2016 International Conference on Frontiers of Information Technology (FIT) (2016)
Dec. 19, 2016 to Dec. 21, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FIT.2016.018
Regression testing is an important activity performed to ensure thatchanges in the baseline version of the system do not influence thealready tested part of the system. It becomes difficult to run the entiretest suite due to constrained or limited resources. A subset of test casesthat is as efficient as the original test suite is searched as optimal suite.Computational intelligence approaches has been used to search therepresentative subset. The major concern is safe reduction of test suite.Safe reduction has been achieved by fuzzy optimization. Neuralnetworks are known for their ability to learn and fuzzy based systemsfor their quality to judge and make decisions. Neuro-fuzzy systems canbe used to learn and make expert judgment. We have implementedAdaptive Neuro-Fuzzy Inference System (ANFIS) for test suiteoptimization. The resultant suite was found to be better than fuzzybased optimization in reducing the time and improving the coverage ofresulting test suite. We concluded that ANFIS can be used to automatethe optimization process. We will implement it on sufficiently largesized test suite available in software infrastructure repository providedby Siemens to validate our findings.
Optimization, Fuzzy logic, Adaptive systems, Neural networks, Expert systems, Fuzzy systems, Testing
A. A. Haider, A. Nadeem and S. Akram, "Regression Test Suite Optimization Using Adaptive Neuro Fuzzy Inference System," 2016 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 2016, pp. 52-56.