English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Software Engineering data is being analyzed by classical statistical methods and non parametric methods. Performance models are constructed using classical approach as a high maturity practice. Such practices are constrained by data quality and inadequacy of data analysis methods to treat data from real life projects. Data mining techniques can broaden the data analysis capability and improve prediction accuracy even with commonly presented data. Artificial neural networks are found as an improved prediction error estimation method against traditional parametric software reliability growth models. In this paper, we study prediction errors of Artificial Neural Networks (ANN) based Software Reliability Growth Models (ANN SRGM) with the objective of arriving at a criteria for selecting the methods having least prediction errors. All major works in ANN SRGM’s are considered and reported errors are analyzed. Accuracy of ANN SRGM’s are compared against that of parametric models. Then, inter-comparison of error performances of ANN SRGM’s of different applications is made.

Accuracy of Artificial Neural Network Models of Software Reliability Growth – A Survey