Search-Based Software Engineering
15th International Symposium, SSBSE 2023, San Francisco, CA, USA, December 8, 2023, Proceedings
(Sprache: Englisch)
This book constitutes the refereed proceedings of the 15th International Symposium on Search-Based Software Engineering, SSBSE 2023, which took place in San Francisco, CA, USA, during December 8, 2023.The 7 full and 7 short papers included in...
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Klappentext zu „Search-Based Software Engineering “
This book constitutes the refereed proceedings of the 15th International Symposium on Search-Based Software Engineering, SSBSE 2023, which took place in San Francisco, CA, USA, during December 8, 2023.The 7 full and 7 short papers included in this book were carefully reviewed and selected from 23 submissions. They focus on formulating various optimization problems in software engineering as search problems, addressing them with search techniques, intending to automate complex software engineering tasks.
Inhaltsverzeichnis zu „Search-Based Software Engineering “
Research Track.- Generating Android Tests using Novelty Search.- Expound: A Black-box Approach for Generating Diversity-Driven Adversarial Examples.- Developer Views on Software Carbon Footprint and its Potential for Automated Reduction.- Search-Based Mock Generation of External Web Service Interactions.- Exploring Genetic Improvement of the Carbon Footprint of Web Pages.- A Novel Mutation Operator for Search-based Test Case Selection.- Challenge Track.- StableYolo: Optimizing Image Generation for Large Language Models.- Improving the Readability of Generated Tests Using GPT-4 and ChatGPT Code Interpreter.- Evaluating Explanations for Software Patches Generated by Large Language Models.- Enhancing Genetic Improvement Mutations Using Large Language Models.- SearchGEM5: Towards Reliable gem5 with Search Based Software Testing and Large Language Models.- RENE/NIER Track.- Multi-Objective Black-box Test Case Prioritization based on WordNet Distances.- On the Impact of Tool Evolution and Case Study Size on SBSE Experiments: A Replicated Study with EvoMaster.- Search-based Optimisation of LLM Learning Shots for Story Point Estimation.Bibliographische Angaben
- 2023, 1st ed. 2024, XX, 167 Seiten, 31 farbige Abbildungen, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Paolo Arcaini, Tao Yue, Erik M. Fredericks
- Verlag: Springer, Berlin
- ISBN-10: 3031487958
- ISBN-13: 9783031487958
Sprache:
Englisch
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