 29426-29427, 2025. doi:10.1609/aaai.v39i28.35275 Keyword(s): Btor2 Funding: DFG-BRIDGE Publisher's Version PDF
   Abstract

   We build the first machine-learning-based algorithm selection tool for hardware verification described in the Btor2 format. In addition to hardware verifiers, our tool also selects from a set of software verifiers to solve a given Btor2 instance, enabled by a Btor2-to-C translator. We propose two embeddings for a Btor2 instance, Bag of Keywords and Bit-Width Aggregation. Pairwise classifiers are applied for algorithm selection. Upon evaluation, our tool Btor2-Select solves 30.0% more instances and reduces PAR-2 by 50.2%, compared to the PDR implementation in the HWMCC'20 winner model checker AVR. Measured by the Shapley values, the software verifiers collectively contributed 27.2% to Btor2-Select's performance.
   BibTeX Entry

   @inproceedings{AAAI25, author = {Zhengyang Lu and Po-Chun Chien and Nian-Ze Lee and Vijay Ganesh}, title = {Algorithm Selection for Word-Level Hardware Model Checking (Student Abstract)}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence~(AAAI)}, volume = {39}, number = {28}, pages = {29426-29427}, year = {2025}, doi = {10.1609/aaai.v39i28.35275}, pdf = {https://www.sosy-lab.org/research/pub/2025-AAAI.Algorithm_Selection_for_Word-Level_Hardware_Model_Checking_Student_Abstract.pdf}, abstract = {We build the first machine-learning-based algorithm selection tool for hardware verification described in the Btor2 format. In addition to hardware verifiers, our tool also selects from a set of software verifiers to solve a given Btor2 instance, enabled by a Btor2-to-C translator. We propose two embeddings for a Btor2 instance, Bag of Keywords and Bit-Width Aggregation. Pairwise classifiers are applied for algorithm selection. Upon evaluation, our tool Btor2-Select solves 30.0% more instances and reduces PAR-2 by 50.2%, compared to the PDR implementation in the HWMCC'20 winner model checker AVR. Measured by the Shapley values, the software verifiers collectively contributed 27.2% to Btor2-Select's performance.}, keyword = {Btor2}, funding = {DFG-BRIDGE}, }
3. Zsófia Ádám, Dirk Beyer, Po-Chun Chien, Nian-Ze Lee, and Nils Sirrenberg. Btor2-Cert: A Certifying Hardware-Verification Framework Using Software Analyzers. In Proc. TACAS (3), LNCS 14572, pages 129-149, 2024. Springer. doi:10.1007/978-3-031-57256-2_7 Keyword(s): Software Model Checking, Witness-Based Validation, Cooperative Verification, Btor2 Funding: DFG-CONVEY Publisher's Version PDF Supplement
   Artifact(s)

   1. doi:10.5281/zenodo.10548597
   Abstract

   Formal verification is essential but challenging: Even the best verifiers may produce wrong verification verdicts. Certifying verifiers enhance the confidence in verification results by generating a witness for other tools to validate the verdict independently. Recently, translating the hardware-modeling language Btor2 to software, such as the programming language C or LLVM intermediate representation, has been actively studied and facilitated verifying hardware designs by software analyzers. However, it remained unknown whether witnesses produced by software verifiers contain helpful information about the original circuits and how such information can aid hardware analysis. We propose a certifying and validating framework Btor2-Cert to verify safety properties of Btor2 circuits, combining Btor2-to-C translation, software verifiers, and a new witness validator Btor2-Val, to answer the above open questions. Btor2-Cert translates a software violation witness to a Btor2 violation witness; As the Btor2 language lacks a format for correctness witnesses, we encode invariants in software correctness witnesses as Btor2 circuits. The validator Btor2-Val checks violation witnesses by circuit simulation and correctness witnesses by validation via verification. In our evaluation, Btor2-Cert successfully utilized software witnesses to improve quality assurance of hardware. By invoking the software verifier CBMC on translated programs, it uniquely solved, with confirmed witnesses, 8% of the unsafe tasks for which the hardware verifier ABC failed to detect bugs.
   BibTeX Entry

