Recovering Traceability Links between Code and Documentation
IEEE Transactions on Software Engineering
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
A cooperative classification mechanism for search and retrieval software components
Proceedings of the 2007 ACM symposium on Applied computing
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Sourcerer: mining and searching internet-scale software repositories
Data Mining and Knowledge Discovery
Harvesting Large-Scale Grids for Software Resources
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
How Software Developers Use Tagging to Support Reminding and Refinding
IEEE Transactions on Software Engineering
A search engine for finding highly relevant applications
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Proceedings of the 1st Workshop on Web 2.0 for Software Engineering
Random k-Labelsets for Multilabel Classification
IEEE Transactions on Knowledge and Data Engineering
MULAN: A Java Library for Multi-Label Learning
The Journal of Machine Learning Research
WTCluster: utilizing tags for web services clustering
ICSOC'11 Proceedings of the 9th international conference on Service-Oriented Computing
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A key challenge for Grid and Cloud infrastructures is to make their services easily accessible and attractive to end-users. In this paper we introduce tagging capabilities to the Miner soft system, a powerful tool for software search and discovery in order to help end-users locate application software suitable to their needs. Miner soft is now able to predict and automatically assign tags to software resources it indexes. In order to achieve this, we model the problem of tag prediction as a multi-label classification problem. Using data extracted from production-quality Grid and Cloud computing infrastructures, we evaluate an important number of multi-label classifiers and discuss which one and with what settings is the most appropriate for use in the particular problem.