Publications of Marian Lingsch-Rosenfeld
Articles in conference or workshop proceedings
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CPAchecker 4.0 as Witness Validator (Competition Contribution).
In A. Gurfinkel and
M. Heule, editors,
Proceedings of the 31th International Conference on
Tools and Algorithms for the Construction and Analysis of Systems
(TACAS 2025, Hamilton, Canada, May 3-8), part 3,
LNCS 15698,
pages 192-198,
2025.
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)
Abstract
CPAchecker is a tool for software verification, witness validation, and test-case generation, based on the concept of configurable program analysis. One of its main applications is to validate correctness and violation witnesses in versions 1.0 and 2.0. The witness validation is achieved by strengthening a selection of verification algorithms using the information from the witness. Due to the modular approach of CPAchecker, extending its verification analyses for witness validation can be easily done. Similar to CPAchecker's verification approach, witness validation uses a selection of analyses dependent on the witness type, the specification, and program features. To validate correctness witnesses, CPAchecker uses įnduction and predicate abstraction to verify that the invariants from the witness hold and the correctness of the program can be proven. To validate violation witnesses, CPAchecker uses predicate abstraction, value analysis, SMGs, and BDDs. CPAchecker's many verification algorithms make it a versatile and successful tool for witness validation.BibTeX Entry
@inproceedings{TACAS25b, author = {Dirk Beyer and Marian Lingsch-Rosenfeld}, title = {{CPAchecker} 4.0 as Witness Validator (Competition Contribution)}, booktitle = {Proceedings of the 31th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS~2025, Hamilton, Canada, May 3-8), part~3}, editor = {A.~Gurfinkel and M.~Heule}, pages = {192-198}, year = {2025}, series = {LNCS~15698}, publisher = {Springer}, doi = {10.1007/978-3-031-57256-2_21}, sha256 = {}, url = {https://cpachecker.sosy-lab.org/}, abstract = {CPAchecker is a tool for software verification, witness validation, and test-case generation, based on the concept of configurable program analysis. One of its main applications is to validate correctness and violation witnesses in versions 1.0 and 2.0. The witness validation is achieved by strengthening a selection of verification algorithms using the information from the witness. Due to the modular approach of CPAchecker, extending its verification analyses for witness validation can be easily done. Similar to CPAchecker's verification approach, witness validation uses a selection of analyses dependent on the witness type, the specification, and program features. To validate correctness witnesses, CPAchecker uses \kinduction and predicate abstraction to verify that the invariants from the witness hold and the correctness of the program can be proven. To validate violation witnesses, CPAchecker uses predicate abstraction, value analysis, SMGs, and BDDs. CPAchecker's many verification algorithms make it a versatile and successful tool for witness validation.}, keyword = {Software Model Checking, Witness-Based Validation, CPAchecker}, _pdf = {https://www.sosy-lab.org/research/pub/2025-TACAS.CPAchecker_4.0_as_Witness_Validator.pdf}, artifact = {10.5281/zenodo.14203369}, funding = {DFG-CONVEY, DFG-IDEFIX}, } -
Towards Automatic Structured Inference of Module Abstractions.
In Festschrift for Prof. Wolfgang Reif,
2025.
(To appear)
BibTeX Entry
@inproceedings{ernst:reif65, author = {Gidon Ernst and Marian Lingsch-Rosenfeld}, title = {Towards Automatic Structured Inference of Module Abstractions}, booktitle = {Festschrift for Prof. Wolfgang Reif}, year = {2025}, note = {(To appear)}, } -
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)
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}, sha256 = {c60d4d261f259284784012dad8cc7a80ee22841bccf3013bc238adba7229de23}, 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. -
P3: A Dataset of Partial Program Patches.
In Proc. MSR,
2024.
ACM.
doi:10.1145/3643991.3644889
Keyword(s):
Partial Fix,
Dataset,
Mining
Funding:
DFG-IDEFIX
Publisher's Version
PDF
Supplement
Artifact(s)
Abstract
Identifying and fixing bugs in programs remains a challenge and is one of the most time-consuming tasks in software development. But even after a bug is identified, and a fix has been proposed by a developer or tool, it is not uncommon that the fix is incomplete and does not cover all possible inputs that trigger the bug. This can happen quite often and leads to re-opened issues and inefficiencies. In this paper, we introduce P3, a curated dataset composed of in- complete fixes. Each entry in the set contains a series of commits fixing the same underlying issue, where multiple of the intermediate commits are incomplete fixes. These are sourced from real-world open-source C projects. The selection process involves both auto- mated and manual stages. Initially, we employ heuristics to identify potential partial fixes from repositories, subsequently we validate them through meticulous manual inspection. This process ensures the accuracy and reliability of our curated dataset. We envision that the dataset will support researchers while investigating par- tial fixes in more detail, allowing them to develop new techniques to detect and fix them.BibTeX Entry
@inproceedings{MSR24, author = {Dirk Beyer and Lars Grunske and Matthias Kettl and Marian Lingsch-Rosenfeld and Moeketsi Raselimo}, title = {P3: A Dataset of Partial Program Patches}, booktitle = {Proc.\ MSR}, pages = {}, year = {2024}, publisher = {ACM}, doi = {10.1145/3643991.3644889}, sha256 = {56954209ffff83bf7c77824a46a4bd2fa4ff52173bea5a1c3fe2b5504f19e6ef}, url = {https://gitlab.com/sosy-lab/research/data/partial-fix-dataset}, pdf = {}, abstract = {Identifying and fixing bugs in programs remains a challenge and is one of the most time-consuming tasks in software development. But even after a bug is identified, and a fix has been proposed by a developer or tool, it is not uncommon that the fix is incomplete and does not cover all possible inputs that trigger the bug. This can happen quite often and leads to re-opened issues and inefficiencies. In this paper, we introduce P3, a curated dataset composed of in- complete fixes. Each entry in the set contains a series of commits fixing the same underlying issue, where multiple of the intermediate commits are incomplete fixes. These are sourced from real-world open-source C projects. The selection process involves both auto- mated and manual stages. Initially, we employ heuristics to identify potential partial fixes from repositories, subsequently we validate them through meticulous manual inspection. This process ensures the accuracy and reliability of our curated dataset. We envision that the dataset will support researchers while investigating par- tial fixes in more detail, allowing them to develop new techniques to detect and fix them.}, keyword = {Partial Fix, Dataset, Mining}, annote = {}, artifact = {10.5281/zenodo.10319627}, funding = {DFG-IDEFIX}, } -
Software Verification Witnesses 2.0.
