Measuring Software Reliability: A Trend Using Machine Learning Techniques - Complex System Modelling and Control Through Intelligent Soft Computations - page 791

Measuring Software Reliability: A Trend
Using Machine Learning Techniques

Nishikant Kumar and Soumya Banerjee

Abstract It has become inevitable for every software developer to understand, to
follow that how and why software fails, and to express reliability in quantitative
terms. This has led to a proliferation of software reliability models to estimate and
predict reliability. The basic approach is to model past failure data to predict future
behavior. Most of the models have three major components: assumptions, factors
and a mathematical function, usually high order exponential or logarithmic used to
relate factors to reliability. Software reliability models are used to forecast the curve
of failure rate by statistical evidence available during testing phase. They also can
indicate about the extra time required to carry out the test procedure in order to meet
the speci

cations and deliver desired functionality with minimum number of
defects. Therefore there are challenges whether, autonomous or machine learning
techniques like other predictive methods could be able to forecast the reliability
measures for a speci c software application. This chapter contemplates reliability

issue through a generic Machine Learning paradigm while referring the most
common aspects of Support Vector Machine scenario. Couples of customized
simulation and experimental results have been presented to support the proposed
reliability measures and strategies.

Keywords Reliability measure Software defects Machine learning Support

vector machine Statistical validations