A Software Reliability Model Combining Representative and Directed Testing

Traditionally, software reliability models have required that failure data be gathered using only representative testing methods. Over time, however, representative testing becomes inherently less effective as a means of improving the actual quality of the software under test. Additionally, the use of failure data based on observations made during representative testing has been criticized because of the statistical noise inherent in this type of data. In this dissertation, a testing method is proposed to make reliability testing more efficient and accurate. Representative testing is used early, when the rate of fault revelation is high. Directed testing is used later in testing to take advantage of its faster rate of fault detection. To make use of the test data from this mixed method approach to testing, a software reliability model is developed that permits reliability estimates to be made regardless of the testing method used to gather failure data. The key to being able to combine data from both representative testing and directed testing is shifting the random variable used by the model from observed interfailure times to a postmortem analysis of the debugged faults and using order statistics to combine the observed failure rates of faults no matter how those faults were detected. This shift from interfailure times removes the statistical noise associated with the use of this measure, which should allow models to provide more accurate estimates and predictions. Several experiments were conducted during the course of this research. The results from these experiments show that using the mixed method approach to testing with the new model provides reliability estimates that are at least as good as estimates from existing models under representative testing, while requiring fewer test cases. The results of this work also show that the high level of noise present in failure data based on observed failure times makes it very difficult for models that use this type of data to make accurate reliability estimates. These findings support the suggested move to the use of more stable quantities for reliability estimation and prediction.