Visible to the public A Meta-learning Framework for Algorithm Recommendation in Software Fault Prediction

TitleA Meta-learning Framework for Algorithm Recommendation in Software Fault Prediction
Publication TypeConference Paper
Year of Publication2016
Authorsdas Dôres, Silvia N., Alves, Luciano, Ruiz, Duncan D., Barros, Rodrigo C.
Conference NameProceedings of the 31st Annual ACM Symposium on Applied Computing
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-3739-7
Keywordsalgorithm recommendation, composability, machine learning, meta-learning, pubcrawl, Scalability, software assurance, software fault prediction, software quality

Software fault prediction is a significant part of software quality assurance and it is commonly used to detect faulty software modules based on software measurement data. Several machine learning based approaches have been proposed for generating predictive models from collected data, although none has become standard given the specificities of each software project. Hence, we believe that recommending the best algorithm for each project is much more important and useful than developing a single algorithm for being used in any project. For achieving that goal, we propose in this paper a novel framework for recommending machine learning algorithms that is capable of automatically identifying the most suitable algorithm according to the software project that is being considered. Our solution, namely SFP-MLF, makes use of the meta-learning paradigm in order to learn the best learner for a particular project. Results show that the SFP-MLF framework provides both the best single algorithm recommendation and also the best ranking recommendation for the software fault prediction problem.

Citation Keydas_dores_meta-learning_2016