As software systems evolve, test suites tend to grow in size and often contain redundant test cases. Such redundancy increases testing effort, time, and cost. Test suite minimization (TSM) aims to eliminate such redundancy while preserving key properties such as requirement coverage and fault detection capability. In this paper, we propose RTM (Requirement coverage-guided Test suite Minimization), a novel TSM approach designed for requirement-based testing (validation), which can effectively reduce test suite redundancy while ensuring full requirement coverage and a high fault detection rate (FDR) under a fixed minimization budget. Based on common practice in critical systems where functional safety is important, we assume test cases are specified in natural language and traced to requirements before being implemented. RTM preprocesses test cases using three different preprocessing methods, and then converts them into vector representations using seven text embedding techniques. Similarity values between vectors are computed utilizing three distance functions. A Genetic Algorithm, whose population is initialized by coverage-preserving initialization strategies, is then employed to identify an optimized subset containing diverse test cases matching the set budget.
We evaluate RTM on an industrial automotive system dataset comprising $736$ system test cases and $54$ requirements. Experimental results show that RTM consistently outperforms baseline techniques in terms of FDR across different minimization budgets while maintaining full requirement coverage. Furthermore, we investigate the impact of test suite redundancy levels on the effectiveness of TSM, providing new insights into optimizing requirement-based test suites under practical constraints.
Mutation-Guided Unit Test Generation with a Large Language Model
University of Limerick（利默里克大学）；Research Ireland Lero Centre for Software, University of Limerick（爱尔兰研究莱罗软件中心，利默里克大学）；University of Ottawa（渥太华大学）；Software Engineering Application Technology Lab Huawei（华为软件工程应用技术实验室）
Synergizing Code Coverage and Gameplay Intent: Coverage-Aware Game Playtesting with LLM-Guided Reinforcement Learning
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；College of Science and Technology, Nihon University（日本立命馆大学科学技术学院）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Waseda Institute for Advanced Study, Waseda University（早稻田大学高级研究机构）
DISTINCT: A Description-Guided Branch-Consistency Analysis Framework for Non-Regressive Test Case Generation
School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；Quancheng Lab（泉城实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Zhejiang University（浙江大学杭州高技区（滨江）区块链与数据安全研究院）
EpiDroid: Dependency-Guided Recomposition for Deep State Discovery in Mobile GUI Testing
Zhejiang University（浙江大学）；University of Electronic Science and Technology of China（电子科技大学）；Hangzhou City University（杭州城市学院）；Shenzhou Aerospace Software Technology Company Limited（神州航天软件技术有限公司）
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing
Nanyang Technological University（南洋理工大学）；Harbin Institute of Technology（哈尔滨工业大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Illinois at Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校）；Kyushu University（九州大学）；NVIDIA AI Technology Center（英伟达人工智能技术中心）
LLM-Based Test Case Generation in DBMS through Monte Carlo Tree Search
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Huawei Hong Kong Research Center（华为香港研究中心）
COMET: Coverage-guided Model Generation For Deep Learning Library Testing
The Hong Kong University of Science and Technology（香港科技大学）；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）；University of Waterloo（滑铁卢大学）；Huazhong University of Science and Technology（华中科技大学）
针对现有 DL 模型生成技术层 API 覆盖不足的问题，本文提出 COMET，利用变异算子、覆盖引导搜索与模型综合提升测试多样性，并在八个 DL 库中发现 32 个新缺陷。
