Black Box Testing is used when code of the module is not available. In such situations appropriate priorities can be given to different test cases, so that the quality of software is not compromised, if testing is to be stopped prematurely. This paper proposes a framework, which uses requirement analysis and design specification, to prioritize the test cases. The work would be beneficial to both practitioners and researchers.
Regression testing re-executes the test cases to validate that changes made in software does not affects the correct functionality inherited from previous version. Due to limited time and resources, test cases are prioritized based on some criteria such that important test cases are executed within testing period. The work proposes a genetic algorithm based prioritization technique, which intelligently reorders the test cases on maximum fault detection rate. The work paves the way of genetic algorithms in regression testing.
Black Box Testing is immensely important because the source code of a module is not always available. Enterprise Resource Planning systems are also tested using Black Box Testing wherein all the test cases are not equally important. The prioritization of these test cases would be helpful in case of premature termination of testing, due to lack of resources. This paper proposes a Neural Network based method to prioritize test cases. The paper also presents guidelines for prioritizing test cases. The technique has been tested using a financial management system and the results are encouraging. This paper paves way for applying Neural Network in Black Box Testing and presents a framework, which would help both researchers and practitioners.
The testing of a system starts with the crafting of test cases. Not all the test cases are, however, equally important. The test cases can be prioritized using policies discussed in the work. The work proposes a neural network model to prioritize the test cases. The work has been validated using backpropagation neural network. 200 test cases were crafted and the experiment was carried out using 2, 5, 10, 15, and 20 layers neural network. The results have been reported and lead to the conclusion that neural network-based priority analyzer can predict the priority of a test.
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