Mitigating Regression Faults Induced by Feature Evolution in Deep Learning Systems
Deep learning (DL) systems have been widely utilized across various domains. However, the evolution of DL systems can result in regression faults. In addition to the evolution of DL systems through the incorporation of new data, feature evolution, such as the addition of new features, is also common and can introduce regression faults. In this work, we first investigate the underlying factors that are correlated with regression faults in feature evolution scenarios, i.e., redundancy and contribution shift. Based on our investigation, we propose a novel mitigation approach called FeaProtect, which aims to minimize the impact of these two factors. To evaluate the performance of FeaProtect, we conducted an extensive study comparing it with state-of-the-art approaches. The results show that FeaProtect outperforms the in-processing baseline approaches, with an average improvement of 50.6% ~ 56.4% in terms of regression fault mitigation. We also show that FeaProtect can further enhance the effectiveness of mitigating regression faults by integrating with state-of-the-art post-processing approaches.
Mon 23 JunDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:00 - 18:00 | SE and AI 1Research Papers / Journal First / Ideas, Visions and Reflections / Demonstrations at Cosmos Hall Chair(s): Yuchao Jiang UNSW | ||
16:00 10mTalk | Learning to Edit Interactive Machine Learning Notebooks Ideas, Visions and Reflections Bihui Jin University of Waterloo, Jiayue Wang University of Waterloo, Pengyu Nie University of Waterloo | ||
16:10 20mTalk | Automatically Detecting Numerical Instability in Machine Learning Applications via Soft Assertions Research Papers Shaila Sharmin Iowa State University, Anwar Hossain Zahid Iowa State University, Subhankar Bhattacharjee Iowa State University, Chiamaka Igwilo Iowa State University, Miryung Kim UCLA and Amazon Web Services, Wei Le Iowa State University DOI | ||
16:30 20mTalk | Mitigating Regression Faults Induced by Feature Evolution in Deep Learning Systems Journal First Hanmo You Tianjin University, Zan Wang Tianjin University, Xuyang Chen College of Intelligence and Computing, Tianjin University, Junjie Chen Tianjin University, Jun Sun Singapore Management University, Shuang Liu Renmin University of China, Zishuo Dong College of Intelligence and Computing, Tianjin University | ||
16:50 10mTalk | ClusterXplain: a Clustering-based Tool for DNN components Debugging Demonstrations | ||
17:00 10mTalk | Capturing Semantic Flow of ML-based Systems Ideas, Visions and Reflections Shin Yoo KAIST, Robert Feldt Chalmers | University of Gothenburg, Somin Kim Korea Advanced Institute of Science and Technology, Naryeong Kim Korea Advanced Institute of Science and Technology | ||
17:10 20mTalk | Has My Code Been Stolen for Model Training? A Naturalness Based Approach to Code Contamination Detection Research Papers Haris Ali Khan Beijing Institute of Technology, Yanjie Jiang Peking University, Qasim Umer Information and Computer Science Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia, Yuxia Zhang Beijing Institute of Technology, Waseem Akram Beijing Institute of Technology, Hui Liu Beijing Institute of Technology DOI | ||
17:30 20mTalk | AlphaTrans: A Neuro-Symbolic Compositional Approach for Repository-Level Code Translation and Validation Research Papers Ali Reza Ibrahimzada University of Illinois Urbana-Champaign, Kaiyao Ke University of Illinois Urbana-Champaign, Mrigank Pawagi Indian Institute of Science, Bengaluru, Muhammad Salman Abid Cornell University, Rangeet Pan IBM Research, Saurabh Sinha IBM Research, Reyhaneh Jabbarvand University of Illinois at Urbana-Champaign DOI Pre-print Media Attached | ||
17:50 10mTalk | Can Hessian-Based Insights Support Fault Diagnosis in Attention-based Models? Ideas, Visions and Reflections | ||
This is the main event hall of Clarion Hotel, which will be used to host keynote talks and other plenary sessions. The FSE and ISSTA banquets will also happen in this room.
The room is just in front of the registration desk, on the other side of the main conference area. The large doors with numbers “1” and “2” provide access to the Cosmos Hall.