Slim Kallel

Ismael Bouassida Rodriguez

Amel Bennaceur

Mohamed Sellami

Claudia Raibulet

Noura Faci

Saoussen Cheikhrouhou

Leila Ben Ayed

Elisa Yumi Nakagawa

Riadh Ben Halima

This two-volume set volume constitutes the papers of ten workshops which were held in conjunction with the 22nd International Conference on Service-Oriented Computing, ICSOC 2024. The conference took place in Tunis, Tunisia, during December 3–6, 2024. The 58 full papers and 3 short papers presented in this book were carefully reviewed and selected from 109 submissions. ICSOC 2024 presents the following ten workshops: – 9th International Workshop on Adaptive Service-oriented and Cloud Applications (ASOCA) – 5th International Workshop on AI-enabled Process Automation (AI-PA) – 20th International Workshop on Engineering Service-Oriented Applications and Cloud Services (WESOACS) – 1st International Workshop on Generative AI as a Software Service (GAISS) – 1st International Workshop on Lightweight AI-based Services (LAIS) – 1st International Workshop on Optimising AI Models Using Local Data on Resource-Constrained Edge Devices - Training \& Inference (AI on Edge) – 1st International Workshop on RealTime Service oriented and EMbedded Systems (RTSEMS) – 2nd International Workshop on Services and Quantum Software (SQS) – 1st International Workshop on Service-Oriented Computing, AI, and IoT for Smart Applications (SOCAISA) – 1st International Workshop on Service-Oriented Computing for AI Applications (SOC4AI).

ASOCA, AI-PA, WESOACS, GAISS, LAIS, AI on Edge, RTSEMS, SQS, SOCAISA, SOC4AI and Satellite Events, Tunis, Tunisia, December 3–6, 2024, Revised Selected Papers, Part II

Service-Oriented Computing – ICSOC 2024 Workshops

Reiner Hähnle

Karl Meinke

Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts. This volume originates from a Dagstuhl Seminar entitled "Machine Learning for Dynamic Software Analysis: Potentials and Limits” held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities. The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches.

International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers

Machine Learning for Dynamic Software Analysis: Potentials and Limits

Amel Bennaceur

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