An adaptive flower pollination algorithm for software test suite minimization
Muhammad Nomani Kabir, Jahan Ali, AbdulRahman A. Alsewari, Kamal Z. Zamli · 2017 3rd International Conference on Electrical Information and Communication Technology (EICT) · 2017
Optimization is the selection of a best set of parameters from available alternative sets. Global optimization is the task of finding the absolutely best set of parameters. In this paper, we present an adaptive flower pollination algorithm for solving an optimization problem, i.e., minimization of software test suite for interaction testing. In software testing, test engineers generate a set of test cases to validate against the requirements to avoid failure of the software. Testing all the interactions for modern software with many system inputs is impractical due to huge number of possible combinations of the inputs. In order to tackle the issue, global optimization algorithms are used to systematically minimize the test suite for interaction testing. Using the proposed adaptive flower pollination algorithm, we carried out some experiments for minimizing software test suite for interaction testing. The results were compared with the results of some existing algorithms to demonstrate the strength of our algorithm. Comparison shows that our algorithm performs slightly better than the existing algorithms and thus, the proposed algorithm can potentially be used by researchers and test engineers to obtain optimal test suite.