software is data too
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.
data mining statistical analysis machine learning information retrieval software engineering empirical research