{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/xcsf-for-automatic-test-case-prioritization","title":"XCSF for Automatic Test Case Prioritization","arxiv_id":null,"date":"2020-11-04","proceeding":"IJCCI 2020 11","authors":["Lukas Rosenbauer","Anthony Stein","David Pätzel","Jörg Hähner"],"abstract":"Testing is a crucial part in the development of a new product. Due to the change from manual testing to\r\nautomated testing, companies can rely on a higher number of tests. There are certain cases such as smoke\r\ntests where the execution of all tests is not feasible and a smaller test suite of critical test cases is necessary.\r\nThis prioritization problem has just gotten into the focus of reinforcement learning. A neural network and an\r\nXCS classifier system have been applied to this task. Another evolutionary machine learning approach is the\r\nXCSF which produces, unlike XCS, continuous outputs. In this work we show that XCSF is superior to both\r\nthe neural network and XCS for this problem.","url_abs":"https://www.researchgate.net/publication/346824192_XCSF_for_Automatic_Test_Case_Prioritization","url_pdf":"https://www.researchgate.net/publication/346824192_XCSF_for_Automatic_Test_Case_Prioritization","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"xcsf-for-automatic-test-case-prioritization","repo_url":"https://github.com/LagLukas/xcsf_atcs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}