IMPROVING SOFTWARE RELIABILITY MODELING USING MACHINE LEARNING TECHNIQUES
Fengzhong Zou, Joseph G. Davis · International Journal of Software Engineering and Knowledge Engineering · 2008
It is well-known that software failure data often contain noise, making the reliability estimation problematic. In particular, the kind of data noise inherent in software failure data is biased. There is no upper bound for the value of a noisy data point, but there is a lower bound of zero. This may lead to over-optimistic estimation of the reliability when using maximum likelihood or least square methods based on standard software reliability models. We attempt to address this problem by modeling software failure data using machine learning techniques such as support vector machine regression and generalized additive models, which have mechanisms that are capable of dealing with data noise. We then analyse the results from machine learning modeling, and compare them to that of some generalized linear modeling techniques that are equivalent to standard software reliability models. The validity of the machine learning modeling of noisy software failure data is evaluated through this comparison.