   @inproceedings{TACAS24a, author = {Zsófia Ádám and Dirk Beyer and Po-Chun Chien and Nian-Ze Lee and Nils Sirrenberg}, title = {{Btor2-Cert}: {A} Certifying Hardware-Verification Framework Using Software Analyzers}, booktitle = {Proc.\ TACAS~(3)}, pages = {129-149}, year = {2024}, series = {LNCS~14572}, publisher = {Springer}, doi = {10.1007/978-3-031-57256-2_7}, sha256 = {eab05660ea54230fb5b7c3395501c05ad33d7489761026de20b4ab1e73dba803}, url = {https://www.sosy-lab.org/research/btor2-cert/}, abstract = {Formal verification is essential but challenging: Even the best verifiers may produce wrong verification verdicts. Certifying verifiers enhance the confidence in verification results by generating a witness for other tools to validate the verdict independently. Recently, translating the hardware-modeling language Btor2 to software, such as the programming language C or LLVM intermediate representation, has been actively studied and facilitated verifying hardware designs by software analyzers. However, it remained unknown whether witnesses produced by software verifiers contain helpful information about the original circuits and how such information can aid hardware analysis. We propose a certifying and validating framework Btor2-Cert to verify safety properties of Btor2 circuits, combining Btor2-to-C translation, software verifiers, and a new witness validator Btor2-Val, to answer the above open questions. Btor2-Cert translates a software violation witness to a Btor2 violation witness; As the Btor2 language lacks a format for correctness witnesses, we encode invariants in software correctness witnesses as Btor2 circuits. The validator Btor2-Val checks violation witnesses by circuit simulation and correctness witnesses by validation via verification. In our evaluation, Btor2-Cert successfully utilized software witnesses to improve quality assurance of hardware. By invoking the software verifier CBMC on translated programs, it uniquely solved, with confirmed witnesses, 8% of the unsafe tasks for which the hardware verifier ABC failed to detect bugs.}, keyword = {Software Model Checking, Witness-Based Validation, Cooperative Verification, Btor2}, _pdf = {https://www.sosy-lab.org/research/pub/2024-TACAS.Btor2-Cert_A_Certifying_Hardware-Verification_Framework_Using_Software_Analyzers.pdf}, annote = {The reproduction package of this article received the "Distinguished Artifact Award" at TACAS 2024!}, artifact = {10.5281/zenodo.10548597}, funding = {DFG-CONVEY}, poster = {https://www.sosy-lab.org/research/pst/2024-03-22_ETAPS_Btor2-Cert_Poster.pdf}, }
   Additional Infos

   The reproduction package of this article received the "Distinguished Artifact Award" at TACAS 2024!
4. Po-Chun Chien and Nian-Ze Lee. CPV: A Circuit-Based Program Verifier (Competition Contribution). In Proc. TACAS, LNCS 14572, pages 365-370, 2024. Springer. doi:10.1007/978-3-031-57256-2_22 Keyword(s): Software Model Checking, Cooperative Verification, Btor2 Funding: DFG-CONVEY Publisher's Version PDF Presentation Supplement
   Artifact(s)

   1. doi:10.5281/zenodo.10203472
   Abstract

   We submit to SV-COMP 2024 CPV, a circuit-based software verifier for C programs. CPV utilizes sequential circuits as its intermediate representation and invokes hardware model checkers to analyze the reachability safety of C programs. As the frontend, it uses Kratos2, a recently proposed verification tool, to translate a C program to a sequential circuit. As the backend, state-of-the-art hardware model checkers ABC and AVR are employed to verify the translated circuits. We configure the hardware model checkers to run various analyses, including IC3/PDR, interpolation-based model checking, and k-induction. Information discovered by hardware model checkers is represented as verification witnesses. In the competition, CPV achieved comparable performance against participants whose intermediate representations are based on control-flow graphs. In the category ReachSafety, it outperformed several mature software verifiers as a first-year participant. CPV manifests the feasibility of sequential circuits as an alternative intermediate representation for program analysis and enables head-to-head algorithmic comparison between hardware and software verification.
   BibTeX Entry

