Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection ApproachVirtual
A growing demand is witnessed in both industry and academia for employing Deep Learning (DL) in various domains to solve real-world problems. Deep reinforcement learning (DRL) is the application of DL in the domain of Reinforcement Learning. Like any software system, DRL applications can fail because of faults in their programs. In this paper, we present the first attempt to categorize faults occurring in DRL programs. We manually analyzed 761 artifacts of DRL programs (from Stack Overflow posts and GitHub issues) developed using well-known DRL frameworks (OpenAI Gym, Dopamine, Keras-rl, Tensorforce) and identified faults reported by developers/users. We labeled and taxonomized the identified faults through several rounds of discussions. The resulting taxonomy is validated using an online survey with 19 developers/researchers. To allow for the automatic detection of faults in DRL programs, we have defined a meta-model of DRL programs and developed DRLinter, a model-based fault detection approach that leverages static analysis and graph transformations. The execution flow of DRLinter consists in parsing a DRL program to generate a model conforming to our meta-model and applying detection rules on the model to identify faults occurrences. The effectiveness of DRLinter is evaluated using 21 synthetic and real faulty DRL programs. For synthetic samples, we injected faults observed in the analyzed artifacts from Stack Overflow and GitHub. The results show that DRLinter can successfully detect faults in both synthesized and real-world examples with a recall of 75% and a precision of 100%.
Thu 13 OctDisplayed time zone: Eastern Time (US & Canada) change
10:00 - 12:00 | Technical Session 21 - SE for AI IIResearch Papers / Late Breaking Results / NIER Track / Journal-first Papers at Banquet B Chair(s): Andrea Stocco Università della Svizzera italiana (USI) | ||
10:00 20mResearch paper | DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks Research Papers Simin Chen University of Texas at Dallas, USA, Mirazul Haque UT Dallas, Cong Liu University of Texas at Dallas, USA, Wei Yang University of Texas at Dallas | ||
10:20 10mPaper | Prototyping Deep Learning Applications with Non-Experts: An Assistant Proposition Late Breaking Results Gustavo Rodrigues dos Reis Rodrigues dos Reis, Adrian Mos NAVER LABS Europe, Cyril Labbé LIG - UGA, Mario Cortes Cornax LIG - UGA | ||
10:30 20mResearch paper | Boosting the Revealing of Detected Violations in Deep Learning Testing: A Diversity-Guided MethodVirtualACM SIGSOFT Distinguished Paper Award Research Papers Xiaoyuan Xie School of Computer Science, Wuhan University, China, Pengbo Yin School of Computer Science, Wuhan University, Songqiang Chen School of Computer Science, Wuhan University | ||
10:50 20mPaper | Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection ApproachVirtual Journal-first Papers Amin Nikanjam École Polytechnique de Montréal, Mohammad Mehdi Morovati École Polytechnique de Montréal, Foutse Khomh Polytechnique Montréal, Houssem Ben Braiek École Polytechnique de Montréal Link to publication DOI Authorizer link | ||
11:10 20mResearch paper | Towards Understanding the Faults of JavaScript-Based Deep Learning SystemsVirtual Research Papers Lili Quan Tianjin University, Qianyu Guo College of Intelligence and Computing, Tianjin University, Xiaofei Xie Singapore Management University, Singapore, Sen Chen Tianjin University, Xiaohong Li TianJin University, Yang Liu Nanyang Technological University | ||
11:30 10mVision and Emerging Results | An Empirical Study on Numerical Bugs in Deep Learning ProgramsVirtual NIER Track Gan Wang , Zan Wang Tianjin University, China, Junjie Chen Tianjin University, Xiang Chen Nantong University, Ming Yan College of Intelligence and Computing, Tianjin University | ||
11:40 20mResearch paper | Toward Improving the Robustness of Deep Learning Models via Model TransformationVirtual Research Papers Yingyi Zhang College of Intelligence and Computing, Tianjin University, Zan Wang Tianjin University, China, Jiajun Jiang Tianjin University, Hanmo You College of Intelligence and Computing, Tianjin University, Junjie Chen Tianjin University | ||