Many software reliability studies attempt to develop a model for predicting the faults of a software module because the application of good prediction models provides important information on significant metrics that should be observed in the early stages of implementation during software development. In this article we propose a new method inspired by a multi-agent based system that was initially used for classification and attribute selection in microarray analysis. Best classifying gene subset selection is a common problem in the field of bioinformatics. If we regard the software metrics measurement values of a software module as a genome of that module, and the real world dynamic characteristic of that module as its phenotype (i.e. failures as a disease symptoms) we can borrow the established bioinformatics methods in the manner first to predict the module behavior and second to data mine the relations between metrics and failures.
Detecting Fault Modules Using Bioinformatics Techniques
PIGHIN, Maurizio
2007-01-01
Abstract
Many software reliability studies attempt to develop a model for predicting the faults of a software module because the application of good prediction models provides important information on significant metrics that should be observed in the early stages of implementation during software development. In this article we propose a new method inspired by a multi-agent based system that was initially used for classification and attribute selection in microarray analysis. Best classifying gene subset selection is a common problem in the field of bioinformatics. If we regard the software metrics measurement values of a software module as a genome of that module, and the real world dynamic characteristic of that module as its phenotype (i.e. failures as a disease symptoms) we can borrow the established bioinformatics methods in the manner first to predict the module behavior and second to data mine the relations between metrics and failures.File | Dimensione | Formato | |
---|---|---|---|
3-2007-IJSEKE.pdf
non disponibili
Tipologia:
Altro materiale allegato
Licenza:
Non pubblico
Dimensione
164.1 kB
Formato
Adobe PDF
|
164.1 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.