Comparing models for identifying fault-prone software components
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Summary
An empirical investigation of the modeling techniques for identifying fault-prone software components early in the software life cycle finds that no model is able to discriminate between components with faults and components without faults.
- Type
- article
- Published
- 1995-01-01
- Cited by
- 88
- References
- 32
- OpenAlex
- https://openalex.org/W3040557
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16449465
Keywords
Computer science, Software quality, Data mining, Principal component analysis, Software
References
- Developing and Analyzing Classification Rules for Predicting Faulty Software Components
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- A Neural Net-Based Approach to Software Metrics
- A methodology for integrating maintainability using software metrics
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- Programs for Machine Learning. Part I
- Software errors and complexity: an empirical investigation0
- Empirically guided software development using metric-based classification trees
- Methodology For Validating Software Metrics
- The Detection of Fault-Prone Programs
- C4.5: Programs for Machine Learning
- Developing Interpretable Models with Optimized Set Reduction for Identifying High-Risk Software Components
- Validating metrics for ensuring Space Shuttle flight software quality
- The TAME Project: Towards Improvement-Oriented Software Environments
- Improving code churn predictions during the system test and maintenance phases
- A comparative study of predictive models for program changes during system testing and maintenance
- A Holographic Model of Memory, Learning and Expression
Cited by
- An experimental study of cost cognizant test case prioritization
- A Fault-Based Model of Fault Localization Techniques
- Evaluating Empirical Models for the Detection of High-Risk Components: Some Lessons Learned
- Fault-Threshold Prediction with Linear Programming Methodologies
- Application of Neural Networks in Software Engineering: A Review
- Software Maintenance Severity Prediction with Soft Computing Approach
- Quality prediction modelling for software customisation in the absence of defect data
- An empirical approach for software fault prediction
- A Neural network based approach for modeling of severity of defects in function based software systems
- Regression via Classification applied on software defect estimation
- A Density Based Clustering approach for early detection of fault prone modules
- A model for early prediction of faults in software systems
- The importance of replications in software engineering: a case study in defect prediction
- A Frame Work for Business Defect Predictions in Mobiles
- Software fault prediction: A literature review and current trends
- A soft computing approach for modeling of severity of faults in software systems
- Cost-cognizant Test Case Prioritization
- Software Defect Prediction Using Regression via Classification
- towards a framework for fault and failure prediction and estimation
- Critical components testing using hybrid genetic algorithm
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