Data integration: a theoretical perspective
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Enterprise information integration: successes, challenges and controversies
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Advancing Candidate Link Generation for Requirements Tracing: The Study of Methods
IEEE Transactions on Software Engineering
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
ACM Transactions on Software Engineering and Methodology (TOSEM)
Data Mining for Software Engineering
Computer
Weighted proportional k-interval discretization for naive-Bayes classifiers
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Proceedings of the 50th Annual Southeast Regional Conference
Goldfish bowl panel: software development analytics
Proceedings of the 34th International Conference on Software Engineering
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Software systems are designed and engineered to process data. However, software is data too. The size and variety of today's software artifacts and the multitude of stakeholder activities result in so much data that individuals can no longer reason about all of it. We argue in this position paper that data mining, statistical analysis, machine learning, information retrieval, data integration, etc., are necessary solutions to deal with software data. New research is needed to adapt existing algorithms and tools for software engineering data and processes, and new ones will have to be created. In order for this type of research to succeed, it should be supported with new approaches to empirical work, where data and results are shared globally among researchers and practitioners. Software engineering researchers can get inspired by other fields, such as, bioinformatics, where results of mining and analyzing biological data are often stored in databases shared across the world.