2015 IEEE/ACM 10th International Workshop on Automation of Software Test (AST) (2015)
May 23, 2015 to May 24, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AST.2015.14
In the context of structural testing, automatic test-pattern generation (ATPG) may fail to provide suites covering 100% of the testing requirements for grey-box programs, i.e., Applications wherein source code is available for some parts (white-box), but not for others (black-box). Furthermore, test suites based on abstract models may elicit behaviors on the actual program that diverge from the intended ones. In this paper, we present a new ATPG methodology to reduce divergence without increasing manual effort. This is achieved by (i) learning models of black-box components as finite-state machines, and (ii) composing the learnt models with the white-box components to generate test-suites for the grey-box program. Experiments with a prototypical implementation of our methodology show that it yields measurable improvements over two comparable state-of-the-art solutions.
Automatic test pattern generation, Software, Model checking, Prototypes, Software algorithms, Data models,
Ali Khalili, Massimo Narizzano, Armando Tacchella, Enrico Giunchiglia, "Automatic Test-Pattern Generation for Grey-Box Programs", 2015 IEEE/ACM 10th International Workshop on Automation of Software Test (AST), vol. 00, no. , pp. 33-37, 2015, doi:10.1109/AST.2015.14