In Proceedings of the 30th International Symposium on Model Checking Software (SPIN 2024, Luxembourg City, Luxembourg, April 10-11),
LNCS 14624,
pages 184-203,
2024.
Springer.
doi:10.1007/978-3-031-66149-5_11
Keyword(s):
Software Model Checking,
Cooperative Verification,
Witness-Based Validation,
Witness-Based Validation (main),
CPAchecker
Funding:
DFG-CONVEY,
DFG-IDEFIX
Publisher's Version
PDF
Presentation
Supplement
Artifact(s)
BibTeX Entry
@inproceedings{SPIN24a, author = {Paulína Ayaziová and Dirk Beyer and Marian Lingsch-Rosenfeld and Martin Spiessl and Jan Strejček}, title = {Software Verification Witnesses 2.0}, booktitle = {Proceedings of the 30th International Symposium on Model Checking Software (SPIN~2024, Luxembourg City, Luxembourg, April 10-11)}, pages = {184-203}, year = {2024}, series = {LNCS~14624}, publisher = {Springer}, doi = {10.1007/978-3-031-66149-5_11}, sha256 = {945f1eb9f3b893d5ca96156bd92a7e7cd3cfa894a998e304ddea1190ce7ccf36}, url = {https://gitlab.com/sosy-lab/benchmarking/sv-witnesses/}, pdf = {https://www.sosy-lab.org/research/pub/2024-SPIN.Software_Verification_Witnesses_2.0.pdf}, presentation = {https://www.sosy-lab.org/research/prs/2024-04-11_SPIN24_Software-Verification-Witnesses-2.0.pdf}, abstract = {}, keyword = {Software Model Checking, Cooperative Verification, Witness-Based Validation, Witness-Based Validation (main), CPAchecker}, annote = {}, artifact = {10.5281/zenodo.10826204}, funding = {DFG-CONVEY,DFG-IDEFIX}, } -
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)
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}, sha256 = {748017d9908b910755c20903e4f1c4b425cd2c6d958bda9038cf7546a4e662a4}, 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}, } -
CEGAR-PT: A Tool for Abstraction by Program Transformation.
In Proc. ASE,
pages 2078-2081,
2023.
IEEE.
doi:10.1109/ASE56229.2023.00215
Keyword(s):
Software Model Checking
Funding:
DFG-CONVEY
Publisher's Version
PDF
Video
Supplement
Artifact(s)
Abstract
Abstraction is a key technology for proving the correctness of computer programs. There are many approaches available, but unfortunately, the various techniques are difficult to combine and the successful techniques have to be re-implemented again and again.
We address this problem by using the tool CEGAR-PT, which views abstraction as program transformation and integrates different verification components off-the-shelf. The idea is to use existing components without having to change their implementation, while still adjusting the precision of the abstraction using the successful CEGAR approach. The approach is largely general: it only restricts the abstraction to transform, given a precision that defines the level of abstraction, one program into another program. The abstraction by program transformation can over-approximate the data flow (e.g., havoc some variables, use more abstract types) or the control flow (e.g., loop abstraction, slicing).BibTeX Entry
@inproceedings{ASE23c, author = {Dirk Beyer and Marian Lingsch-Rosenfeld and Martin Spiessl}, title = {{CEGAR-PT}: {A} Tool for Abstraction by Program Transformation}, booktitle = {Proc.\ ASE}, pages = {2078-2081}, year = {2023}, series = {}, publisher = {IEEE}, doi = {10.1109/ASE56229.2023.00215}, url = {https://www.sosy-lab.org/research/cegar-pt}, pdf = {https://www.sosy-lab.org/research/pub/2023-ASE.CEGAR-PT_A_Tool_for_Abstraction_by_Program_Transformation.pdf}, abstract = {Abstraction is a key technology for proving the correctness of computer programs. There are many approaches available, but unfortunately, the various techniques are difficult to combine and the successful techniques have to be re-implemented again and again. <br> We address this problem by using the tool CEGAR-PT, which views abstraction as program transformation and integrates different verification components off-the-shelf. The idea is to use existing components without having to change their implementation, while still adjusting the precision of the abstraction using the successful CEGAR approach. The approach is largely general: it only restricts the abstraction to transform, given a precision that defines the level of abstraction, one program into another program. The abstraction by program transformation can over-approximate the data flow (e.g., havoc some variables, use more abstract types) or the control flow (e.g., loop abstraction, slicing).}, keyword = {Software Model Checking}, artifact = {10.5281/zenodo.8287183}, funding = {DFG-CONVEY}, video = {https://youtu.be/ASZ6hoq8asE}, }
Internal reports
-
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)
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.
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