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An Approach to Reduce Test Effort By Using Machine Learning Techniques


Mohammed Inayathulla, Silpa C


Vol. 15  No. 9  pp. 99-103


It is obvious that testing consumes more than fifty percent of development effort. Hence it may be advantageous for any organization if the testing effort is reduced. Various fault prediction models have been proposed but their effectiveness in reducing testing effort or improving quality is not addressed. An approach(TERA) is proposed which uses the machine learning techniques in order to reduce the testing effort. Initially by using the prediction models the number of defects are predicted and based on these defects appropriate testing effort is allocated to each module. The test effort can be reduced only if the suitable test strategy is used with appropriate fault-prediction accuracy.


Fault Prediction, Machine Learning,