Search-based software testing and complex test data generation in a dynamic programming language

Document type: Conference Papers
Peer reviewed: Yes
Author(s): Stefan Mairhofer, Robert Feldt, Richard Torkar
Title: Search-based software testing and complex test data generation in a dynamic programming language
Conference name: Genetic and Evolutionary Computation Conference GECCO
Year: 2011
Pagination: 1859-1866
Publisher: GECCO
City: Dublin
Organization: Blekinge Institute of Technology
Department: School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
http://www.bth.se/com
Language: English
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.
Subject: Software Engineering\General
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