@misc{7628, author = {{Selbach, Nils}}, publisher = {{Universität Paderborn}}, title = {{{Modeling Crypto API usages in OpenSSL's EVP library}}}, year = {{2019}}, } @inproceedings{7635, author = {{Sharma, Arnab and Wehrheim, Heike}}, booktitle = {{IEEE International Conference on Software Testing, Verification and Validation (ICST)}}, location = {{Xi'an, China, April, 2019}}, pages = {{125----135}}, publisher = {{IEEE}}, title = {{{Testing Machine Learning Algorithms for Balanced Data Usage}}}, year = {{2019}}, } @article{8424, abstract = {{The vision of On-the-Fly (OTF) Computing is to compose and provide software services ad hoc, based on requirement descriptions in natural language. Since non-technical users write their software requirements themselves and in unrestricted natural language, deficits occur such as inaccuracy and incompleteness. These deficits are usually met by natural language processing methods, which have to face special challenges in OTF Computing because maximum automation is the goal. In this paper, we present current automatic approaches for solving inaccuracies and incompletenesses in natural language requirement descriptions and elaborate open challenges. In particular, we will discuss the necessity of domain-specific resources and show why, despite far-reaching automation, an intelligent and guided integration of end users into the compensation process is required. In this context, we present our idea of a chat bot that integrates users into the compensation process depending on the given circumstances. }}, author = {{Bäumer, Frederik Simon and Kersting, Joschka and Geierhos, Michaela}}, issn = {{2073-431X}}, journal = {{Computers}}, keywords = {{Inaccuracy Detection, Natural Language Software Requirements, Chat Bot}}, location = {{Vilnius, Lithuania}}, number = {{1}}, publisher = {{MDPI AG, Basel, Switzerland}}, title = {{{Natural Language Processing in OTF Computing: Challenges and the Need for Interactive Approaches}}}, doi = {{10.3390/computers8010022}}, volume = {{8}}, year = {{2019}}, } @inproceedings{8868, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke and Hetzer, Alexander}}, location = {{Bayreuth, Germany}}, title = {{{Towards Automated Machine Learning for Multi-Label Classification}}}, year = {{2019}}, } @inproceedings{15838, abstract = {{In the field of software analysis a trade-off between scalability and accuracy always exists. In this respect, Android app analysis is no exception, in particular, analyzing large or many apps can be challenging. Dealing with many small apps is a typical challenge when facing micro-benchmarks such as DROIDBENCH or ICC-BENCH. These particular benchmarks are not only used for the evaluation of novel tools but also in continuous integration pipelines of existing mature tools to maintain and guarantee a certain quality-level. Considering this latter usage it becomes very important to be able to achieve benchmark results as fast as possible. Hence, benchmarks have to be optimized for this purpose. One approach to do so is app merging. We implemented the Android Merge Tool (AMT) following this approach and show that its novel aspects can be used to produce scaled up and accurate benchmarks. For such benchmarks Android app analysis tools do not suffer from the scalability-accuracy trade-off anymore. We show this throughout detailed experiments on DROIDBENCH employing three different analysis tools (AMANDROID, ICCTA, FLOWDROID). Benchmark execution times are largely reduced without losing benchmark accuracy. Moreover, we argue why AMT is an advantageous successor of the state-of-the-art app merging tool (APKCOMBINER) in analysis lift-up scenarios.