| Guidelines for quality assurance of machine learning-based artificial intelligence G Fujii, K Hamada, F Ishikawa, S Masuda, M Matsuya, T Myojin, Y Nishi, ... International journal of software engineering and knowledge engineering 30 …, 2020 | 62 | 2020 |
| Comparative studies on the Tyzzer's organisms from rats and mice F Kosaku The Japanese journal of experimental medicine 41 (2), 125-133, 1971 | 28 | 1971 |
| A test architecture for machine learning product Y Nishi, S Masuda, H Ogawa, K Uetsuki 2018 IEEE International Conference on Software Testing, Verification and …, 2018 | 27 | 2018 |
| Formal verification of a decision-tree ensemble model and detection of its violation ranges N Sato, H Kuruma, Y Nakagawa, H Ogawa IEICE TRANSACTIONS on Information and Systems 103 (2), 363-378, 2020 | 18 | 2020 |
| A rule-based automated approach for extracting models from source code M Ichii, T Myojin, Y Nakagawa, M Chikahisa, H Ogawa 2012 19th Working Conference on Reverse Engineering, 308-317, 2012 | 16 | 2012 |
| Model checking process with goal oriented requirements analysis H Ogawa, F Kumeno, S Honiden 2008 15th Asia-Pacific Software Engineering Conference, 377-384, 2008 | 13 | 2008 |
| Case study of applying SPLE to development of network switch products T Kato, M Kawakami, T Myojin, H Ogawa, K Hirono, T Hasegawa Proceedings of the 17th International Software Product Line Conference, 198-207, 2013 | 9 | 2013 |
| A practical study of debugging using model checking H Ogawa, M Ichii, F Kumeno, T Aoki 2013 20th Asia-Pacific Software Engineering Conference (APSEC) 2, 134-139, 2013 | 7 | 2013 |
| Formal verification of decision-tree ensemble model and detection of its violating-input-value ranges N Sato, H Kuruma, Y Nakagawa, H Ogawa arXiv preprint arXiv:1904.11753, 2019 | 6 | 2019 |
| Pathological studies on corynebacterial ulcerative entero-colitis in rats treated with ACTH (author's transl) A Yamada, H Ogawa Jikken dobutsu. Experimental Animals 24 (4), 151-160, 1975 | 5 | 1975 |
| Experimental fault analysis process implemented using model extraction and model checking H Ogawa, M Ichii, F Kumeno, T Aoki 2015 IEEE 39th Annual Computer Software and Applications Conference 2, 95-104, 2015 | 3 | 2015 |
| Feature-analysis-based selection method for system configuration for system testing D Shimbara, H Watanabe, S Kakushi, M Kawakami, H Ogawa 2012 Third International Workshop on Product LinE Approaches in Software …, 2012 | 3 | 2012 |
| Data-creation assistance apparatus and data-creation assistance method T Myojin, H Kuruma, N Sato, H Ogawa US Patent App. 17/550,285, 2022 | 2 | 2022 |
| Policing functions for machine learning systems TS Hoang, N Sato, T Myosin, M Butler, Y Nakagawa, H Ogawa | 2 | 2018 |
| Refactoring verification using model transformation M Ichii, D Shimbara, Y Suzuki, H Ogawa Proceedings of the 1st International Workshop on Software Refactoring, 17-24, 2016 | 2 | 2016 |
| Higher-order AMR based on IDO scheme for fluid-structure interaction K Fujiwara, T Aoki, H Ogawa CD-ROM WCCM VI in conjunction with APCOM 4, 2004 | 2 | 2004 |
| Unsupposable Test-data Generation for Machine-learned Software N Sato, H Kuruma, H Ogawa arXiv preprint arXiv:2005.10442, 2020 | 1 | 2020 |
| DeepSaucer: Unified Environment for Verifying Deep Neural Networks N Sato, H Kuruma, M Kaneko, Y Nakagawa, H Ogawa, TS Hoang, ... arXiv preprint arXiv:1811.03752, 2018 | 1 | 2018 |
| Simplified Influence Evaluation of Additional Training on Deep Neural Networks. N Sato, H Kuruma, Y Nakagawa, H Ogawa WESPr-iMLSE@ APSEC, 34-39, 2018 | 1 | 2018 |
| Software test apparatus and software test method H Kuruma, N Sato, T Myojin, H Ogawa, M Ishikawa US Patent 11,914,507, 2024 | | 2024 |
