Object oriented software quality prediction using general regression neural networks

Authors:
S. Kanmani;V. Rhymend Uthariaraj;V. Sankaranarayanan;P. Thambidurai
Affiliations:
Pondicherry Engineering College Pondicherry, India;Anna University, Chennai, India;Anna University, Chennai, India;Pondicherry Engineering College Pondicherry, India
Venue:
ACM SIGSOFT Software Engineering Notes
Year:
2004

Citing 5
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Abstract

This paper discusses the application of General Regression Neural Network (GRNN) for predicting the software quality attribute -- fault ratio. This study is carried out using static Object-Oriented (OO) measures (64 in total) as the independent variables and fault ratio as the dependent variable. Software metrics used include those concerning inheritance, size, cohesion and coupling. Prediction models are designed using 15 possible combinations of the four categories of the measures. We also tested the goodness of fit of the neural network model with the standard parameters. Our study is conducted in an academic institution with the software developed by students of Undergraduate/Graduate courses.