Fuzzy engineering
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Information Retrieval
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An Application of Fuzzy Clustering to Software Quality Prediction
ASSET '00 Proceedings of the 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology (ASSET'00)
Application of Neural Networks for Software Quality Prediction Using Object-Oriented Metrics
ICSM '03 Proceedings of the International Conference on Software Maintenance
Learning Early Lifecycle IV&V Quality Indicators
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
Extract Rules from Software Quality Prediction Model Based on Neural Network
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
A Novel Method for Early Software Quality Prediction Based on Support Vector Machine
ISSRE '05 Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Early Software Quality Prediction Based on a Fuzzy Neural Network Model
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
Fault Prediction using Early Lifecycle Data
ISSRE '07 Proceedings of the The 18th IEEE International Symposium on Software Reliability
Comparing design and code metrics for software quality prediction
Proceedings of the 4th international workshop on Predictor models in software engineering
Implications of ceiling effects in defect predictors
Proceedings of the 4th international workshop on Predictor models in software engineering
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Early prediction of defect prone modules helps in better resource planning, test planning and reducing the cost of defect correction in later stages of software lifecycle. Early prediction models based on design and code metrics are difficult to develop because precise values of the model inputs are not available. Conventional prediction techniques require exact inputs, therefore such models cannot always be used for early predictions. Innovative prediction methods that use imprecise inputs, however, can be applied to overcome the requirement of exact inputs. This paper presents a fuzzy inference system (FIS) that predicts defect proneness in software using vague inputs defined as fuzzy linguistic variables. The paper outlines the methodology for developing the FIS and applies the model to a real dataset. Performance analysis in terms of recall, accuracy, misclassification rate and a few other measures has been conducted resulting in useful insight to the FIS application. The FIS model predictions at an early stage have been compared with conventional prediction methods (i.e. classification trees, linear regression and neural networks) based on exact values. In case of the FIS model, the maximum and the minimum performance shortfalls were noticed for true negative rate (TNRate) and F measure respectively. Whereas for Recall, the FIS model performed better than the other models even with the imprecise inputs.