   @inproceedings{CPV-TACAS24, author = {Po-Chun Chien and Nian-Ze Lee}, title = {CPV: A Circuit-Based Program Verifier (Competition Contribution)}, booktitle = {Proc.\ TACAS}, pages = {365-370}, year = {2024}, series = {LNCS~14572}, publisher = {Springer}, doi = {10.1007/978-3-031-57256-2_22}, url = {https://gitlab.com/sosy-lab/software/cpv}, pdf = {https://www.sosy-lab.org/research/pub/2024-TACAS.CPV_A_Circuit-Based_Program_Verifier_Competition_Contribution.pdf}, presentation = {https://www.sosy-lab.org/research/prs/2024-04-08_SVCOMP_CPV_A_Circuit-Based_Program_Verifier_Po-Chun.pdf}, abstract = {We submit to SV-COMP 2024 CPV, a circuit-based software verifier for C programs. CPV utilizes sequential circuits as its intermediate representation and invokes hardware model checkers to analyze the reachability safety of C programs. As the frontend, it uses Kratos2, a recently proposed verification tool, to translate a C program to a sequential circuit. As the backend, state-of-the-art hardware model checkers ABC and AVR are employed to verify the translated circuits. We configure the hardware model checkers to run various analyses, including IC3/PDR, interpolation-based model checking, and <i>k</i>-induction. Information discovered by hardware model checkers is represented as verification witnesses. In the competition, CPV achieved comparable performance against participants whose intermediate representations are based on control-flow graphs. In the category <i>ReachSafety</i>, it outperformed several mature software verifiers as a first-year participant. CPV manifests the feasibility of sequential circuits as an alternative intermediate representation for program analysis and enables head-to-head algorithmic comparison between hardware and software verification.}, keyword = {Software Model Checking, Cooperative Verification, Btor2}, artifact = {10.5281/zenodo.10203472}, funding = {DFG-CONVEY}, poster = {https://www.sosy-lab.org/research/pst/2024-03-22_ETAPS_CPV_Poster.pdf}, }
5. Dirk Beyer, Po-Chun Chien, and Nian-Ze Lee. Bridging Hardware and Software Analysis with Btor2C: A Word-Level-Circuit-to-C Translator. In Proc. TACAS, LNCS 13994, pages 152-172, 2023. Springer. doi:10.1007/978-3-031-30820-8_12 Keyword(s): Software Model Checking, Cooperative Verification, Btor2 Funding: DFG-CONVEY Publisher's Version PDF Presentation Supplement
   Artifact(s)

   1. doi:10.5281/zenodo.7551707
   Abstract

   Across the broad field for the analysis of computational systems, research endeavors are often categorized by the respective models under investigation. Algorithms and tools are usually developed for a specific model, hindering their applications to similar problems originating from other computational systems. A prominent example of such situation is the studies on formal verification and testing for hardware and software systems. The two research communities share common theoretical foundations and solving methods, including satisfiability, interpolation, and abstraction refinement. Nevertheless, it is often demanding for one community to benefit from the advancements of the other, as analyzers typically assume a particular input format. To bridge the gap between the hardware and software analysis, we propose Btor2C, a converter from word-level sequential circuits to C programs. We choose the Btor2 language as the input format for its simplicity and bit-precise semantics. It can be deemed as an intermediate representation tailored for analysis. Given a Btor2 circuit, Btor2C generates a behaviorally equivalent program in the C language, supported by most static program analyzers. We demonstrate the use cases of Btor2C by translating the benchmark set from the Hardware Model Checking Competitions into C programs and analyze them by tools from the Competitions on Software Verification and Testing. Our results show that software analyzers can complement hardware verifiers for enhanced quality assurance.
   BibTeX Entry

   @inproceedings{TACAS23a, author = {Dirk Beyer and Po-Chun Chien and Nian-Ze Lee}, title = {Bridging Hardware and Software Analysis with {Btor2C}: {A} Word-Level-Circuit-to-{C} Translator}, booktitle = {Proc.\ TACAS}, pages = {152-172}, year = {2023}, series = {LNCS~13994}, publisher = {Springer}, doi = {10.1007/978-3-031-30820-8_12}, url = {https://www.sosy-lab.org/research/btor2c/}, presentation = {https://www.sosy-lab.org/research/prs/2023-04-26_TACAS23_Bridging_Hardware_and_Software_Analysis_with_Btor2C_Po-Chun.pdf}, abstract = {Across the broad field for the analysis of computational systems, research endeavors are often categorized by the respective models under investigation. Algorithms and tools are usually developed for a specific model, hindering their applications to similar problems originating from other computational systems. A prominent example of such situation is the studies on formal verification and testing for hardware and software systems. The two research communities share common theoretical foundations and solving methods, including satisfiability, interpolation, and abstraction refinement. Nevertheless, it is often demanding for one community to benefit from the advancements of the other, as analyzers typically assume a particular input format. To bridge the gap between the hardware and software analysis, we propose Btor2C, a converter from word-level sequential circuits to C programs. We choose <a href="https://doi.org/10.1007/978-3-319-96145-3_32">the Btor2 language</a> as the input format for its simplicity and bit-precise semantics. It can be deemed as an intermediate representation tailored for analysis. Given a Btor2 circuit, Btor2C generates a behaviorally equivalent program in the C language, supported by most static program analyzers. We demonstrate the use cases of Btor2C by translating the benchmark set from the Hardware Model Checking Competitions into C programs and analyze them by tools from the Competitions on Software Verification and Testing. Our results show that software analyzers can complement hardware verifiers for enhanced quality assurance.}, keyword = {Software Model Checking, Cooperative Verification, Btor2}, _pdf = {https://www.sosy-lab.org/research/pub/2023-TACAS.Bridging_Hardware_and_Software_Analysis_with_Btor2C_A_Word-Level-Circuit-to-C_Translator.pdf}, artifact = {10.5281/zenodo.7551707}, funding = {DFG-CONVEY}, poster = {https://www.sosy-lab.org/research/pst/2023-04-24_ETAPS_Btor2C_Poster.pdf}, }

