EXPLORATION FOR SOFTWARE RELIABILITY USING NEURAL NETWORK-BASED CLASSIFICATION METHOD

Chitra S.¹, Madhusudhanan B.², Rajaram M.^3
1
2
3

Received : -     Accepted : -     Published : 21-12-2009
Volume : 1     Issue : 2       Pages : 10 - 13
Int J Mach Intell 1.2 (2009):10-13
DOI : http://dx.doi.org/10.9735/0975-2927.1.2.10-13

Software reliability is an important aspect of software quality. According to ANSI, it is defined as "the probability of failure-free operation of a computer program in a specified environment for a specified time". One of reliability's distinguishing characteristics is that it is objective, measurable, and can be estimated, whereas much of software quality is subjective criteria. This distinction is especially important in the discipline of SQA. These measured criteria are typically called software metrics. Although software reliability is defined as a probabilistic function, and comes with the notion of time, we must note that, software reliability is different from traditional hardware reliability, and not a direct function of time. Electronic and mechanical parts may become "old" and wear out with time and usage, but software will not rust or wear-out during its life cycle. Software will not change over time unless intentionally changed or upgraded. Neural Network-based Classification Method (NNCM) was used to classify the data using recordset cyclomatic density and design density. The records were preprocessed using normal distribution. The overall error in the classification using NNCM after normal distribution was found to be 0.38%. The reliability of classification with goodness of fit measure results in and forms the subsequent improvement of error classification among the dataset.

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