Testing Software and Systems

This book constitutes the refereed proceedings of the 36th IFIP WG 6.1 International Conference on Testing Software and Systems, ICTSS 2024, held in London, UK, during October 30November 1, 2024.
The 17 full papers and 5 short papers included in this book were carefully reviewed and selected from 40 submissions. They were organized in topical sections as follows: Best Paper Award; Industry and Challenge Tracks; Mutation Testing and Code Generation; Advancing Code Vulnerability Detection; Short Papers; Tutorial; Journal First; Health Track; Innovations in Software Testing and AI Compliance; Improving Software Testing Reliability and Advancements in Testing Methodologies.

Inhalt

.- Best Paper Award.
.- Estimating Combinatorial t-way Coverage based on Matrix Complexity Metrics.
.- Industry and Challenge Tracks.
.- Enhancing RL Safety with Counterfactual LLM Reasoning.
.- GoNoGo: An Efficient LLM-based Multi-Agent System for Streamlining Automotive Software Release Decision-Making.
.- Test Prioritization based on the Coverage of Recently Modified Source Code: An Industrial Case Study.
.- On the variations of ChatGPT's response quality for generating source code across programming languages.
.- Reevaluating the small-scope testing hypothesis of answer set programs.
.- Advancing Code Vulnerability Detection.
.- Enhancing Vulnerability Detection with Domain Knowledge: a Comparison of Different Mechanisms.
.- LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic Understanding Tasks.
.- Enhanced Graph Neural Networks for Vulnerability Detection in Java via Advanced Subgraph Construction.
.- Short Papers.
.- Mutating Clingo's AST with clingabomino.
.- Towards a Knowledge Graph based approach for vulnerable code weaknesses identification.
.- Tutorial.
.- Automatic Summarization Evaluation: Methods and Practices.
.- Journal First.
.- Summary of ObfSec: Measuring the security of obfuscations from a testing perspective.
.- Health Track.
.- A trusted friend in the middle of the night: End-user perspectives on Artificial Intelligence informed software systems as a decision-making aid for patients.
.- Binary Classification Optimisation with AI-Generated Data.
.- Responsible MLOps Design Methodology for an Auditing System for AI-based Clinical Decision Support Systems.
.- Innovations in Software Testing and AI Compliance.
.- Software System Testing assisted by Large Language Models: An Exploratory Study.
.- Continuous Auditing Based Conformity Assessment for AI Systems: A Proof-of-Concept Evaluation.
.- Improving Software Testing Reliability.
.- Checking Test Suite Efficacy Through Dual-Channel Techniques.
.- Extending a Flakiness Score for System-Level Tests.
.- Advancements in Testing Methodologies.
.- Autonomous Driving System Testing: Traffic Density Does Matter.
.- Annotation-based input modeling for combinatorial testing.