With the increasing prevalence of software incorporating deep neural networks (DNNs), quality assurance for these software systems has become a crucial concern. To this end, various methods have been proposed to repair the misbehavior of DNNs by modifying their weights. However, these repair methods may not meet the developer’s needs for a given dataset and model. In this study, we build prediction models for repair outcomes (i.e., repairs and breaks) to help determine whether the repair method is likely to work. By using our prediction models, developers and operators of DNNs can decide whether or not to apply a repair method, and if so, which method to use. Our prediction models utilize four metrics as explanatory metrics that represent the confidence or ambiguity in the DNN predictions. We experimented with four repair methods and 10 datasets. The experimental results demonstrate that our prediction models successfully select a repair method that meets developers’ needs in 16 out of 24 cases, resulting in an average time saving of 16.29% compared to the naive method. Based on these results, our prediction models can reduce costs for developers and operators when deciding whether to employ repair methods for real-world applications of DNNs.
Mon 23 JunDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:00 - 18:00 | RepairsResearch Papers / Journal First at Andromeda Chair(s): Michael Pradel University of Stuttgart | ||
16:00 20mTalk | HornBro: Homotopy-like Method for Automated Quantum Program Repair Research Papers Siwei Tan Zhejiang University, Liqiang Lu Zhejiang University, Debin Xiang Zhejiang University, Tianyao Chu Zhejiang University, Congliang Lang Zhejiang University, Jintao Chen Zhejiang University, Xing Hu Zhejiang University, Jianwei Yin Zhejiang University DOI | ||
16:20 20mTalk | RePurr: Automated Repair of Block-Based Learners' Programs Research Papers DOI | ||
16:40 20mTalk | Demystifying Memorization in LLM-based Program Repair via a General Hypothesis Testing Framework Research Papers Jiaolong Kong Singapore Management University, Xiaofei Xie Singapore Management University, Shangqing Liu Nanyang Technological University DOI | ||
17:00 20mTalk | IRepair: An Intent-Aware Approach to Repair Data-Driven Errors in Large Language Models Research Papers Sayem Mohammad Imtiaz Iowa State University, Astha Singh Dept. of Computer Science, Iowa State University, Fraol Batole Tulane University, Hridesh Rajan Tulane University DOI | ||
17:20 20mTalk | Repairs and Breaks Prediction for Deep Neural Networks Journal First Yuta Ishimoto Kyushu University, Masanari Kondo Kyushu University, Lei Ma The University of Tokyo & University of Alberta, Naoyasu Ubayashi Waseda University, Yasutaka Kamei Kyushu University | ||
17:40 20mTalk | Element-Based Automated DNN Repair with Fine-Tuned Masked Language Model Research Papers Xu Wang Beihang University; Zhongguancun Laboratory; Ministry of Education, Mingming Zhang Beihang University, Xiangxin Meng Beihang University, Jian Zhang Nanyang Technological University, Yang Liu Nanyang Technological University, Chunming Hu Beihang University DOI | ||
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