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.

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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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Regression testing is required whenever changes are incorporated in software. Regression testing re-executes the test cases to verify that changes made during maintainance phase have not caused any unintended effects. Test case prioritization technique re-orders the test cases in such a way that important test cases are executed within the resources. The work reviews different test case prioritization techniques. The work also presents computational intelligence based test case prioritization techniques. The work reviews test case prioritization based on Neural Networks, Genetic Algorithms and Fuzzy Logic.

Distributed Agile Software Development (DASD) is an amalgamation of Agile Software Development (ASD) with Distributed Software Development (DSD). Although DASD integrates the speed benefits of ASD with the cost benefits of DSD, however, it brings along various risk factors that arise due to the fact that both ASD and DSD works on a different set of principles. These associated risks must be addressed and managed well in time for the successful completion of the project. This paper reviews the current literature and presents the current challenges of Risk Management in the DASD environment. This paper also determines 71 risk factors associated with DASD and analyses them based on their causes and sources. Further, these risk factors are segregated into 11 different categories. Timely management of these risks may reduce the uncertainty of project failure in the DASD environment.