{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T04:19:18Z","timestamp":1759033158029,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T00:00:00Z","timestamp":1593388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,6,29]]},"DOI":"10.1145\/3387939.3388613","type":"proceedings-article","created":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T02:13:13Z","timestamp":1600481593000},"page":"31-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Towards classes of architectural dependability assurance for machine-learning-based systems"],"prefix":"10.1145","author":[{"given":"Max","family":"Scheerer","sequence":"first","affiliation":[{"name":"FZI Research Center for Information Technology, Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonas","family":"Klamroth","sequence":"additional","affiliation":[{"name":"FZI Research Center for Information Technology, Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ralf","family":"Reussner","sequence":"additional","affiliation":[{"name":"FZI Research Center for Information Technology, Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernhard","family":"Beckert","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,9,18]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output. arXiv preprint arXiv:1910.10307","author":"Abdelzad Vahdat","year":"2019","unstructured":"Vahdat Abdelzad, Krzysztof Czarnecki, Rick Salay, Taylor Denounden, Sachin Vernekar, and Buu Phan. 2019. Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output. arXiv preprint arXiv:1910.10307 (2019)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_2_1","DOI":"10.1109\/ICAS.2008.35"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_3_1","DOI":"10.1145\/2465478.2465489"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.1080\/01621459.2017.1285773"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.5555\/2666795.2666805"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.1109\/ICCV.2015.312"},{"key":"e_1_3_2_1_7_1","volume-title":"Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 300--303","author":"Cheng Chih-Hong","year":"2019","unstructured":"Chih-Hong Cheng, Georg N\u00fchrenberg, and Hirotoshi Yasuoka. 2019. Runtime monitoring neuron activation patterns. In 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 300--303."},{"doi-asserted-by":"crossref","unstructured":"Rogerio de Lemos and Marek Grzes. 2019. Self-adaptive Artificial Intelligence. (2019).","key":"e_1_3_2_1_8_1","DOI":"10.1109\/SEAMS.2019.00028"},{"unstructured":"Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. (2017).","key":"e_1_3_2_1_9_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_10_1","DOI":"10.1007\/s10817-018-09509-5"},{"volume-title":"Uncertainty in self-adaptive software systems","author":"Esfahani Naeem","unstructured":"Naeem Esfahani and Sam Malek. 2013. Uncertainty in self-adaptive software systems. In Software Engineering for Self-Adaptive Systems II. Springer, 214--238.","key":"e_1_3_2_1_11_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_12_1","DOI":"10.1109\/SEAMS.2017.19"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1109\/ICRA.2017.7989385"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_14_1","DOI":"10.1145\/3302509.3311038"},{"key":"e_1_3_2_1_15_1","volume-title":"A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51, 5","author":"Guidotti Riccardo","year":"2018","unstructured":"Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018. A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51, 5 (2018), 93."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_16_1","DOI":"10.1109\/IOLTS.2018.8474192"},{"key":"e_1_3_2_1_17_1","volume-title":"Muhammad Abdullah Hanif, and Muhammad Shafique","author":"Hoang Le-Ha","year":"2019","unstructured":"Le-Ha Hoang, Muhammad Abdullah Hanif, and Muhammad Shafique. 2019. FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation. arXiv preprint arXiv:1912.00941 (2019)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1109\/SEAMS.2017.21"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.1007\/978-3-319-63387-9_5"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_20_1","DOI":"10.1007\/978-3-030-25540-4_26"},{"volume-title":"Probabilistic graphical models: principles and techniques","author":"Koller Daphne","unstructured":"Daphne Koller and Nir Friedman. 2009. Probabilistic graphical models: principles and techniques. MIT press.","key":"e_1_3_2_1_21_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_22_1","DOI":"10.1145\/1712605.1712624"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_23_1","DOI":"10.1145\/2786805.2786853"},{"unstructured":"Christian Murphy and Gail E Kaiser. 