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Testing Software and Systems: 37th IFIP WG 6.1 International Conference, ICTSS 2025, Limassol, Cyprus, September 1719, 2025, Proceedings [Paperback]

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  • Category: Books (Computers)
  • ISBN-10:  3032051878
  • ISBN-10:  3032051878
  • ISBN-13:  9783032051875
  • ISBN-13:  9783032051875
  • Publisher:  Springer
  • Publisher:  Springer
  • Binding:  Paperback
  • Binding:  Paperback
  • SKU:  3032051878-11-SPRI
  • SKU:  3032051878-11-SPRI
  • Pages:  366
  • Pages:  366
  • Item ID: 106906672
  • List Price: $139.99
  • Seller: ShopSpell
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  • Delivery by: Oct 10 to Oct 12
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This book constitutes the refereed proceedings of the 37th IFIP WG 6.1 International Conference on Testing Software and Systems, ICTSS 2025, held in Limassol, Cyprus, during September 17–19, 2025.

The 19 full papers and 4 short papers included in this book were carefully reviewed and selected from 38 submissions. They were organized in topical sections as follows: Foundations and Advanced Testing Techniques; Intelligent and Automated Testing; LLMs, Agents, and AI-Driven Testing; Testing in Complex and Security-Critical Systems.
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.- Foundations and Advanced Testing Techniques.

.- A Time Series Analysis of Assertions in the Linux Kernel.

.- Loop unrolling: formal definition and application to testing.

.- Time for Quiescence: Modelling Quiescent Behaviour in Testing via
Time outs in Timed Automata.

.- On using Homing Sequences instead of Distinguishing in FSM based
Testing.

.- Testability Indicators for Refactoring.

.- Enhancing Path Testing with Eye Tracking: A Human Centric
Approach to Functional Software Testing.

.- Intelligent and Automated Testing.

.- Introducing CreaTest: a framework for test case generation in itemis
CREATE.

.- Distributed Critical Test Generation for Cyber Physical Systems.

.- Reusable Test Suites for Reinforcement Learning.

.- Test Generation for Deep Reinforcement Learning Using LRP Guided
Mutation of Classified Configurations.

.- Test Amplification for REST APIs via Single and Multi Agent LLM
Systems.

.- Reverse Engineering for Input Modeling: Input Parameter Model
Inference from Netl©

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