The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Practical results from measuring software quality
Communications of the ACM
Handbook of software reliability engineering
Handbook of software reliability engineering
A predictive metric based on discriminant statistical analysis
ICSE '97 Proceedings of the 19th international conference on Software engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Towards More Optimal Medical Diagnosing with Evolutionary Algorithms
Journal of Medical Systems
Software Metrics: A Rigorous and Practical Approach
Software Metrics: A Rigorous and Practical Approach
Decision Trees: An Overview and Their Use in Medicine
Journal of Medical Systems
A Phase-Based Approach to Creating Highly Reliable Software
COMPSAC '00 24th International Computer Software and Applications Conference
Prediction Models for Software Fault Correction Effort
CSMR '01 Proceedings of the Fifth European Conference on Software Maintenance and Reengineering
A logarithmic poisson execution time model for software reliability measurement
ICSE '84 Proceedings of the 7th international conference on Software engineering
Constructing a Model-Based Software Monitor for the Insulin Pump Behavior
Journal of Medical Systems
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Software reliability analysis is inevitable for modern medical systems, since a large amount of medical system functionality is now dependent on software, and software does contribute to system failures. Most software reliability models are based on software failure data collected from the project. This creates a problem for the designers since, during the early stage, software failure data are not available. However, a valuable knowledge can be learned from the analysis of previous projects and applied to the new ones. This paper presents the approach that predicts the potentially dangerous software modules under development based on the analysis of the already finished modules using the machine-learning techniques. On the basis of the prediction given by our method software designers are able to devote more testing effort to the dangerous parts of the system, which results in a more reliable medical software system.