6. Salih Ates, Dirk Beyer, Po-Chun Chien, and Nian-Ze Lee. MoXIchecker: An Extensible Model Checker for MoXI. Technical report 2407.15551, arXiv/CoRR, March 2024. doi:10.48550/arXiv.2407.15551 Keyword(s): Btor2 Funding: DFG-CONVEY, DFG-BRIDGE Publisher's Version PDF Supplement
   Artifact(s)

   1. doi:10.5281/zenodo.12787654
   Abstract

   MoXI is a new intermediate verification language introduced in 2024 to promote the standardization and open-source implementations for symbolic model checking by extending the SMT-LIB 2 language with constructs to define state-transition systems. The tool suite of MoXI provides a translator from MoXI to Btor2, which is a lower-level intermediate language for hardware verification, and a translation-based model checker, which invokes mature hardware model checkers for Btor2 to analyze the translated verification tasks. The extensibility of such a translation-based model checker is restricted because more complex theories, such as integer or real arithmetics, cannot be precisely expressed with bit-vectors of fixed lengths in Btor2. We present MoXIchecker, the first model checker that solves MoXI verification tasks directly. Instead of translating MoXI to lower-level languages, MoXIchecker uses the solver-agnostic library PySMT for SMT solvers as backend for its verification algorithms. MoXIchecker is extensible because it accommodates verification tasks involving more complex theories, not limited by lower-level languages, facilitates the implementation of new algorithms, and is solver-agnostic by using the API of PySMT. In our evaluation, MoXIchecker uniquely solved tasks that use integer or real arithmetics, and achieved a comparable performance against the translation-based model checker from the MoXI tool suite.
   BibTeX Entry

   @techreport{TechReport24b, author = {Salih Ates and Dirk Beyer and Po-Chun Chien and Nian-Ze Lee}, title = {{MoXIchecker}: {An} Extensible Model Checker for {MoXI}}, number = {2407.15551}, year = {2024}, doi = {10.48550/arXiv.2407.15551}, url = {https://gitlab.com/sosy-lab/software/moxichecker}, pdf = {https://arxiv.org/abs/2407.15551}, abstract = {MoXI is a new intermediate verification language introduced in 2024 to promote the standardization and open-source implementations for symbolic model checking by extending the SMT-LIB 2 language with constructs to define state-transition systems. The tool suite of MoXI provides a translator from MoXI to Btor2, which is a lower-level intermediate language for hardware verification, and a translation-based model checker, which invokes mature hardware model checkers for Btor2 to analyze the translated verification tasks. The extensibility of such a translation-based model checker is restricted because more complex theories, such as integer or real arithmetics, cannot be precisely expressed with bit-vectors of fixed lengths in Btor2. We present MoXIchecker, the first model checker that solves MoXI verification tasks directly. Instead of translating MoXI to lower-level languages, MoXIchecker uses the solver-agnostic library PySMT for SMT solvers as backend for its verification algorithms. MoXIchecker is extensible because it accommodates verification tasks involving more complex theories, not limited by lower-level languages, facilitates the implementation of new algorithms, and is solver-agnostic by using the API of PySMT. In our evaluation, MoXIchecker uniquely solved tasks that use integer or real arithmetics, and achieved a comparable performance against the translation-based model checker from the MoXI tool suite.}, keyword = {Btor2}, artifact = {10.5281/zenodo.12787654}, funding = {DFG-CONVEY,DFG-BRIDGE}, institution = {arXiv/CoRR}, month = {March}, }
7. Po-Chun Chien. Bridging Hardware and Software Formal Verification (Extended Abstract). Technical report 2024-06, LMU Munich, 2024. Keyword(s): Software Model Checking, Cooperative Verification, Btor2, CPAchecker, Witness-Based Validation Funding: DFG-CONVEY PDF Presentation
   Abstract

