A scheme on automated test data generation and its evaluation

Abstract

By analyzing some existing test data generation methods, a new automated test data generation approach was presented. The linear predicate functions on a given path was directly used to construct a linear constrain system for input variables. Only when the predicate function is nonlinear, does the linear arithmetic representation need to be computed. If the entire predicate functions on the given path are linear, either the desired test data or the guarantee that the path is infeasible can be gotten from the solution of the constrain system. Otherwise, the iterative refining for the input is required to obtain the desired test data. Theoretical analysis and test results show that the approach is simple and effective, and takes less computation. The scheme can also be used to generate path-based test data for the programs with arrays and loops.

Keywords

test data generation / linear constrain / linear arithmetic representation

References

[1] AvritzerA, WeyukerE J. The automatic generation of load test suites and the assessment of the resulting software [J]. IEEE Transactions on Software Engineering, 1995, 21(9): 705-716

[2] DeasonW H, BrownD B, ChangK, et al.. A rule based software test data generator[J]. IEEE Transactions Knowledge and Data Engineering, 1991, 3(1): 108-116

[3] BauerJ, FingerA. Test plan generation using formal grammars [C]. Proceedings of the 4th International Conference of Software Engineering, 1979, Munich, IEEE Computer Society Press: 425-432

[4] Coen-PorisiniA, DenaroG, GhezziC, et al.. Using symbolic execution for verifying safety-critical systems [J]. ACM SIGSOFT Software Engineering Notes, 2001, 26(5): 142-151

[5] ChenT Y, TseT H, ZhouZhi-quan. Semi-proving: an integrated method based on global symbolic evaluation and metamorphic testing[J]. ACM SIGSOFT Software Engineering Notes, 2002, 27(4): 191-195

[6] ClarkeL A. A system to generate test data and symbolically execute programs[J]. IEEE Transactions on Software Engineering, 1976, SE-2(3): 215-222

[7] DeMilloR, OffuttJ. Constraint-based automatic test data generation[J]. IEEE Transactions on Software Engineering, 1991, 17(9): 900-910

[8] GallagherM J, NarasimhanV L. Adtest: a test data generation suite for ada software systems[J]. IEEE Transactions on Software Engineering, 1997, 23(8): 473-484

[9] KorelB. Automated software test data generation[J]. IEEE Transactions on Software Engineering, 1990, 16(8): 870-879

[10] MichaelC C, McGrawG, SchatzM A. Generating software test data by evolution[J]. IEEE Transactions on Software Engineering, 2001, 27(12): 1085-1110

[11] GuptaN, MathurA P, SoffaM L. Generating test data for branch coverage[C]. Proceeding of the 15th IEEE International Conference on Automated Software Engineering, 2000, Grenoble, IEEE: 219-227

[12] BeydedaS, GruhnV. Test data generation based on binary search for class-level testing [C]. ACS/IEEE International Conference on Computer Systems and Applications, 2003, Tunis, IEEE: 107-114

[13] TransyN, DevilleY. Consistency techniques for interprocedural test data generation[J]. ACM SIGSOFT Software Engineering Notes, 2003, 28(5): 108-117

[14] GuptaN, ChoY J, HossainM ZUNA: A simple numerical algorithm to solve linear constraints in real variables [R], 2003, Tucson, Computer Science Department, The University of Arizona

[15] GuptaN, MathurA P, SoffaM L. Automated test data generation using an iterative relaxation method [J]. ACM SIGSOFT Software Engineering Notes, 1998, 23(6): 231-244

[16] ShanJin-huiOn path-wise automated test data generation [D], 2002, Changsha, National University of Defense Technology(in Chinese)

[17] GuptaN, ChoY J, HossainM Z. Experiments with UNA for solving linear constraints in real variables [C]. Proceedings of the 2004 ACM Symposium on Applied Computing, 2004, Nicosia, ACM: 1013-1020

[18] EdvardssonJ, KamkarM. Analysis of the constraint solver in UNA based test data generation [J]. ACM SIGSOFT Software Engineering Notes, 2001, 26(5): 237-245