Training on errors experiment to detect fault-prone software modules by spam filter
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Regression via Classification applied on software defect estimation
Expert Systems with Applications: An International Journal
An extension of fault-prone filtering using precise training and a dynamic threshold
Proceedings of the 2008 international working conference on Mining software repositories
Journal of Software Maintenance and Evolution: Research and Practice
Accuracy and efficiency comparisons of single- and multi-cycled software classification models
Information and Software Technology
Prediction of Fault-Prone Software Modules Using a Generic Text Discriminator
IEICE - Transactions on Information and Systems
Fault-prone module detection using large-scale text features based on spam filtering
Empirical Software Engineering
An integrated approach to detect fault-prone modules using complexity and text feature metrics
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
Software metrics reduction for fault-proneness prediction of software modules
NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Empirical study of Software Quality estimation
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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Over the last years, software quality has become one of the most important requirement in thedevelopment of systems. Fault-proneness estimation could play a key role in quality control of softwareproducts. In this area, much effort has been spent in defining metrics and identifying models for systemassessment. Using these metrics to assess which parts of the system are more fault-proneness is of primaryimportance. This paper reports a research study begun with the analysis of more than 100 metrics and aimed at producing suitable models for fault-pronenessestimation and prediction of software modules/files. The objective has been to find a compromise between the fault-proneness estimation rate and the size of theestimation model in terms of number of metrics used in the model itself. To this end, two differentmethodologies have been used, compared, and some synergies exploited. The methodologies were the logisticregression and the discriminant analyses. Thecorresponding models produced for fault-proneness estimation and prediction have been based on metricsaddressing different aspects of computer programming. The comparison has produced satisfactory results in terms of fault-proneness prediction. The produced models have been cross validated by using data sets derived from source codes provided by two applicationscenarios.