   Modern technology relies heavily on the integration of hardware and software systems, from embedded devices in consumer electronics to safety-critical controllers. Despite their interdependence, the tools and methods used for verifying the correctness and reliability of these systems are often segregated, meaning that the advancement in one community cannot benefit another directly. Addressing this challenge, my dissertation aims at bridging the gap between hardware and software formal analysis. This involves translating representations of verification tasks, generating certificates for verification results, integrating state-of-the-art formal analysis tools into a cohesive framework, and adapting and combining model-checking algorithms across domains. By translating word-level hardware circuits into C programs, we found out that software analyzers were able to identify property violations that well-established hardware verifiers failed to detect. Moreover, by adopting interpolation-based hardware-verification algorithms for software analysis, we were able to tackle tasks unsolvable by existing methods. Our research consolidates knowledge from both hardware and software domains, paving a pathway for comprehensive system-level verification.
   BibTeX Entry

   @techreport{chien:fm24-doc-symposium, author = {Po-Chun Chien}, title = {Bridging Hardware and Software Formal Verification (Extended Abstract)}, number = {2024-06}, year = {2024}, pdf = {https://www.sosy-lab.org/research/pub/2024-FM_Doctoral_Symposium.Bridging_Hardware_and_Software_Formal_Verification_Extended_Abstract.pdf}, presentation = {https://www.sosy-lab.org/research/prs/2024-09-10_FM24_Bridging_Hardware_and_Software_Formal_Verification_Po-Chun.pdf}, abstract = {Modern technology relies heavily on the integration of hardware and software systems, from embedded devices in consumer electronics to safety-critical controllers. Despite their interdependence, the tools and methods used for verifying the correctness and reliability of these systems are often segregated, meaning that the advancement in one community cannot benefit another directly. Addressing this challenge, my dissertation aims at bridging the gap between hardware and software formal analysis. This involves translating representations of verification tasks, generating certificates for verification results, integrating state-of-the-art formal analysis tools into a cohesive framework, and adapting and combining model-checking algorithms across domains. By translating word-level hardware circuits into C programs, we found out that software analyzers were able to identify property violations that well-established hardware verifiers failed to detect. Moreover, by adopting interpolation-based hardware-verification algorithms for software analysis, we were able to tackle tasks unsolvable by existing methods. Our research consolidates knowledge from both hardware and software domains, paving a pathway for comprehensive system-level verification.}, keyword = {Software Model Checking, Cooperative Verification, Btor2, CPAchecker, Witness-Based Validation}, annote = {An extended abstract of the dissertation project. Submitted to the Doctoral Symposium of FM 2024.}, funding = {DFG-CONVEY}, institution = {LMU Munich}, poster = {https://www.sosy-lab.org/research/pst/2024-09-10_FM24_Bridging_Hardware_and_Software_Formal_Verification_Poster.pdf}, }
   Additional Infos

   An extended abstract of the dissertation project. Submitted to the Doctoral Symposium of FM 2024.

8. Nils Sirrenberg. Certifying Software Violation Witnesses for Hardware Verification Tasks via Simulation-Based Validation. Bachelor's Thesis, LMU Munich, Software Systems Lab, 2024. Keyword(s): Btor2, Witness-Based Validation PDF
   BibTeX Entry

   @misc{SirrenbergBtor2ViolationWitness, author = {Nils Sirrenberg}, title = {Certifying Software Violation Witnesses for Hardware Verification Tasks via Simulation-Based Validation}, year = {2024}, pdf = {https://www.sosy-lab.org/research/bsc/2024.Sirrenberg.Certifying_Software_Violation_Witnesses_for_Hardware_Verification_Tasks_via_Simulation-Based_Validation.restricted.pdf}, keyword = {Btor2, Witness-Based Validation}, field = {Computer Science}, howpublished = {Bachelor's Thesis, LMU Munich, Software Systems Lab}, }
9. Salih Ates. Improving the Encoding of Arrays in Btor2-to-C Translation. Bachelor's Thesis, LMU Munich, Software Systems Lab, 2023. Keyword(s): Btor2, Arrays PDF Presentation
   BibTeX Entry

   @misc{AtesBtor2CArray, author = {Salih Ates}, title = {Improving the Encoding of Arrays in Btor2-to-C Translation}, year = {2023}, pdf = {https://www.sosy-lab.org/research/bsc/2023.Ates.Improving_the_Encoding_of_Arrays_in_Btor2-to-C_Translation.pdf}, presentation = {https://www.sosy-lab.org/research/prs/2023-08-30_BA_Improving_the_Encoding_of_Arrays_in_Btor2-to-C_Translation_Salih_Ates.pdf}, keyword = {Btor2, Arrays}, field = {Computer Science}, howpublished = {Bachelor's Thesis, LMU Munich, Software Systems Lab}, }