Machine Learning Based Test Data Generation for Safety-critical Software
Unit testing focused on Modified Condition/Decision Coverage (MC/DC) criterion is essential in development safety-critical systems. However, design of test data that meets the MC/DC criterion currently needs detailed manual analysis of branching conditions in units under test by test engineers. Multiple state-of-art approaches exist with proven usage even in industrial projects. However, these approaches have multiple shortcomings, one of them being the Path explosion problem which has not been fully solved yet. Machine learning methods as meta-heuristic approximations can model behaviour of programs that are hard to test using traditional approaches, where the Path explosion problem does occur and thus could solve the limitations of the current state-of-art approaches. I believe, motivated by an ongoing collaboration with an industrial partner, that the machine learning methods could be combined with existing approaches to produce an approach suitable for testing of safety-critical projects.
Thu 12 NovDisplayed time zone: (UTC) Coordinated Universal Time change
08:00 - 08:30 | ML Testing 2Journal First / Paper Presentations / Research Papers / Tool Demos / Visions and Reflections at Virtual room 2 | ||
08:00 2mTalk | DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks Research Papers DOI | ||
08:03 1mTalk | Machine Learning Based Test Data Generation for Safety-critical Software Paper Presentations Ján Čegiň Faculty of Informatics and Information Technologies Slovak Technical University | ||
08:05 1mTalk | Machine Learning Testing: Survey, Landscapes and Horizons Journal First Jie M. Zhang University College London, UK, Mark Harman University College London, UK, Lei Ma Kyushu University, Yang Liu Nanyang Technological University, Singapore | ||
08:07 1mTalk | Machine Translation Testing via Pathological Invariance Research Papers Shashij Gupta IIT Bombay, India, Pinjia He ETH Zurich, Switzerland, Clara Meister ETH Zurich, Switzerland, Zhendong Su ETH Zurich DOI | ||
08:09 1mTalk | Model-Based Exploration of the Frontier of Behaviours for Deep Learning System Testing Research Papers DOI | ||
08:11 1mTalk | PRODeep: A Platform for Robustness Verification of Deep Neural Networks Tool Demos Renjue Li Institute of Software at Chinese Academy of Sciences, China, Jianlin Li Institute of Software at Chinese Academy of Sciences, China, Cheng-Chao Huang Institute of Intelligent Software, China, Pengfei Yang Institute of Software at Chinese Academy of Sciences, China, Xiaowei Huang University of Liverpool, Lijun Zhang Institute of Software, Chinese Academy of Sciences, Bai Xue Institute of Software at Chinese Academy of Sciences, China, Holger Hermanns Saarland University DOI | ||
08:13 1mTalk | Testing Machine Learning Code using Polyhedral Region Visions and Reflections Md Sohel Ahmed National Institute of Informatics, Japan, Fuyuki Ishikawa National Institute of Informatics, Mahito Sugiyama National Institute of Informatics, Japan DOI | ||
08:15 15mTalk | Conversations on ML Testing 2 Paper Presentations Fuyuan Zhang MPI-SWS, Germany, Ján Čegiň Faculty of Informatics and Information Technologies Slovak Technical University, Mark Harman University College London, UK, Renjue Li Institute of Software at Chinese Academy of Sciences, China, Shashij Gupta IIT Bombay, India, Vincenzo Riccio USI Lugano, Switzerland, M: Shin Yoo Korea Advanced Institute of Science and Technology | ||