Artificial intelligence

Abstract: Interactive method for simplification and efficiency improvement of test scenario development to satisfy high-level requirements coverage is proposed. The method implements semiautomatic generation of tests on a basis of points-of-interest of desired behavior of a system under development. It is based on efficient algorithm for state space reducing, which makes on-the-fly assessment of the prospects of achieving elements yet uncovered. The algorithm will suspend consideration of the behavior branches that will provably not be able contribute to coverage.

Keywords:

References:

1. Myers G.J. (2004). The Art Of Software Testing.New York. John Wiley & Sons, Inc. –254p.
2. Volkov V., Kolchin A., Letychevskiy A., Potiyenko S.Obzor sistematicheskih metodov generacii testovyhdannyh po ishodnomu kodu programmnyh sistem //Iskusstvennyj intellekt. –2017. –N2. –P. 71–84.
3. Fraser G., Wotawa F., Ammann P. (2009). Testingwith model checkers: a survey // Software Testing,Verification and Reliability, 19, 215–261.
4. Utting M., Legeard B. (2007). Practical ModelBased Testing: A Tools Approach. MorganKaufmann. – 456p.
5. Petrenko A., Silva S., Maldonado J. (2012). Modelbased testing of software and systems: recentadvances and challenges // Software tools fortechnology transfer, 14(4), 383–386.
6. Rushby J. (2008). Automated test generation andverified software // Verified Software: Theories,Tools, Experiments, 161–172.
7. Gay G., Staats M., Whalen M., Heimdahl M.(2015). The risks of coverage-directed test casegeneration // IEEE Transactions on SoftwareEngineering, 41, 803–819.
8. Heimdahl M., Whalen M., Rajan A., Staats M.(2008). On MC/DC and implementation structure:An empirical study // In Proc. of Digital AvionicsSystems Conf.
9. Barr E., Harman M., McMinn P., Shahbaz M.,Yoo S. (2015). The oracle problem in softwaretesting: a survey // IEEE Transactions on SoftwareEngineering, 41, 507–525.
10. Athanasiou D., Nugroho A., Visser J. and ZaidmanA. (2014). Test code quality and its relation to issuehandling performance // IEEE Transactions onSoftw. Eng, 40(11), 1100–1125.
11. Palomba F., Panichella A., and oth. (2016).Automatic Test Case Generation: What if TestCode Quality Matters? // In Proc. of Int. Symp. onSoftw. Testing and Analysis, 130–141.
12. Beyer D., Dangl M. (2016). SMT-based SoftwareModel Checking: An Experimental Comparison ofFour Algorithms // Verified Software. Theories,Tools, and Experiments, 181–198.
13. Baranov S., Kotlyarov V., Weigert T. (2012).Varifiable Coverage Criteria For AutomatedTesting. SDL2011: Integrating System andSoftware Modeling // LNCS, 7083, 79–89.
14. Kolchin A.V., Kotlyarov V.P., Drobintsev P.D.(2012). Metod generacii testovyh scenariev v srede insercionnogo modelirovanija // Upravljajushhiesistemy i mashiny, 6, 43–48.
15. Kolchin A.V. (2009). Razrabotka instrumentalnykhsredstv dlya proverki formalnykh modeleyasinkhronnykh sistem, Dis. … kand. fiz.-mat. nauk,Kiev. –140p.
16. Kolchin A.V. (2018). Interactive method forcumulative analysis of software formal modelsbehavior // Proc. of the 11th Int. conf. of programmingUkrPROG'2018, CEUR-WS, 2139, 115–123.
17. Kolchin A. (2013). Metod reduktsii analiziruemogoprostranstva povedeniya pri verifikatsii formalnyihmodeley raspredelennyih programmnyih sistem //Iskusstvennyiy intellect, 4, 113–126.
18. Hessel A., Petterson P. (2007). A global algorithmfor model-based test suite generation // Electr.Notes Theor. Comput. Sci, 190(2), 47–59.
19. Boonstoppel P., Cadar C. (2008). RWset: Attackingpath explosion in constraint-based test generation.LNCS, 4963, 351–366.
20. Cseppento L., Micskei Z. (2015). Evaluatingsymbolic execution-based test tools // In IEEE Int.Conf. on Software Testing, Verification andValidation, 1–10.
21. Fraser G., Arcuri A. (2015). 1600 faults in 100projects: automatically finding faults whileachieving high coverage with Evosuite // Empericalsoftware engineering, 20(3), 611–639.
22. Kolchin A. (2018). A novel algorithm for attackingpath explosion in model-based test generation fordata flow coverage // IEEE Int. Conf. on SystemAnalysis & Intelligent Computing.
23. Su T., Wu K., Miao W., Pu G., and oth. (2017).A Survey on Data-Flow Testing // ACM Comput.Survey, 50(1), 35p.
24. Voinov N., Drobintsev P. and oth. (2015). Methodof Symbolic Test Scenarios AutomatedConcretization // Proceedings of the Institute forsystem programming of the RAS, 27, 115–124.
25. Guba A., Kolchin A., Potiyenko S. (2016). Metodizvlechenija logiki povedenija iz promyshlennogoprogrammnogo koda na jazyke Cobol // Problemyprogrammirovanija, 1–2, 17–25.
26. Kolchin A.V., Potiyenko S.V. (2016). Metodgeneracii testovyh dannyh po ishodnomu kodu Javaprogramm // Iskusstvennyj intellect, 3, 50–58.