A Tale of Two DL Cities: When Library Tests Meet CompilerSE for AI
Deep Learning (DL) compilers typically load a DL model and optimize it with intermediate representation. Existing DL compiler testing techniques mainly focus on model optimization stages, but rarely explore bug detection at the model loading stage. Effectively testing the model loading stage requires covering diverse usages of each DL operator from various DL libraries, which shares a common objective with DL library testing, indicating that the embedded knowledge in DL library tests could potentially be beneficial for testing the model loading stage of DL compilers. Thus, we conducted the first empirical study to investigate the effectiveness and efficiency of migrating the knowledge embedded in DL library tests to test the model loading stage. To support the conduct of this study, we develop a technique, called OPERA, consisting of test migration (regarding effectiveness investigation) and test prioritization (regarding efficiency investigation). We considered three sources of tests in DL libraries for migration and used eight frontends from three DL compilers (e.g., TVM, TensorRT, and OpenVINO) for evaluation. The migrated tests with the aid of OPERA detected 170 previously unknown bugs in total, 90 of which have been confirmed/fixed by developers, demonstrating the effectiveness of such the migration-based idea. The test prioritization strategy in OPERA improves testing efficiency with migrated tests by 11.9%~47.4% on average compared to general test prioritization strategies. Finally, we obtained 7 major findings and provided a set of guidelines for future work from this study.
Fri 2 MayDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | SE for AI with Quality 1Research Track at 215 Chair(s): Chris Poskitt Singapore Management University | ||
11:00 15mTalk | A Tale of Two DL Cities: When Library Tests Meet CompilerSE for AI Research Track Qingchao Shen Tianjin University, Yongqiang Tian , Haoyang Ma Hong Kong University of Science and Technology, Junjie Chen Tianjin University, Lili Huang College of Intelligence and Computing, Tianjin University, Ruifeng Fu Tianjin University, Shing-Chi Cheung Hong Kong University of Science and Technology, Zan Wang Tianjin University | ||
11:15 15mTalk | Iterative Generation of Adversarial Example for Deep Code ModelsSE for AIAward Winner Research Track | ||
11:30 15mTalk | On the Mistaken Assumption of Interchangeable Deep Reinforcement Learning ImplementationsSE for AI Research Track Rajdeep Singh Hundal National University of Singapore, Yan Xiao Sun Yat-sen University, Xiaochun Cao Sun Yat-Sen University, Jin Song Dong National University of Singapore, Manuel Rigger National University of Singapore Pre-print Media Attached File Attached | ||
11:45 15mTalk | µPRL: a Mutation Testing Pipeline for Deep Reinforcement Learning based on Real FaultsSE for AI Research Track Deepak-George Thomas Tulane University, Matteo Biagiola Università della Svizzera italiana, Nargiz Humbatova Università della Svizzera italiana, Mohammad Wardat Oakland University, USA, Gunel Jahangirova King's College London, Hridesh Rajan Tulane University, Paolo Tonella USI Lugano Pre-print | ||
12:00 15mTalk | Testing and Understanding Deviation Behaviors in FHE-hardened Machine Learning ModelsSE for AI Research Track Yiteng Peng Hong Kong University of Science and Technology, Daoyuan Wu Hong Kong University of Science and Technology, Zhibo Liu Hong Kong University of Science and Technology, Dongwei Xiao Hong Kong University of Science and Technology, Zhenlan Ji The Hong Kong University of Science and Technology, Juergen Rahmel HSBC, Shuai Wang Hong Kong University of Science and Technology | ||
12:15 15mTalk | TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron ProvenanceSE for AI Research Track Pre-print | ||