Software engineering (3rd ed.): a practitioner's approach
Software engineering (3rd ed.): a practitioner's approach
Object-oriented metrics that predict maintainability
Journal of Systems and Software - Special issue on object-oriented software
Characterizing and modeling the cost of rework in a library of reusable software components
ICSE '97 Proceedings of the 19th international conference on Software engineering
An investigation into coupling measures for C++
ICSE '97 Proceedings of the 19th international conference on Software engineering
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Reusability Hypothesis Verification using Machine Learning Techniques: A Case Study
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
Using Coupling Measurement for Impact Analysis in Object-Oriented Systems
ICSM '99 Proceedings of the IEEE International Conference on Software Maintenance
Machine-Learning Techniques for Software Product Quality Assessment
QSIC '04 Proceedings of the Quality Software, Fourth International Conference
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
Software metrics play a major role in predicting software quality; they help project managers in decision-making. Indeed, software metrics provide a quantitative approach allowing the control and the improvement of the development process including the maintenance. The ISO/IEC international standard (14598) on software product quality states, “Internal metrics are of little value unless there is evidence that they are related to external quality”. Many empirical prediction models are presented in the literature; their goal is to investigate the relationship between internal metrics and external qualities, in order to assess software quality. In this paper, we explore different machine-learning (ML) algorithms provided by an open source data-mining environment. We analyse their capacities to produce accurate and usable predictive models.