RISKY MODULE PREDICTION FOR NUCLEAR I&C SOFTWARE

Software Fault-proneness Prediction using Module Severity Metrics

Most of the fault prediction studies have focused on the binary classification models that determine whether the input modules are fault-prone or not. More recently, several studies have shown that severity-based multi-classification models are more useful since they can predict the fault-proneness depending on the severity of the defects in the module. We present new severity-based prediction ...

IC Performance Prediction for Test Cost Reduction

Ð This paper describes a methodology for building models predicting manufactured integrated circuit performances as a function of inline and wafer electrical test measurements. We show how these predictions can be used to predict the performance of an industrial microprocessor, and reduce the average number of speed bins that must be tested by 45%.

Development of a Software Module for Automating the Quality Control of Quantitative Nuclear Renography

Component Module Classification for Distributed Software Understanding

Effective analysis and evolution of existing distributed software systems rely to great an extent on the ability to recognise implemented executable components, particularly their constituent modules. Traditionally, this information is obtained via manual examination of configuration files and the source code directory hierarchy. However, both types of artifact are limited in distinguishing whi...

A software metric system for module coupling

Low module coupling is considered to be a desirable quality for modular programs to have. Previously, coupling has been de ned subjectively, and not quanti ed, making it di cult to use in practice. In this paper, we extend previous work to re ect newer programming languages, and quantify coupling by developing a general software metric system that allows us to automatically measure coupling. We...