15th International Symposium, SSBSE 2023, San Francisco, CA, USA, December 8, 2023, Proceedings Arcaini, Paolo. editor. edt http://id.loc.gov/vocabulary/relators/edt Yue, Tao. editor. edt http://id.loc.gov/vocabulary/relators/edt Fredericks, Erik M. editor. edt http://id.loc.gov/vocabulary/relators/edt SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

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access XX, 167 p. 37 illus., 31 illus. in color. online resource. 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. . 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. edited by Paolo Arcaini, Tao Yue, Erik M. Fredericks. Software engineering Operating systems (Computers) Computer networks  Computer science Artificial intelligence Software Engineering Operating Systems Computer Communication Networks Theory of Computation Artificial Intelligence QA76.758 9783031487965 https://doi.org/10.1007/978-3-031-48796-5 https://doi.org/10.1007/978-3-031-48796-5 231203 20251030110847.0 978-3-031-48796-5