2019/02/28 by Ghanem Soltana, Mehrdad Sabetzadeh, Lionel C. Briand · 34 citations
Computer Science · #Constraint (computer-aided design) #Formal Methods in Verification #Metaheuristic #Model-Driven Software Engineering Techniques #Rotation formalisms in three dimensions #Satisfiability #Satisfiability modulo theories #Scalability #Software #Software Testing and Debugging Techniques #Test case #Test data #cs.SE
paper · pdf · doi:10.1145/3381032
published in ACM Transactions on Software Engineering and Methodology 29(2), 1-48 (Association for Computing Machinery) · Published in ACM Transactions on Software Engineering and Methodology (TOSEM)
openalex publication_date 2020/04/29 · openalex created_date 2020/05/13 · arxiv created 2020/05/15 · arxiv updated 2020/05/19 · openalex updated_date 2026/08/05
The ability to generate test data is often a necessary prerequisite for automated software testing. For the generated data to be fit for their intended purpose, the data usually have to satisfy various logical constraints. When testing is performed at a system level, these constraints tend to be complex and are typically captured in expressive formalisms based on first-order logic. Motivated by improving the feasibility and scalability of data generation for system testing, we present a novel approach, whereby we employ a combination of metaheuristic search and Satisfiability Modulo Theories (SMT) for constraint solving. Our approach delegates constraint solving tasks to metaheuristic search and SMT in such a way as to take advantage of the complementary strengths of the two techniques. We ground our work on test data models specified in UML, with OCL used as the constraint language. We present tool support and an evaluation of our approach over three industrial case studies. The results indicate that, for complex system test data generation problems, our approach presents substantial benefits over the state-of-the-art in terms of applicability and scalability.