Abstract
Need for regression testing has been ever growing demand for software development organizations so does the cost of executing regression test suites. The intent of this idea is to propose a unique method in regression test for selecting a subset of test cases as oppose to select all test cases for a given software change using Neural Network. In general, a software consist of multiple features and it interacts with each other in the order of different magnitude. Each feature interaction is assessed based on five relative dimensional complexity. These complexities for each feature interaction is determined and subjected to neural network based training model with appropriate test set. Subsequently any code change in a software iterative build can be evaluated for regression test by providing a new set of change dimensional complexity parameters for the affected features to the machine learning software for predicting optimal number of regression test cases. This method will help the organization to reduce the overall regression test effort significantly.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Dhanadevan, Komateswar; Nallasamy, Jayasankar; and Murugavel, Sundaravadivel, "Neural network based regression test selection", Technical Disclosure Commons, (September 15, 2017)
https://www.tdcommons.org/dpubs_series/669