Although it is well understood to be a generally undecidable problem, a number of attempts have been made over the years to develop systems to automatically generate test data. These approaches have ranged from early attempts at symbolic execution to more recent approaches based on, for example, dynamic data flow analysis or constraint satisfaction. Despite their variety (and varying degrees of success), all the systems developed have involved a detailed analysis of the program or system under test and have encountered problems (such as handling of procedure calls, efficiently finding solutions to systems of predicates and dealing with problems of scale) which have hindered their progress from research prototype to commercial tool. The approach described in this paper uses the ideas of Genetic Algorithms (GAs) to automatically develop a set of test data to achieve a level of coverage (branch coverage in this case). Using GAs neatly sidesteps many of the problems encountered by other systems in attempting to automatically generate test data.
|Publication status||Published - 1997|
|Event||10th International Quality Week - San Fransisco, United States|
Duration: 27 May 1997 → 30 May 1997
|Conference||10th International Quality Week|
|Period||27/05/97 → 30/05/97|
- software testing
- genetic algorithms