Analytics for software development
Proceedings of the FSE/SDP workshop on Future of software engineering research
Code clone detection experience at microsoft
Proceedings of the 5th International Workshop on Software Clones
ICST '11 Proceedings of the 2011 Fourth IEEE International Conference on Software Testing, Verification and Validation
Software analytics as a learning case in practice: approaches and experiences
Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering
Performance debugging in the large via mining millions of stack traces
Proceedings of the 34th International Conference on Software Engineering
ReBucket: a method for clustering duplicate crash reports based on call stack similarity
Proceedings of the 34th International Conference on Software Engineering
Pathways to technology transfer and adoption: achievements and challenges (mini-tutorial)
Proceedings of the 2013 International Conference on Software Engineering
Software analytics: achievements and challenges
Proceedings of the 2013 International Conference on Software Engineering
Report on the international symposium on high confidence software (ISHCS 2011/2012)
ACM SIGSOFT Software Engineering Notes
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A huge wealth of various data exist in the practice of software development. Further rich data are produced by modern software and services in operation, many of which tend to be data-driven and/or data-producing in nature. Hidden in the data is information about the quality of software and services and the dynamics of software development. Software analytics is to develop and apply data exploration and analysis technologies, such as pattern recognition, machine learning, and information visualization, on software data to obtain insightful and actionable information for modern software and services. This tutorial presents latest research and practice on principles, techniques, and applications of software analytics in practice, highlighting success stories in industry, research achievements that are transferred to industrial practice, and future research and practice directions in software analytics. The attendees can acquire the skills and knowledge needed to perform industrial research or conduct industrial practice in the field of software analytics and to integrate analytics in their own industrial research, practice, and training.