Toward Cost-Effective Adaptive Random Testing: An Approximate Nearest Neighbor Approach
This program is tentative and subject to change.
Adaptive Random Testing (ART) enhances the testing effectiveness (including faultdetection capability) of Random Testing (RT) by increasing the diversity of the random test cases throughout the input domain. Many ART algorithms have been investigated such as Fixed-Size-Candidate-Set ART (FSCS) and Restricted Random Testing (RRT), and have been widely used in many practical applications. Despite its popularity, ART suffers from the problem of high computational costs during test-case generation, especially as the number of test cases increases. Although several strategies have been proposed to enhance the ART testing efficiency, such as the forgetting strategy and the 𝑘-dimensional tree strategy, these algorithms still face some challenges, including: (1) Although these algorithms can reduce the computation time, their execution costs are still very high, especially when the number of test cases is large; and (2) To achieve low computational costs, they may sacrifice some fault-detection capability. In this paper, we propose an approach based on Approximate Nearest Neighbors (ANNs), called Locality-Sensitive Hashing ART (LSH-ART). When calculating distances among different test inputs, LSHART identifies the approximate (not necessarily exact) nearest neighbors for candidates in an efficient way. The results of our simulations and empirical studies show that, overall, LSH-ART achieves comparable and even better fault detection than the original ART and its variants. LSH-ART incurs lower computational costs when generating the same number of test cases, especially when the input domain dimensionality is high, resulting in better cost-effectiveness. We have also reported on a preliminary investigation into expanding the scope of LSH-ART from numerical to nonnumerical domains. The study showed LSH efficiency to be superior to other ART variants, with significant advantages over the others when searching for the (approximate) nearest neighbors in these nonnumerical domains. These findings highlight the potential for LSH-ART to be extended to other types of programs, and we look forward to exploring such a possibility in our future research.
This program is tentative and subject to change.
Wed 19 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 10mTalk | PALM: Synergizing Program Analysis and LLMs to Enhance Rust Unit Test Coverage Research Papers | ||
11:10 10mTalk | ROR-DSE: ROR adequate test case generation using dynamic symbolic execution Journal-First Track Sangharatna Godboley NIT Warangal | ||
11:20 10mTalk | Reflective Unit Test Generation for Precise Type Error Detection with Large Language Models Research Papers Chen Yang Tianjin University, Ziqi Wang Tianjin University, Yanjie Jiang Peking University, Lin Yang Tianjin University, Yuteng Zheng Tianjin University, Jianyi Zhou Huawei Cloud Computing Technologies Co., Ltd., Junjie Chen Tianjin University | ||
11:30 10mTalk | FailMapper: Automated Generation of Unit Tests Guided by Failure Scenarios Research Papers ruiqi dong Swinburne University of Technology, Zehang Deng Swinburne University of Technology, Xiaogang Zhu The University of Adelaide, Xiaoning Du Monash University, Huai Liu Swinburne University of Technology, Shaohua Wang Central University of Finance and Economics, Sheng Wen Swinburne University of Technology, Yang Xiang Digital Research & Innovation Capability Platform, Swinburne University of Technology | ||
11:40 10mTalk | Advancing Code Coverage: Incorporating Program Analysis with Large Language Models Journal-First Track Chen Yang Tianjin University, Junjie Chen Tianjin University, Bin Lin Hangzhou Dianzi University, Ziqi Wang Tianjin University, Jianyi Zhou Huawei Cloud Computing Technologies Co., Ltd. | ||
11:50 10mTalk | Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models Research Papers Dianshu Liao the Australian National University, Xin Yin Zhejiang University, Shidong Pan Columbia University & New York University, Chao Ni Zhejiang University, Zhenchang Xing CSIRO's Data61, Xiaoyu Sun Australian National University, Australia Pre-print | ||
12:00 10mTalk | Enhancing LLM’s Ability to Generate More Repository-Aware Unit Tests Through Precise Context Injection Research Papers Xin Yin Zhejiang University, Chao Ni Zhejiang University, Xinrui Li School of Software Technology, Zhejiang University, Liushan Chen Douyin Co., Ltd., Guojun Ma Douyin Co., Ltd., Xiaohu Yang Zhejiang University Pre-print | ||
12:10 10mTalk | Toward Cost-Effective Adaptive Random Testing: An Approximate Nearest Neighbor Approach Journal-First Track Rubing Huang Macau University of Science and Technology (M.U.S.T.), Chenhui Cui Macau University of Science and Technology, Junlong Lian Jiangsu University, Haibo Chen Jiangsu University, Dave Towey University of Nottingham Ningbo China, Weifeng Sun | ||
12:20 10mTalk | Automated Combinatorial Test Generation for Alloy Research Papers Agustín Borda Dept. of Computer Science FCEFQyN, University of Rio Cuarto, Germán Regis University of Rio Cuarto and CONICET, Nazareno Aguirre University of Rio Cuarto and CONICET, Marcelo F. Frias Dept. of Software Engineering Instituto Tecnológico de Buenos Aires, Pablo Ponzio Dept. of Computer Science FCEFQyN, University of Rio Cuarto | ||