Communications of the ACM - Special issue on parallelism
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Machine Learning
Machine Learning and Data Mining; Methods and Applications
Machine Learning and Data Mining; Methods and Applications
An Investigation of Analysis Techniques for Software Datasets
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
An Empirical Study of the Impact of Count Models Predictions on Module-Order Models
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Machine Learning and Software Engineering
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Empirical Assessment of Machine Learning based Software Defect Prediction Techniques
WORDS '05 Proceedings of the 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems
Optimal Project Feature Weights in Analogy-Based Cost Estimation: Improvement and Limitations
IEEE Transactions on Software Engineering
A Unified Framework for Defect Data Analysis Using the MBR Technique
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
The Soft Computing Approach to Program Development Time Estimation
ICIT '06 Proceedings of the 9th International Conference on Information Technology
Estimating Software Quality with Advanced Data Mining Techniques
ICSEA '06 Proceedings of the International Conference on Software Engineering Advances
Unsupervised learning for expert-based software quality estimation
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
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This paper explores software quality improvement through early prediction of error patterns. It summarizes a variety of techniques for software quality prediction in the domain of software engineering. The objective of this research is to apply the various machine learning approaches, such as Case-Based Reasoning and Fuzzy logic, to predict software quality. The system predicts the error after accepting the values of certain parameters of the software. This paper advocates the use of case-based reasoning (i.e., CBR) to build a software quality prediction system with the help of human experts. The prediction is based on analogy. We have used different similarity measures to find the best method that increases reliability. This software is compiled using Turbo C++ 3.0 and hence it is very compact and standalone. It can be readily deployed on any configuration without affecting its performance.