Titlebook: Search-Based Software Engineering; 15th International S Paolo Arcaini,Tao Yue,Erik M. Fredericks Conference proceedings 2024 The Editor(s)

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Evaluating Explanations for?Software Patches Generated by?Large Language Modelshe use of LLMs to enhance the explainability of software patches. In particular, we evaluate the performance of GPT 3.5 in explaining patches generated by the search-based automated program repair system ARJA-e for 30 bugs from the popular Defects4J benchmark. We also investigate the performance ach

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Enhancing Genetic Improvement Mutations Using Large Language Modelsrch-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the . Java GI toolkit to call OpenAI’s API to generate edits for the JCodec tool. We randomly sample the

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SearchGEM5: Towards Reliable Gem5 with?Search Based Software Testing and?Large Language Models resultant code snippets, and feed compilable ones to ., our extension to . fuzzer with customised new mutation operators. We run thus created 4005 binaries through our system under test, ., increasing its existing test coverage by more than 1000 lines. We discover 244 instances where . simulation o

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Conference proceedings 2024in?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,

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