Table of Contents

• Message from the Program Co-Chairs; ICST 2024Gregory Gay 0002, Shiva Nejati. [doi]
• MACS: Multi-Agent Adversarial Reinforcement Learning for Finding Diverse Critical Driving ScenariosShuting Kang, Qian Dong, Yunzhi Xue, Yanjun Wu. 1-12 [doi]
• EmoSTL: Formal Spatial-Temporal Verification of Emotion Specifications in Computer GamesSaba Gholizadeh Ansari, I. S. W. B. Prasetya, Mehdi Dastani, Frank Dignum, Gabriele Keller. 13-24 [doi]
• Differential Optimization Testing of Gremlin-Based Graph Database SystemsYingying Zheng, Wensheng Dou, Lei Tang, Ziyu Cui, Jiansen Song, Ziyue Cheng, Wei Wang 0049, Jun Wei 0001, Hua Zhong 0001, Tao Huang 0001. 25-36 [doi]
• FusionFL: A Statement-Level Feature Fusion Based Fault Localization ApproachYanbo Zhang, Yawen Wang, Dongming Zhu, Wenjing Liu. 37-46 [doi]
• Enhanced Fast and Reliable Statistical Vulnerability Root Cause Analysis with SanitizerZhuo Yan, Haipeng Qu, Lingyun Ying, Ke Liu, Q. Chao. 47-58 [doi]
• State of the Practice in Software Testing Teaching in Four European CountriesPorfirio Tramontana, Beatriz Marín, Ana C. R. Paiva, Alexandra Mendes, Tanja E. J. Vos, Domenico Amalfitano, Felix Cammaerts, Monique Snoeck, Anna Rita Fasolino. 59-69 [doi]
• Predicting Safety Misbehaviours in Autonomous Driving Systems Using Uncertainty QuantificationRuben Grewal, Paolo Tonella, Andrea Stocco 0001. 70-81 [doi]
• KAT: Dependency-Aware Automated API Testing with Large Language ModelsTri Le, Thien Tran, Duy Cao, Vy Le, Tien N. Nguyen, Vu Nguyen. 82-92 [doi]
• Quantizing Large-Language Models for Predicting Flaky TestsShanto Rahman, Abdelrahman Baz, Sasa Misailovic, August Shi. 93-104 [doi]
• Evolutionary Testing for Program RepairHaifeng Ruan, Hoang Lam Nguyen, Ridwan Shariffdeen, Yannic Noller, Abhik Roychoudhury. 105-116 [doi]
• METAL: Metamorphic Testing Framework for Analyzing Large-Language Model QualitiesSangwon Hyun, Mingyu Guo, Muhammad Ali Babar 0001. 117-128 [doi]
• Intent-Driven Mobile GUI Testing with Autonomous Large Language Model AgentsJuyeon Yoon, Robert Feldt, Shin Yoo. 129-139 [doi]
• MSGFuzzer: Message Sequence Guided Industrial Robot Protocol FuzzingYang Zhang, Dongliang Fang, Puzhuo Liu, Laile Xi, Xiao Lu, Xin Chen, Shuaizong Si, Limin Sun. 140-150 [doi]
• Formatted Stateful Greybox Fuzzing of TLS ServerFan Hu, Jiangan Ji, Hui Shu, Zheming Li, Tieming Liu, Chao Zhang. 151-160 [doi]
• Causal Test AdequacyMichael Foster 0001, Christopher Wild, Robert M. Hierons, Neil Walkinshaw. 161-172 [doi]
• Assessing Quality Metrics for Neural Reality Gap Input Mitigation in Autonomous Driving TestingStefano Carlo Lambertenghi, Andrea Stocco 0001. 173-184 [doi]
• Brewing Up Reliability: Espresso Test Generation for Android AppsIván Arcuschin, Lisandro Di Meo, Michael Auer, Juan P. Galeotti, Gordon Fraser 0001. 185-196 [doi]
• Learning Environment Models with Continuous Stochastic Dynamics - with an Application to Deep RL TestingMartin Tappler, Edi Muskardin, Bernhard K. Aichernig, Bettina Köninghofer. 197-208 [doi]
• U-Fuzz: Stateful Fuzzing of IoT Protocols on COTS DevicesZewen Shang, Matheus E. Garbelini, Sudipta Chattopadhyay 0001. 209-220 [doi]
• Aurora: Navigating UI Tarpits via Automated Neural Screen UnderstandingSafwat Ali Khan, Wenyu Wang, Yiran Ren, Bin Zhu, Jiangfan Shi, Alyssa McGowan, Wing Lam, Kevin Moran. 221-232 [doi]
• Automatically Removing Unnecessary Stubbings from Test SuitesMengzhen Li, Mattia Fazzini. 233-244 [doi]
• Spectral Analysis of the Relation between Deep Learning Faults and Neural Activation ValuesNargiz Humbatova, Gunel Jahangirova, Paolo Tonella. 245-256 [doi]
• 230,439 Test Failures Later: An Empirical Evaluation of Flaky Failure ClassifiersAbdulrahman Alshammari, Paul Ammann, Michael Hilton, Jonathan Bell 0001. 257-268 [doi]
