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Details for article 3 of 11 found articles
| Title: |
Exploration for Software Reliability using Neural Network-Based Classification method |
| Author: |
Chitra S. Madhusudhanan B. Rajaram M. |
| Appeared in: |
International journal of machine intelligence |
| Paging: | Volume 1 (2009) nr. 2 pages 10-13 |
| Year: |
2009 |
| Contents: |
Software reliability is an important aspect of software quality. According to ANSI, it is defined as "the probability offailure-free operation of a computer program in a specified environment for a specified time". One of reliability's distinguishingcharacteristics 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, softwarereliability is different from traditional hardware reliability, and not a direct function of time. Electronic and mechanical parts maybecome "old" and wear out with time and usage, but software will not rust or wear-out during its life cycle. Software will notchange over time unless intentionally changed or upgraded. Neural Network-based Classification Method (NNCM) was used toclassify the data using recordset cyclomatic density and design density. The records were preprocessed using normaldistribution. The overall error in the classification using NNCM after normal distribution was found to be 0.38%. The reliability ofclassification with goodness of fit measure results in and forms the subsequent improvement of error classification among thedataset. |
| Publisher: |
Bioinfo Publications (provided by DOAJ) |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 3 of 11 found articles
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