Maurizio Pighin

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The purpose of this paper is to put forward a methodology based on discriminant statistical analysis, which, by evaluating a series of structural parameters of a program, is able to predict its risk level, namely how prone it is to containing faults. The metric was constructed in an experimental context in which the high number of available observations(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t While challenging, the ability to predict faulty modules of a program(More)
Accurate prediction of faulty modules reduces the cost of software development and evolution. Two case studies with a language-processing based fault prediction measure are presented. The measure, refereed to as a QALP score, makes use of techniques from information retrieval to judge software quality. The QALP score has been shown to correlate with human(More)
Reliability is one of the most important aspects of software systems of any kind (embedded systems, information systems, intelligent systems, etc.) The size and complexity of software is growing dramatically during last decades and especially during last few years. Various methods can be used to achieve the software reliability i.e. software reliability(More)
abstract The design and configuration of a data warehouse can be difficult tasks especially in the case of very large databases and in the presence of redundant information. In particular, the choice of which attributes have to be considered as dimensions and measures can be not trivial and it can heavily influence the effectiveness of the final system. In(More)
This paper presents a new experimental methodology that operates on a series of programs structural parameters. We calculated some simple metrics on these parameters and then we applied linear programming techniques on them. It was therefore possible to define a model that can predict the risk level of a program, namely how prone it is to containing faults.(More)