TY - CONF AU - Richter, Cedric AU - Haltermann, Jan Frederik AU - Jakobs, Marie-Christine AU - Pauck, Felix AU - Schott, Stefan AU - Wehrheim, Heike ID - 35426 T2 - 37th IEEE/ACM International Conference on Automated Software Engineering TI - Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs? ER - TY - CONF AU - Schott, Stefan AU - Pauck, Felix ID - 36848 T2 - 2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM) TI - Benchmark Fuzzing for Android Taint Analyses ER - TY - CONF AU - Pauck, Felix ID - 35427 T2 - 37th IEEE/ACM International Conference on Automated Software Engineering TI - Scaling Arbitrary Android App Analyses ER - TY - THES AU - Pauck, Felix ID - 43108 TI - Cooperative Android App Analysis ER - TY - THES AU - König, Jürgen ID - 47833 TI - On the Membership and Correctness Problem for State Serializability and Value Opacity ER - TY - CONF AU - Richter, Cedric AU - Wehrheim, Heike ID - 32590 T2 - 2022 IEEE Conference on Software Testing, Verification and Validation (ICST) TI - Learning Realistic Mutations: Bug Creation for Neural Bug Detectors ER - TY - CONF AU - Richter, Cedric AU - Wehrheim, Heike ID - 32591 T2 - 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR) TI - TSSB-3M: Mining single statement bugs at massive scale ER - TY - CONF AU - Dongol, Brijesh AU - Schellhorn, Gerhard AU - Wehrheim, Heike ED - Klin, Bartek ED - Lasota, Slawomir ED - Muscholl, Anca ID - 45248 T2 - 33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland TI - Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement VL - 243 ER - TY - CONF AB - In recent years, we observe an increasing amount of software with machine learning components being deployed. This poses the question of quality assurance for such components: how can we validate whether specified requirements are fulfilled by a machine learned software? Current testing and verification approaches either focus on a single requirement (e.g., fairness) or specialize on a single type of machine learning model (e.g., neural networks). In this paper, we propose property-driven testing of machine learning models. Our approach MLCheck encompasses (1) a language for property specification, and (2) a technique for systematic test case generation. The specification language is comparable to property-based testing languages. Test case generation employs advanced verification technology for a systematic, property dependent construction of test suites, without additional user supplied generator functions. We evaluate MLCheck using requirements and data sets from three different application areas (software discrimination, learning on knowledge graphs and security). Our evaluation shows that despite its generality MLCheck can even outperform specialised testing approaches while having a comparable runtime AU - Sharma, Arnab AU - Demir, Caglar AU - Ngonga Ngomo, Axel-Cyrille AU - Wehrheim, Heike ID - 28350 T2 - Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA) TI - MLCHECK–Property-Driven Testing of Machine Learning Classifiers ER - TY - JOUR AB - Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely documented, which makes it difficult to compare and reproduce the results. To push Android taint analysis research forward, this paper thus recommends criteria for constructing real-world benchmark suites for this specific domain, and presents TaintBench, the first real-world malware benchmark suite with documented taint flows. TaintBench benchmark apps include taint flows with complex structures, and addresses static challenges that are commonly agreed on by the community. Together with the TaintBench suite, we introduce the TaintBench framework, whose goal is to simplify real-world benchmarking of Android taint analyses. First, a usability test shows that the framework improves experts’ performance and perceived usability when documenting and inspecting taint flows. Second, experiments using TaintBench reveal new insights for the taint analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions of both tools are less accurate than their predecessors. AU - Luo, Linghui AU - Pauck, Felix AU - Piskachev, Goran AU - Benz, Manuel AU - Pashchenko, Ivan AU - Mory, Martin AU - Bodden, Eric AU - Hermann, Ben AU - Massacci, Fabio ID - 27045 JF - Empirical Software Engineering SN - 1382-3256 TI - TaintBench: Automatic real-world malware benchmarking of Android taint analyses ER - TY - GEN AU - Schott, Stefan ID - 22304 TI - Android App Analysis Benchmark Case Generation ER - TY - CONF AU - Pauck, Felix AU - Wehrheim, Heike ID - 28199 T2 - 2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM) TI - Jicer: Simplifying Cooperative Android App Analysis Tasks ER - TY - CONF AU - Pauck, Felix AU - Wehrheim, Heike ED - Koziolek, Anne ED - Schaefer, Ina ED - Seidl, Christoph ID - 21238 T2 - Software Engineering 2021 TI - Cooperative Android App Analysis with CoDiDroid ER - TY - CONF AU - Sharma, Arnab AU - Wehrheim, Heike ID - 19656 T2 - Proceedings of the 32th IFIP International Conference on Testing Software and Systems (ICTSS) TI - Automatic Fairness Testing of Machine Learning Models ER - TY - GEN AU - Mayer, Stefan ID - 19999 TI - Optimierung von JMCTest beim Testen von Inter Method Contracts ER - TY - CONF AU - Bila, Eleni AU - Doherty, Simon AU - Dongol, Brijesh AU - Derrick, John AU - Schellhorn, Gerhard AU - Wehrheim, Heike ED - Gotsman, Alexey ED - Sokolova, Ana ID - 20274 T2 - Formal Techniques for Distributed Objects, Components, and Systems - 40th {IFIP} {WG} 6.1 International Conference, {FORTE} 2020, Held as Part of the 15th International Federated Conference on Distributed Computing Techniques, DisCoTec 2020, Valletta, Malta, June 15-19, 2020, Proceedings TI - Defining and Verifying Durable Opacity: Correctness for Persistent Software Transactional Memory VL - 12136 ER - TY - CONF AU - Beringer, Steffen AU - Wehrheim, Heike ED - van Sinderen, Marten ED - Fill, Hans{-}Georg ED - A. Maciaszek, Leszek ID - 20275 T2 - Proceedings of the 15th International Conference on Software Technologies, {ICSOFT} 2020, Lieusaint, Paris, France, July 7-9, 2020 TI - Consistency Analysis of AUTOSAR Timing Requirements ER - TY - CONF AU - Beyer, Dirk AU - Wehrheim, Heike ED - Margaria, Tiziana ED - Steffen, Bernhard ID - 20276 T2 - Leveraging Applications of Formal Methods, Verification and Validation: Verification Principles - 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, October 20-30, 2020, Proceedings, Part {I} TI - Verification Artifacts in Cooperative Verification: Survey and Unifying Component Framework VL - 12476 ER - TY - GEN ED - Wehrheim, Heike ED - Cabot, Jordi ID - 20277 SN - 978-3-030-45233-9 TI - Fundamental Approaches to Software Engineering - 23rd International Conference, FASE 2020, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Dublin, Ireland, April 25-30, 2020, Proceedings VL - 12076 ER - TY - GEN ED - Ahrendt, Wolfgang ED - Wehrheim, Heike ID - 20278 SN - 978-3-030-50994-1 TI - Tests and Proofs - 14th International Conference, TAP@STAF 2020, Bergen, Norway, June 22-23, 2020, Proceedings [postponed] VL - 12165 ER -