2008. Improving the dependability of machine learning applications. (2008).","key":"e_1_3_2_1_24_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_25_1","DOI":"10.1145\/3052973.3053009"},{"key":"e_1_3_2_1_26_1","first-page":"686","article-title":"A combined simulation and test case generation strategy for self-adaptive systems","volume":"7","author":"P\u00fcschel Georg","year":"2014","unstructured":"Georg P\u00fcschel, Christian Piechnick, Sebastian G\u00f6tz, Christoph Seidl, Sebastian Richly, Thomas Schlegel, and Uwe A\u00dfmann. 2014. A combined simulation and test case generation strategy for self-adaptive systems. Journal On Advances in Software 7, 3&4 (2014), 686--696.","journal-title":"Journal On Advances in Software"},{"unstructured":"Markus Schumacher Eduardo Fernandez-Buglioni Duane Hybertson FrankBuschmann and Peter Sommerlad. 2006. Security Patterns - Integrating Security and Systems Engineering.","key":"e_1_3_2_1_27_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_28_1","DOI":"10.1109\/ICSA-C.2019.00051"},{"key":"e_1_3_2_1_29_1","volume-title":"Towards verified artificial intelligence. arXiv preprint arXiv:1606.08514","author":"Seshia Sanjit A","year":"2016","unstructured":"Sanjit A Seshia, Dorsa Sadigh, and S Shankar Sastry. 2016. Towards verified artificial intelligence. arXiv preprint arXiv:1606.08514 (2016)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_30_1","DOI":"10.1007\/978-3-319-99229-7_39"},{"volume-title":"Reinforcement learning: An introduction","author":"Sutton Richard S","unstructured":"Richard S Sutton and Andrew G Barto. 2018. Reinforcement learning: An introduction. MIT press.","key":"e_1_3_2_1_31_1"},{"key":"e_1_3_2_1_32_1","volume-title":"Expectation-Maximization methods for solving (PO) MDPs and optimal control problems. Inference and Learning in Dynamic Models","author":"Toussaint Marc","year":"2010","unstructured":"Marc Toussaint, Amos Storkey, and Stefan Harmeling. 2010. Expectation-Maximization methods for solving (PO) MDPs and optimal control problems. Inference and Learning in Dynamic Models (2010)."},{"key":"e_1_3_2_1_33_1","volume-title":"On the safety of machine learning: Cyber-physical systems, decision sciences, and data products. Big data 5, 3","author":"Varshney Kush R","year":"2017","unstructured":"Kush R Varshney and Homa Alemzadeh. 2017. On the safety of machine learning: Cyber-physical systems, decision sciences, and data products. Big data 5, 3 (2017), 246--255."},{"key":"e_1_3_2_1_34_1","volume-title":"Viviana Meruane, and Mohammad Modarres.","author":"Verstraete David","year":"2017","unstructured":"David Verstraete, Andr\u00e9s Ferrada, Enrique L\u00f3pez Droguett, Viviana Meruane, and Mohammad Modarres. 2017. Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings. Shock and Vibration 2017 (2017)."},{"unstructured":"Shiqi Wang Kexin Pei Justin Whitehouse Junfeng Yang and Suman Jana. 2018. Efficient formal safety analysis of neural networks. In Advances in Neural Information Processing Systems. 6367--6377.","key":"e_1_3_2_1_35_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_36_1","DOI":"10.1145\/2338966.2336803"},{"key":"e_1_3_2_1_37_1","volume-title":"Four dark corners of requirements engineering. ACM transactions on Software Engineering and Methodology (TOSEM) 6, 1","author":"Zave Pamela","year":"1997","unstructured":"Pamela Zave and Michael Jackson. 1997. Four dark corners of requirements engineering. ACM transactions on Software Engineering and Methodology (TOSEM) 6, 1 (1997), 1--30."},{"key":"e_1_3_2_1_38_1","volume-title":"Perturbed Model Validation: A New Framework to Validate Model Relevance. arXiv preprint arXiv:1905.10201","author":"Zhang Jie M","year":"2019","unstructured":"Jie M Zhang, Earl T Barr, Benjamin Guedj, Mark Harman, and John Shawe-Taylor. 2019. Perturbed Model Validation: A New Framework to Validate Model Relevance. arXiv preprint arXiv:1905.10201 (2019)."}],"event":{"sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS"],"acronym":"SEAMS '20","name":"SEAMS '20: IEEE\/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","location":"Seoul Republic of Korea"},"container-title":["Proceedings of the IEEE\/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3387939.3388613","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3387939.3388613","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:42Z","timestamp":1750200102000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3387939.3388613"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,29]]},"references-count":38,"alternative-id":["10.1145\/3387939.3388613","10.1145\/3387939"],"URL":"https:\/\/doi.org\/10.1145\/3387939.3388613","relation":{},"subject":[],"published":{"date-parts":[[2020,6,29]]},"assertion":[{"value":"2020-09-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}