Assessing software quality allows cost cuts from the early development stages. Software quality information helps taking development decisions, checking fault corrections effect, estimating maintenance effort. Our fault density based quality model relies on static source code analyzers and on a set of language specific metrics. We compute the fault ratio for each static analyzer rule. Giving user defined weights to fault ratios we can quantify quality as a number. We identified, described informally and implemented in a prototype a set of Java metrics in order to fulfill our model and to accomplish our quality assessment goal.
software metrics; Java object-oriented systems; software quality assessment
@Book{Chirila2012Springer,
author = {Ciprian-Bogdan Chirila and Vladimir Cretu},
editor = {Radu-Emil Precup and Szilveszter Kovács and Stefan Preitl and Emil M. Petriu},
title = {A Set of Java Metrics for Software Quality Tree Based on Static Code Analyzers},
booktitle = {Applied Computational Intelligence in Engineering and Information Technology},
publisher = {Springer Verlag},
issn = {2193-9411},
isbn = {978-3-642-28304-8},
doi = {10.1007/978-3-642-28305-5},
month = {March},
day = {21},
year = {2012},
}
[Chirila2012Springer] - Ciprian-Bogdan Chirila, Vladimir Cretu. A Set of Java Metrics for Software Quality Tree Based on Static Code Analyzers, Radu-Emil Precup, Szilveszter Kovács, Stefan Preitl, Emil M. Petriu Applied Computational Intelligence in Engineering and Information Technology, Springer Verlag, issn 2193-9411, isbn 978-3-642-28304-8, doi 10.1007/978-3-642-28305-5, March 21, 2012
[Chirila2012Springer] - Ciprian-Bogdan Chirila, Vladimir Cretu. A Set of Java Metrics for Software Quality Tree Based on Static Code Analyzers, Radu-Emil Precup, Szilveszter Kovács, Stefan Preitl, Emil M. Petriu Applied Computational Intelligence in Engineering and Information Technology, Springer Verlag, issn 2193-9411, isbn 978-3-642-28304-8, doi 10.1007/978-3-642-28305-5, March 21, 2012