- author = "Anna I. Esparcia-Alcazar and Francisco Almenar and Tanja E. J. Vos and Urko Rueda",
- title = "Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the {TESTAR} tool",
- journal = "Memetic Computing",
- year = "2018",
- volume = "10",
- number = "3",
- pages = "257--265",
- month = sep,
- keywords = "genetic algorithms, genetic programming, linear genetic programming, SBSE, Automated software testing via the GUI, Action selection for testing, Testing metrics",
- ISSN = "1865-9284",
-
URL = "
http://link.springer.com/article/10.1007/s12293-018-0263-8",
-
DOI = "
doi:10.1007/s12293-018-0263-8",
- size = "9 pages",
- abstract = "Traversal-based automated software testing involves testing an application via its graphical user interface (GUI) and thereby taking the user's point of view and executing actions in a human-like manner. These actions are decided on the fly, as the software under test (SUT) is being run, as opposed to being set up in the form of a sequence prior to the testing, a sequence that is then used to exercise the SUT. In practice, random choice is commonly used to decide which action to execute at each state (a procedure commonly referred to as monkey testing), but a number of alternative mechanisms have also been proposed in the literature. Here we propose using genetic programming (GP) to evolve such an action selection strategy, defined as a list of IF-THEN rules. Genetic programming has proved to be suited for evolving all sorts of programs, and rules in particular, provided adequate primitives (functions and terminals) are defined. These primitives must aim to extract the most relevant information from the SUT and the dynamics of the testing process. We introduce a number of such primitives suited to the problem at hand and evaluate their usefulness based on various metrics. We carry out experiments and compare the results with those obtained by random selection and also by Q-learning, a reinforcement learning technique. Three applications are used as Software Under Test (SUT) in the experiments. The analysis shows the potential of GP to evolve action selection strategies.",
-
notes = "13000th GP entry
GUI reflection to give widget tree. No source code needed. Implicit test oracles. validate with 30 runs of 500 (psuedo user) actions each. STGP? 100 crossover plus some mutation, pop=11, Tournament size 11. Coverage metrics p263 'did not prove very useful'
microsoft PowerPoint. Odoo. Testona
'The authors wish to thank Xara Sharman for her help with the graphics.'",
