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Methodol."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>Search-based software testing (SBST) typically relies on fitness functions to guide the search exploration toward software failures. There are two main techniques to define fitness functions: (a)\u00a0automated fitness function computation from the specification of the system requirements, and (b)\u00a0manual fitness function design. Both techniques have advantages. The former uses information from the system requirements to guide the search toward portions of the input domain more likely to contain failures. The latter uses the engineers\u2019 domain knowledge.<\/jats:p>\n          <jats:p>\n            We propose\n            <jats:monospace>ATheNA<\/jats:monospace>\n            , a novel SBST framework that combines fitness functions automatically generated from requirements specifications and those manually defined by engineers. We design and implement\n            <jats:monospace>ATheNA-S<\/jats:monospace>\n            , an instance of\n            <jats:monospace>ATheNA<\/jats:monospace>\n            that targets Simulink\n            <jats:sup>\u00ae<\/jats:sup>\n            models. We evaluate\n            <jats:monospace>ATheNA-S<\/jats:monospace>\n            by considering a large set of models from different domains. Our results show that\n            <jats:monospace>ATheNA-S<\/jats:monospace>\n            generates more failure-revealing test cases than existing baseline tools and that the difference between the runtime performance of\n            <jats:monospace>ATheNA-S<\/jats:monospace>\n            and the baseline tools is not statistically significant. We also assess whether\n            <jats:monospace>ATheNA-S<\/jats:monospace>\n            could generate failure-revealing test cases when applied to two representative case studies: one from the automotive domain and one from the medical domain. Our results show that\n            <jats:monospace>ATheNA-S<\/jats:monospace>\n            successfully revealed a requirement violation in our case studies.\n          <\/jats:p>","DOI":"10.1145\/3624745","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T11:26:32Z","timestamp":1695209192000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Search-Based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3033-7371","authenticated-orcid":false,"given":"Federico","family":"Formica","sequence":"first","affiliation":[{"name":"McMaster University, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8567-2611","authenticated-orcid":false,"given":"Tony","family":"Fan","sequence":"additional","affiliation":[{"name":"McMaster University, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5303-8481","authenticated-orcid":false,"given":"Claudio","family":"Menghi","sequence":"additional","affiliation":[{"name":"University of Bergamo, Italy and McMaster University, Canada"}]}],"member":"320","published-online":{"date-parts":[[2023,12,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-83903-1"},{"key":"e_1_3_2_3_2","volume-title":"MathWorks","author":"MathWorks","year":"2022","unstructured":"MathWorks. 2022 [Online]. 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