• Automatically Reproducing Timing-Dependent Flaky-Test FailuresShanto Rahman, Aaron Massey, Wing Lam, August Shi, Jonathan Bell 0001. 269-280 [doi]
• Does Going Beyond Branch Coverage Make Program Repair Tools More Reliable?Amirfarhad Nilizadeh, Gary T. Leavens, Corina S. Pasareanu, Xuan-Bach Dinh Le, David R. Cok. 281-292 [doi]
• Adversarial Testing with Reinforcement Learning: A Case Study on Autonomous DrivingAndréa Doreste, Matteo Biagiola, Paolo Tonella. 293-304 [doi]
• On the Coupling between Vulnerabilities and LLM-Generated Mutants: A Study on Vul4J DatasetAayush Garg, Renzo Degiovanni, Mike Papadakis, Yves Le Traon. 305-316 [doi]
• Improving Patch Correctness Analysis via Random Testing and Large Language ModelsFacundo Molina, Juan Manuel Copia, Alessandra Gorla. 317-328 [doi]
• Cost of Flaky Tests in Continuous Integration: An Industrial Case StudyFabian Leinen, Daniel Elsner, Alexander Pretschner, Andreas Stahlbauer, Michael Sailer, Elmar Jürgens. 329-340 [doi]
• Using Mutation Testing To Improve and Minimize Test Suites for Smart ContractsEnzo Nicourt, Benjamin Kushigian, Chandrakana Nandi, Yliès Falcone. 341-352 [doi]
• Are We Testing or Being Tested? Exploring the Practical Applications of Large Language Models in Software TestingRobson Santos, Ítalo Santos, Cleyton V. C. de Magalhães, Ronnie de Souza Santos. 353-360 [doi]
• Metamorphic Testing of an Autonomous Delivery Robots SchedulerThomas Laurent 0003, Paolo Arcaini, Xiaoyi Zhang 0005, Fuyuki Ishikawa. 361-372 [doi]
• BugOut: Automated Test Generation and Bug Detection for Low-CodeJoana Coutinho, Alexandre Lemos, Miguel Terra-Neves, André Ribeiro, Vasco M. Manquinho, Rui Quintino, Bartlomiej Matejczyk. 373-382 [doi]
• In Industrial Embedded Software, are Some Compilation Errors Easier to Localize and Fix than Others?Han Fu, Sigrid Eldh, Kristian Wiklund, Andreas Ermedahl, Philipp Haller, Cyrille Artho. 383-394 [doi]
• SafeRevert: When Can Breaking Changes be Automatically Reverted?Tim A. D. Henderson, Avi Kondareddy, Sushmita Azad, Eric Nickell. 395-406 [doi]
• Randomised Testing of the Compiler for a Verification-Aware Programming LanguageAlastair F. Donaldson, Dilan Sheth, Jean-Baptiste Tristan, Alex Usher. 407-418 [doi]
• End-to-End RPA-Like Testing Using Reinforcement LearningCiprian Paduraru, Rares Cristea, Alin Stefanescu. 419-429 [doi]
• Poster: Towards Understanding Root Causes of Real Failures in Healthcare Machine Learning ApplicationsGuna Sekaran Jaganathan, Nazmul Kazi, Indika Kahanda, Upulee Kanewala. 430-433 [doi]
• Poster: VeriSol-MCE: Verification-Based Condition Coverage Analysis of Smart Contracts Using Model Checker EnginesSangharatna Godboley, P. Radha Krishna 0001. 434-437 [doi]
• Poster: gptCombFuzz: Combinatorial Oriented LLM Seed Generation for effective FuzzingDarshan Lohiya, Monika Rani Golla, Sangharatna Godboley, P. Radha Krishna 0001. 438-441 [doi]
• The GitHub Recent Bugs Dataset for Evaluating LLM-Based Debugging ApplicationsJae-Yong Lee, Sungmin Kang, Juyeon Yoon, Shin Yoo. 442-444 [doi]
• MLHCBugs: A Framework to Reproduce Real Faults in Healthcare Machine Learning ApplicationsGuna Sekaran Jaganathan, Nazmul Kazi, Indika Kahanda, Upulee Kanewala. 445-447 [doi]
• U-Fuzz: A Tool Prototype for Stateful Fuzzing of IoT Protocols on COTS DevicesZewen Shang, Matheus E. Garbelini, Sudipta Chattopadhyay 0001. 448-450 [doi]
• MOTIF: A tool for Mutation Testing with FuzzingJaekwon Lee, Enrico Viganò, Fabrizio Pastore, Lionel C. Briand. 451-453 [doi]
• Teaching Model-Driven Engineering from a Model-Testing PerspectiveFelix Cammaerts. 454-456 [doi]
• Agile Regression TestingSuddhasvatta Das. 457-459 [doi]
• Insights into System Failures: ML-Assisted Testing and Failure Models for Cyber-Physical SystemsBaharin Aliashrafi Jodat. 460-462 [doi]
• Search-Based Security Testing of Enterprise MicroservicesSusruthan Seran. 463-465 [doi]