Search based algorithms for test sequence generation in functional testing

• We extend Classification Tree Method in order to automatically generate test sequences.
• The Extended Classification Tree Method is formally defined.
• Our proposed algorithms deal with the Test Sequence Generation Problem for class and transition coverage.
• We perform an experimental analysis using 12 software models and comparing three different techniques.
• We have made some experimental validation of our proposed algorithms on real programs.

ContextThe generation of dynamic test sequences from a formal specification, complementing traditional testing methods in order to find errors in the source code.ObjectiveIn this paper we extend one specific combinatorial test approach, the Classification Tree Method (CTM), with transition information to generate test sequences. Although we use CTM, this extension is also possible for any combinatorial testing method.MethodThe generation of minimal test sequences that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search-based approaches are required to find such (near) optimal test sequences.ResultsThe experimental analysis compares the search-based technique with a greedy algorithm on a set of 12 hierarchical concurrent models of programs extracted from the literature. Our proposed search-based approaches (GTSG and ACOts) are able to generate test sequences by finding the shortest valid path to achieve full class (state) and transition coverage.ConclusionThe extended classification tree is useful for generating of test sequences. Moreover, the experimental analysis reveals that our search-based approaches are better than the greedy deterministic approach, especially in the most complex instances. All presented algorithms are actually integrated into a professional tool for functional testing.