Machine learning methods have been employed in data mining to discover useful, valid, and beneficial patterns for various applications of which, the domain encompasses areas in business, medicine, agriculture, census, and software engineering. Focusing on software engineering, this report presents an investigation of machine learning techniques that have been utilized to predict programming faults during the verification and validation of software. Artifacts such as traces in program executions, information about test case coverage and data pertaining to execution failures are of special interest to address the following concerns: Completeness for test suite coverage; Automation of test oracles to reduce human intervention in Software testing; Detection of faults causing program failures; Defect prediction in software. A survey of literature pertaining to the verification and validation of software also revealed a novel concept designed to improve black-box testing using Category-Partition for test specifications and test suites. The report includes two experiments using data extracted from source code available from the website (15) to demonstrate the application of a decision tree (C4.5) and the multilayer perceptron for fault prediction, and an example that shows a potential candidate for the Category-Partition scheme. The results from several research projects shows that the application of machine learning in software testing has achieved various degrees of success in effectively assisting software developers to improve their test strategy in verification and validation of software systems. / text