This work addresses how to validate group fairness in image recognition software. We propose a distribution-aware fairness testing approach (called DistroFair) that systematically exposes class-level fairness violations in image classifiers via a synergistic combination of out-of-distribution (OOD) testing and semantic-preserving image mutation. DistroFair automatically learns the distribution of objects in a set of images. Then it systematically mutates objects in the images to become OOD using three semantic-preserving image mutations – object deletion, object insertion and object rotation. We evaluate DistroFair using two well-known datasets (CityScapes and MS-COCO) and three major, commercial image recognition software (namely, Amazon Rekognition, Google Cloud Vision and Azure Computer Vision). Results show that about 21% of images generated by DistroFair reveal class-level fairness violations and DistroFair is up to 2.3x more effective than the baselines.
Thu 31 OctDisplayed time zone: Pacific Time (US & Canada) change
10:30 - 12:00 | Test generationResearch Papers / Journal-first Papers at Gardenia Chair(s): Lingming Zhang University of Illinois at Urbana-Champaign | ||
10:30 15mTalk | Towards Understanding the Effectiveness of Large Language Models on Directed Test Input Generation Research Papers Zongze Jiang Huazhong University of Science and Technology, Ming Wen Huazhong University of Science and Technology, Jialun Cao Hong Kong University of Science and Technology, Xuanhua Shi Huazhong University of Science and Technology, Hai Jin Huazhong University of Science and Technology | ||
10:45 15mTalk | Distribution-aware Fairness Test Generation Journal-first Papers Sai Sathiesh Rajan Singapore University of Technology and Design, Singapore, Ezekiel Soremekun Royal Holloway, University of London, Yves Le Traon University of Luxembourg, Luxembourg, Sudipta Chattopadhyay Singapore University of Technology and Design | ||
11:00 15mTalk | Effective Unit Test Generation for Java Null Pointer Exceptions Research Papers Myungho Lee Korea University, Jiseong Bak Korea University, Seokhyeon Moon , Yoon-Chan Jhi Technology Research, Samsung SDS, Seoul, South Korea, Hakjoo Oh Korea University | ||
11:15 15mTalk | SlicePromptTest4J: High-coverage Test Generation using LLM via Method Slicing Research Papers Zejun Wang Peking University, Kaibo Liu Peking University, Ge Li Peking University, Zhi Jin Peking University | ||
11:30 15mTalk | DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning Research Papers Davide Corradini University of Verona, Zeno Montolli University of Verona, Michele Pasqua University of Verona, Mariano Ceccato University of Verona | ||
11:45 15mTalk | On the Evaluation of Large Language Models in Unit Test Generation Research Papers Lin Yang Tianjin University, Chen Yang Tianjin University, Shutao Gao Tianjin University, Weijing Wang College of Intelligence and Computing, Tianjin University, Bo Wang Beijing Jiaotong University, Qihao Zhu DeepSeek-AI, Xiao Chu Huawei Cloud Computing Co. Ltd., Jianyi Zhou Huawei Cloud Computing Technologies Co., Ltd., Guangtai Liang Huawei Cloud Computing Technologies, Qianxiang Wang Huawei Technologies Co., Ltd, Junjie Chen Tianjin University Pre-print | ||