A STRATEGY FOR USING GENETIC ALGORITHMS TO AUTOMATE BRANCH AND FAULT-BASED TESTING

Genetic algorithms have been used successfully to generate software te st data automatically; all branches were covered with substantially fe wer generated tests than simple random testing. We generated test sets which executed all branches in a variety of programs including a quad ratic equation solver, remainder, linear and binary search procedures, and a triangle classifier comprising a system of five procedures. We regard the generation of test sets as a search through the input domai n for appropriate inputs. The genetic algorithms generated test data t o give 100% branch coverage in up to two orders of magnitude fewer tes ts than random testing. Whilst some of this benefit is offset by incre ased computation effort, the adequacy of the test data is improved by the genetic algorithm's ability to generate test sets which are at or close to the input subdomain boundaries. Genetic algorithms may be use d for fault-based testing where faults associated with mistakes in bra nch predicates are revealed. The software has been deliberately seeded with faults in the branch predicates (i.e. mutation testing), and our system successfully killed 97% of the mutants.