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• W2024762777 abstract "Manually creating test cases is time consuming and error prone. Search-based software testing can help automate this process and thus reduce time and effort and increase quality by automatically generating relevant test cases. Previous research has mainly focused on static programming languages and simple test data inputs such as numbers. This is not practical for dynamic programming languages that are increasingly used by software developers. Here we present an approach for search-based software testing for dynamically typed programming languages that can generate test scenarios and both simple and more complex test data. The approach is implemented as a tool, RuTeG, in and for the dynamic programming language Ruby. It combines an evolutionary search for test cases that give structural code coverage with a learning component to restrict the space of possible types of inputs. The latter is called for in dynamic languages since we cannot always know statically which types of objects are valid inputs. Experiments on 14 cases taken from real-world Ruby projects show that RuTeG achieves full or higher statement coverage on more cases and does so faster than randomly generated test cases." @default.
• W2024762777 created "2016-06-24" @default.
• W2024762777 creator A5030588511 @default.
• W2024762777 creator A5063787358 @default.
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• W2024762777 date "2011-01-01" @default.
• W2024762777 modified "2023-09-23" @default.
• W2024762777 title "Search-based software testing and test data generation for a dynamic programming language" @default.
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• W2024762777 doi "https://doi.org/10.1145/2001576.2001826" @default.
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