Failures of mission-critical software systems can have catastrophic consequences and, hence, there is strong need for scientifically rigorous methods for assuring high system reliability. To reduce the V&V cost for achieving high confidence levels, quantitatively based software defect prediction techniques can be used to effectively estimate defects from prior data. Better prediction models facilitate better project planning and risk/cost estimation.
Memory Based Reasoning (MBR) is one such classifier that quantitatively solves new cases by reusing knowledge gained from past experiences. However, it can have different configurations by varying its input parameters, giving potentially different predictions. To overcome this problem, we develop a framework that derives the optimal configuration of an MBR classifier for software defect data, by logical variation of its configuration parameters. We observe that this adaptive MBR technique provides a flexible and effective environment for accurate prediction of mission-critical software defect data.
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