Inferring operational requirements from scenarios and goal models using inductive learning
Proceedings of the 2006 international workshop on Scenarios and state machines: models, algorithms, and tools
A Doctrine of Cognitive Informatics (CI)
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)
Combining finite learning automata with GSAT for the satisfiability problem
Engineering Applications of Artificial Intelligence
A novel composite model approach to improve software quality prediction
Information and Software Technology
Bayesian reasoning for software testing
Proceedings of the FSE/SDP workshop on Future of software engineering research
Testing and validating machine learning classifiers by metamorphic testing
Journal of Systems and Software
Handling missing data in software effort prediction with naive Bayes and EM algorithm
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
A framework for integrated software quality prediction using Bayesian nets
ICCSA'11 Proceedings of the 2011 international conference on Computational science and Its applications - Volume Part V
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
A machine learning application for human resource data mining problem
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Modeling software component criticality using a machine learning approach
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
A Doctrine of Cognitive Informatics (CI)
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)
A Value-Based Framework for Software Evolutionary Testing
International Journal of Software Science and Computational Intelligence
Controversy Corner: Search Based Software Engineering: Review and analysis of the field in Brazil
Journal of Systems and Software
A learning-based method for combining testing techniques
Proceedings of the 2013 International Conference on Software Engineering
| Hi-index | 0.00 |
Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. They are particularly useful for (a) poorly understood problem domains where little knowledge exists for the humans to develop effective algorithms; (b) domains where there are large databases containing valuable implicit regularities to be discovered; or (c) domains where programs must adapt to changing conditions. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This paper deals with the subject of applying machine learning in software engineering. In the paper, we first provide the characteristics and applicability of some frequently utilized machine learning algorithms. We then summarize and analyze the existing work and discuss some general issues in this niche area. Finally we offer some guidelines on applying machine learning methods to software engineering tasks and use some software development and maintenance tasks as examples to show how they can be formulated as learning problems and approached in terms of learning algorithms.