Predicting fault-prone software modules using feature selection and classification through data mining algorithms
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- Type
- article
- Published
- 2012-12-01
- Cited by
- 16
- References
- 17
- OpenAlex
- https://openalex.org/W2025193835
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:12977202
Keywords
Computer science, Data mining, Feature selection, Statistical classification, Machine learning
References
- Discovery of Knowledge Patterns in Clinical Data through Data Mining Algorithms: Multi-class Categorization of Breast Tissue Data
- Enhance Rule Based Detection for Software Fault Prone Modules
- Handbook of Statistical Analysis and Data Mining Applications
- Data Mining - Concepts and Techniques
- Applications of data mining in software engineering
- Software Defect Prediction Based on Association Rule Classification
- Discovering data mining: from concept to implementation
- A General Software Defect-Proneness Prediction Framework
- Performance Analysis of Datamining Algorithms for Software Quality Prediction
- Software Defect Detection with Rocus
- Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Second Edition
- Web data mining: exploring hyperlinks, contents, and usage data
Cited by
- Improved Random Forest Algorithm for Software Defect Prediction through Data Mining Techniques
- A Review of Estimation Techniques to Reduce Testing Efforts in Software Development
- A systematic review of machine learning techniques for software fault prediction
- Online Match-Making Recommendation using Case Based Reasoning and K-Nearest- Neighbors
- Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques
- Software defect prediction in large space systems through hybrid feature selection and classification
- A comparative analysis of soft computing techniques in software fault prediction model development
- Empirical Study of Software Defect Prediction: A Systematic Mapping
- Predicting and Classifying Software Faults: A Data Mining Approach
- How Machine Learning Has Been Applied in Software Engineering?
- Development of partial least squares regression with discriminant analysis for software bug prediction
- ML@SE: What do we know about how Machine Learning impact Software Engineering practice?
- An Empirical Study of Predicting Fault-prone Components and their Evolution
- An Empirical Study on Common Defects in Modern Web Browsers Using Knowledge Embedding in GPT-4o
- An Empirical Analysis of Software Fault Proneness Using Factor Analysis with Regression
- Survey on Crop Yield Prediction Using Data Mining Techniques
- Systematic Literature Review on the Machine Learning Approach in Software Engineering
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