Enhance Rule Based Detection for Software Fault Prone Modules
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Summary
Results show that the enhanced RIDOR algorithm is better than other classification techniques in terms of the number of extracted rules and accuracy, and the implemented algorithm learns defect prediction using mining static code attributes and presents a new defect predictor with high accuracy and low error rate.
- Type
- article
- Published
- 2012-01-01
- Cited by
- 29
- References
- 28
- Access
- Open access
- OpenAlex
- https://openalex.org/W25198546
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15280358
Keywords
Data mining, Computer science, Software, Software quality, Fault (geology)
References
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- Quantitative Analysis of Faults and Failures in a Complex Software System
- Predicting Defects for Eclipse
- Classification With Ant Colony Optimization
- What we have learned about fighting defects
- Data Mining Practical Machine Learning Tools and Techniques
- Data Mining Static Code Attributes to Learn Defect Predictors
- A survey and taxonomy of approaches for mining software repositories in the context of software evolution
- A Complexity Measure
- Data Mining Concepts and Techniques Third Edition
Cited by
- Improved Random Forest Algorithm for Software Defect Prediction through Data Mining Techniques
- Predicting fault-prone software modules using feature selection and classification through data mining algorithms
- Assessing Software Reliability Based on NHPP Using SPC
- Burr Type III Software Reliability Growth Model
- A Study on Software Metrics based Software Defect Prediction using Data Mining and Machine Learning Techniques
- A Comparative Study of Different Software Fault Prediction and Classification Techniques
- Intelligent Reduction in Signaling Load of Location Management in Mobile Data Networks
- Complexity-based Prediction of Faults Number for Software Modules Ranking Before Testing: Technique and Case Study
- Software defect prediction in large space systems through hybrid feature selection and classification
- Software attributes that impact popularity
- A Nonlinear Manifold Detection based Model for Software Defect Prediction
- Predicting Defect-Prone Software Modules Using Shifted-Scaled Dirichlet Distribution
- A Survey on Software Defect Prediction in Cross Project
- Implication of Data Mining and Machine Learning in Software Engineering Domain for Software Model, Quality and Defect Prediction
- Software Reusability of Object-Oriented Systems using Data Mining Techniques
- Performance Evaluation of Data Mining Techniques to Enhance the Reusability of Object-Oriented (O-O) Systems
- Study of Data Mining Techniques for Software Quality Assurance
- A Survey of Different Software Fault Prediction Using Data Mining Techniques Methods
- A RULE-BASED PREDICTION METHOD FOR DEFECT DETECTION IN SOFTWARE SYSTEM
- Analysis of Software Fault and Defect Prediction by Fuzzy C-Means Clustering and Adaptive Neuro Fuzzy C-Means Clustering
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