}}, author = {{Pauck, Felix and Zhang, Shikun}}, booktitle = {{2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW)}}, isbn = {{9781728141367}}, keywords = {{Program Analysis, Android App Analysis, Taint Analysis, App Merging, Benchmark}}, title = {{{Android App Merging for Benchmark Speed-Up and Analysis Lift-Up}}}, doi = {{10.1109/asew.2019.00019}}, year = {{2019}}, } @misc{15920, abstract = {{Secure hardware design is the most important aspect to be considered in addition to functional correctness. Achieving hardware security in today’s globalized Integrated Cir- cuit(IC) supply chain is a challenging task. One solution that is widely considered to help achieve secure hardware designs is Information Flow Tracking(IFT). It provides an ap- proach to verify that the systems adhere to security properties either by static verification during design phase or dynamic checking during runtime. Proof-Carrying Hardware(PCH) is an approach to verify a functional design prior to using it in hardware. It is a two-party verification approach, where the target party, the consumer requests new functionalities with pre-defined properties to the producer. In response, the producer designs the IP (Intellectual Property) cores with the requested functionalities that adhere to the consumer-defined properties. The producer provides the IP cores and a proof certificate combined into a proof-carrying bitstream to the consumer to verify it. If the verification is successful, the consumer can use the IP cores in his hardware. In essence, the consumer can only run verified IP cores. Correctly applied, PCH techniques can help consumers to defend against many unintentional modifications and malicious alterations of the modules they receive. There are numerous published examples of how to use PCH to detect any change in the functionality of a circuit, i.e., pairing a PCH approach with functional equivalence checking for combinational or sequential circuits. For non-functional properties, since opening new covert channels to leak secret information from secure circuits is a viable attack vector for hardware trojans, i.e., intentionally added malicious circuitry, IFT technique is employed to make sure that secret/untrusted information never reaches any unclassified/trusted outputs. This master thesis aims to explore the possibility of adapting Information Flow Tracking into a Proof-Carrying Hardware scenario. It aims to create a method that combines Infor- mation Flow Tracking(IFT) with a PCH approach at bitstream level enabling consumers to validate the trustworthiness of a module’s information flow without the computational costs of a complete flow analysis.}}, author = {{Keerthipati, Monica}}, publisher = {{Universität Paderborn}}, title = {{{A Bitstream-Level Proof-Carrying Hardware Technique for Information Flow Tracking}}}, year = {{2019}}, } @article{14896, author = {{Dann, Andreas and Hermann, Ben and Bodden, Eric}}, issn = {{0098-5589}}, journal = {{IEEE Transactions on Software Engineering}}, pages = {{1--1}}, title = {{{ModGuard: Identifying Integrity &Confidentiality Violations in Java Modules}}}, doi = {{10.1109/tse.2019.2931331}}, year = {{2019}}, } @inproceedings{15007, author = {{Melnikov, Vitaly and Hüllermeier, Eyke}}, booktitle = {{Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)}}, title = {{{Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA}}}, doi = {{10.1016/j.jmva.2019.02.017}}, year = {{2019}}, } @inproceedings{15011, author = {{Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019}}, editor = {{Hoffmann, Frank and Hüllermeier, Eyke and Mikut, Ralf}}, isbn = {{978-3-7315-0979-0}}, location = {{Dortmund}}, pages = {{135--146}}, publisher = {{KIT Scientific Publishing, Karlsruhe}}, title = {{{Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking}}}, year = {{2019}}, } @phdthesis{13126, author = {{Khaluf, Lial}}, publisher = {{Universität Paderborn}}, title = {{{Organic Programming of Dynamic Real-Time Applications}}}, year = {{2019}}, } @inproceedings{13132, author = {{Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke}}, booktitle = {{INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}}, location = {{Kassel}}, pages = {{ 273--274 }}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{From Automated to On-The-Fly Machine Learning}}}, year = {{2019}}, } @misc{13435, author = {{Friesen, Edwin}}, publisher = {{Universität Paderborn}}, title = {{{Requirements Engineering im OTF-Computing: Informationsextraktion und Unvollständigkeitskompensation mittels domänenspezifischer Wissensbasis}}}, year = {{2019}}, } @inproceedings{10093, author = {{Beyer, Dirk and Jakobs, Marie-Christine and Lemberger, Thomas and Wehrheim, Heike}}, booktitle = {{Software Engineering and Software Management (SE/SWM 2019), Stuttgart, Germany, February 18-22, 2019}}, editor = {{Becker, Steffen and Bogicevic, Ivan and Herzwurm, Georg and Wagner, Stefan}}, pages = {{151----152}}, publisher = {{GI}}, title = {{{Combining Verifiers in Conditional Model Checking via Reducers}}}, doi = {{10.18420/se2019-46}}, volume = {{P-292}}, year = {{2019}}, } @inproceedings{10095, author = {{Richter, Cedric and Wehrheim, Heike}}, booktitle = {{Tools and Algorithms for the Construction and Analysis of Systems - 25 Years of {TACAS:} TOOLympics, Held as Part of {ETAPS} 2019, Prague, Czech Republic, April 6-11, 2019, Proceedings, Part {III}}}, editor = {{Beyer, Dirk and Huisman, Marieke and Kordon, Fabrice and Steffen, Bernhard}}, pages = {{229--233}}, publisher = {{Springer}}, title = {{{PeSCo: Predicting Sequential Combinations of Verifiers - (Competition Contribution)}}}, doi = {{10.1007/978-3-030-17502-3_19}}, volume = {{11429}}, year = {{2019}}, } @misc{10105, author = {{Haltermann, Jan}}, publisher = {{Universität Paderborn}}, title = {{{Analyzing Data Usage in Array Programs}}}, year = {{2019}}, } @inproceedings{10232, abstract = {{Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn, and more recently ML-Plan, have shown impressive results for the tasks of single-label classification and regression. Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Tornede, Alexander and Hüllermeier, Eyke}}, location = {{Long Beach, CA, USA}}, title = {{{Automating Multi-Label Classification Extending ML-Plan}}}, year = {{2019}}, } @inproceedings{7626, author = {{Schubert, Philipp and Hermann, Ben and Bodden, Eric}}, booktitle = {{Proceedings of the 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2019), Held as Part of the European Joint Conferences on Theory and Practice of Software (ETAPS 2019)}}, location = {{Prague, Czech Republic}}, pages = {{393--410}}, title = {{{PhASAR: An Inter-Procedural Static Analysis Framework for C/C++}}}, doi = {{10.1007/978-3-030-17465-1_22}}, volume = {{II}}, year = {{2019}}, } @inproceedings{13259, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Hagen, Matthias and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom}}, pages = {{76--82}}, title = {{{Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition}}}, year = {{2019}}, } @inproceedings{10108, abstract = {{Recent years have seen the development of numerous tools for the analysis of taint flows in Android apps. Taint analyses aim at detecting data leaks, accidentally or by purpose programmed into apps. Often, such tools specialize in the treatment of specific features impeding precise taint analysis (like reflection or inter-app communication). This multitude of tools, their specific applicability and their various combination options complicate the selection of a tool (or multiple tools) when faced with an analysis instance, even for knowledgeable users, and hence hinders the successful adoption of taint analyses. In this work, we thus present CoDiDroid, a framework for cooperative Android app analysis. CoDiDroid (1) allows users to ask questions about flows in apps in varying degrees of detail, (2) automatically generates subtasks for answering such questions, (3) distributes tasks onto analysis tools (currently DroidRA, FlowDroid, HornDroid, IC3 and two novel tools) and (4) at the end merges tool answers on subtasks into an overall answer. Thereby, users are freed from having to learn about the use and functionality of all these tools while still being able to leverage their capabilities. Moreover, we experimentally show that cooperation among tools pays off with respect to effectiveness, precision and scalability.}}, author = {{Pauck, Felix and Wehrheim, Heike}}, booktitle = {{Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering}}, isbn = {{978-1-4503-5572-8}}, keywords = {{Android Taint Analysis, Cooperation, Precision, Tools}}, pages = {{374--384}}, title = {{{Together Strong: Cooperative Android App Analysis}}}, doi = {{10.1145/3338906.3338915}}, year = {{2019}}, } @inproceedings{13874, author = {{Isenberg, Tobias and Jakobs, Marie-Christine and Pauck, Felix and Wehrheim, Heike}}, booktitle = {{Tests and Proofs - 13th International Conference, {TAP} 2019, Held as Part of the Third World Congress on Formal Methods 2019, Porto, Portugal, October 9-11, 2019, Proceedings}}, pages = {{3--20}}, title = {{{When Are Software Verification Results Valid for Approximate Hardware?}}}, doi = {{10.1007/978-3-030-31157-5_1}}, year = {{2019}}, }