A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Predictive Modeling Techniques of Software Quality from Software Measures
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
C4.5: programs for machine learning
C4.5: programs for machine learning
IEEE Transactions on Software Engineering - Special issue on software reliability
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Software quality classification model based on McCabe's complexity measure
Journal of Systems and Software - Special issue on achieving quality in software
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Proceedings of the Conference on The Future of Software Engineering
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
The Practical Implementation of Software Metrics
The Practical Implementation of Software Metrics
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Applying Reliability Models More Effectively
IEEE Software
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Model Combination in the Multiple-Data-Batches Scenario
ECML '97 Proceedings of the 9th European Conference on Machine Learning
A New Representation And Crossover Operator For Search-based Optimization Of Software Modularization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Machine Learning and Software Engineering
Software Quality Control
Combining Software Quality Predictive Models: An Evolutionary Approach
ICSM '02 Proceedings of the International Conference on Software Maintenance (ICSM'02)
Classification and regression by combining models
Classification and regression by combining models
Genetic Programming-Based Decision Trees for Software Quality Classification
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Resource-oriented software quality classification models
Journal of Systems and Software
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
Predicting software defects in varying development lifecycles using Bayesian nets
Information and Software Technology
Adaptive mixtures of local experts
Neural Computation
Reducing overfitting in genetic programming models for software quality classification
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
IEEE Transactions on Neural Networks
A genetic algorithm to enhance transmembrane helices prediction
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Ant colony based approach to predict stock market movement from mood collected on Twitter
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Journal of Systems and Software
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Context:: How can quality of software systems be predicted before deployment? In attempting to answer this question, prediction models are advocated in several studies. The performance of such models drops dramatically, with very low accuracy, when they are used in new software development environments or in new circumstances. Objective: The main objective of this work is to circumvent the model generalizability problem. We propose a new approach that substitutes traditional ways of building prediction models which use historical data and machine learning techniques. Method: In this paper, existing models are decision trees built to predict module fault-proneness within the NASA Critical Mission Software. A genetic algorithm is developed to combine and adapt expertise extracted from existing models in order to derive a ''composite'' model that performs accurately in a given context of software development. Experimental evaluation of the approach is carried out in three different software development circumstances. Results: The results show that derived prediction models work more accurately not only for a particular state of a software organization but also for evolving and modified ones. Conclusion: Our approach is considered suitable for software data nature and at the same time superior to model selection and data combination approaches. It is then concluded that learning from existing software models (i.e., software expertise) has two immediate advantages; circumventing model generalizability and alleviating the lack of data in software-engineering.