Using a genetic algorithm and formal concept analysis to generate branch coverage test data automatically

Automatic test generators (ATGs) are an important support tool for large-scale software development. Contemporary ATGs include JTest that does white box testing down to the method level only and black box testing if a specification exists, and AETG that tests pairwise interactions among input variables. The first automatic test generation approaches were static, based on symbolic execution (Clarke, 1976). Korel suggested a dynamic approach to automatic test data generation using function minimization and directed search (Korel, 1990). A dynamic approach can handle array, pointer, function and other dynamic constructs more accurately than a static approach but it may also be more expensive since the program under test is executed repeatedly. Subsequent ATGs explored the use of genetic algorithms (Jones et al., 1996; Michael et al., 2001; Pargas et al., 1999) and simulated annealing (Tracey et al., 1998). These ATGs address the problem of producing test data for low level code coverage like statement, branch and condition/decision and depend on branch function (Korel, 1990) style instrumentation (Jones et al., 1996; Michael et al., 2001) and/or the program graph (Jones et al., 1996; Pargas et al., 1999). Unlike previous work, our ATG, called genet, produces test data for branch coverage with simpler instrumentation than branch functions, does not use program graphs, and is programming language independent, genet uses a genetic algorithm (GA) (Holland, 1975) to search for tests and formal concept analysis (FCA) (Ganter and Wille, 1999) to organize the relationships between tests and their execution traces. The combination of GA with FCA is novel. Further, genet extends the opportunistic approach of GADGET (Michael et al., 2001) by targeting several uncovered branches simultaneously. The relationships that genet learns provides useful insights for test selection, test maintenance and debugging