1. A metaheuristic approach to automatic
test case generation for application
with GUI
Belhassen Ouerghi

2. The metaheuristic methods

3. The problem

4. The problem
● Users love features and options.
● The majority of software application employ a graphical user
interface(GUI).
● The GUI are becoming more and more complex (more
options, more features).
● Testing a GUI taking into consideration
(fonctionality,securitiy, usability…) is laborious and resource
intensive.

5. New generation of GUI

6. Old school GUI

7. The solution

8. The solution
● Automating the process of testing.
● Scripting and capture and reply is the most common
technique in the industry.

9. Is it really a good solution?

10. Why?
● Actions need often to be in specific order.
● Actions have to appear in the context of certain other
actions to provoke faults.
● Tester have to compile the entire test suite.
● Slight changes to the GUI of SUT will break tests.
 We do have obviously a problem to generate
automatically test sequences.

11. The metaheuristic approach
● The problem of generating test sequences to GUIs will
be treated as an optimization problem.
● Ant colony optimization algorithm is employed.
● New metric called MCT(Maximum Call Tree) is used
to search fault-sensitive test cases.

12. How ant colony optimize their way

13. The event flow graph (EFG)
● Since many sequence are infeasible like this one:
s=(Edit, Paste) it is helpful to employ a model of the
GUI.

14. The maximum call tree criterion (MCT)
● Choosing the right criteria is critical for finding faults.
● Generating sequences that induce a large call tree
within the SUT.
● Sequences are generated online (executing the SUT)
therefore no need for a model of the GUI.
● Don‘t have to deal with the infeasibility.

15. The maximum call tree criterion (MCT)

16. Merging thread

17. Test environment requierment
● To be able to scan the GUI of the SUT to obtain all
visible widgets and their properties(size, position,
focus…).
● To derive a set of interesting actions(visible, enabled
button, is clickable…).
● To give these actions unique name.
● To execute sequences of these actions.

18. How sequences are generated

19. The framework

20. RandomVs ACO
● K is the number of top-K sequences in every
generation.
● α is the pheromone evaporation rate.
● ρ is the probability parameter for the pseudo
proportional random selection rule.

21. Conclusion
We used the ACO optimization algorithm directed by MCT
to automatically generate the input sequence for application
with GUI.
Since we forgo the application of GUI model there is no
risk of generating infeasible sequences.

22. References
● http://de.wikipedia.org/wiki/Ameisenalgorithmus
● „A Metaheuristic Approach to Test sequence Generation
for Applications with a GUI“ paper from Sebastian
Bauersfeld, Stefan Wappler, Joachim Wegner.
● http://en.wikipedia.org/wiki/Metaheuristic

23. A question
Is testing a GUI really an optimization problem?
Are we searching or planning when we test?
Find it out at 19.06.2013