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  1. 1. A metaheuristic approach to automatictest case generation for applicationwith GUIBelhassen Ouerghi
  2. 2. The metaheuristic methods
  3. 3. The problem
  4. 4. The problem● Users love features and options.● The majority of software application employ a graphical userinterface(GUI).● The GUI are becoming more and more complex (moreoptions, more features).● Testing a GUI taking into consideration(fonctionality,securitiy, usability…) is laborious and resourceintensive.
  5. 5. New generation of GUI
  6. 6. Old school GUI
  7. 7. The solution
  8. 8. The solution● Automating the process of testing.● Scripting and capture and reply is the most commontechnique in the industry.
  9. 9. Is it really a good solution?
  10. 10. Why?● Actions need often to be in specific order.● Actions have to appear in the context of certain otheractions 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 generateautomatically test sequences.
  11. 11. The metaheuristic approach● The problem of generating test sequences to GUIs willbe treated as an optimization problem.● Ant colony optimization algorithm is employed.● New metric called MCT(Maximum Call Tree) is usedto search fault-sensitive test cases.
  12. 12. How ant colony optimize their way
  13. 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 theGUI.
  14. 14. The maximum call tree criterion (MCT)● Choosing the right criteria is critical for finding faults.● Generating sequences that induce a large call treewithin 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. 15. The maximum call tree criterion (MCT)
  16. 16. Merging thread
  17. 17. Test environment requierment● To be able to scan the GUI of the SUT to obtain allvisible widgets and their properties(size, position,focus…).● To derive a set of interesting actions(visible, enabledbutton, is clickable…).● To give these actions unique name.● To execute sequences of these actions.
  18. 18. How sequences are generated
  19. 19. The framework
  20. 20. RandomVs ACO● K is the number of top-K sequences in everygeneration.● α is the pheromone evaporation rate.● ρ is the probability parameter for the pseudoproportional random selection rule.
  21. 21. ConclusionWe used the ACO optimization algorithm directed by MCTto automatically generate the input sequence for applicationwith GUI.Since we forgo the application of GUI model there is norisk of generating infeasible sequences.
  22. 22. References●● „A Metaheuristic Approach to Test sequence Generationfor Applications with a GUI“ paper from SebastianBauersfeld, Stefan Wappler, Joachim Wegner.●
  23. 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
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