Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Data mining: concepts and techniques
Data mining: concepts and techniques
Business Intelligence
Detection of Logical Coupling Based on Product Release History
ICSM '98 Proceedings of the International Conference on Software Maintenance
Active learning for automatic classification of software behavior
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Mining API patterns as partial orders from source code: from usage scenarios to specifications
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Parseweb: a programmer assistant for reusing open source code on the web
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Journal of Software Maintenance and Evolution: Research and Practice
An approach to detecting duplicate bug reports using natural language and execution information
Proceedings of the 30th international conference on Software engineering
Future of Mining Software Archives: A Roundtable
IEEE Software
Mining exception-handling rules as sequence association rules
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Improving software quality via code searching and mining
SUITE '09 Proceedings of the 2009 ICSE Workshop on Search-Driven Development-Users, Infrastructure, Tools and Evaluation
The promises and perils of mining git
MSR '09 Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories
Data Mining for Software Engineering
Computer
SpotWeb: Detecting Framework Hotspots and Coldspots via Mining Open Source Code on the Web
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Alattin: Mining Alternative Patterns for Detecting Neglected Conditions
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
Inferring Resource Specifications from Natural Language API Documentation
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
Mining software engineering data
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2
Software analytics as a learning case in practice: approaches and experiences
Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering
Software mining and fault prediction
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Informing development decisions: from data to information
Proceedings of the 2013 International Conference on Software Engineering
Data stream mining for predicting software build outcomes using source code metrics
Information and Software Technology
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Mining software engineering data has emerged as a successful research direction over the past decade. In this position paper, we advocate Software Intelligence (SI) as the future of mining software engineering data, within modern software engineering research, practice, and education. We coin the name SI as an inspiration from the Business Intelligence (BI) field, which offers concepts and techniques to improve business decision making by using fact-based support systems. Similarly, SI offers software practitioners (not just developers) up-to-date and pertinent information to support their daily decision-making processes. SI should support decision-making processes throughout the lifetime of a software system not just during its development phase. The vision of SI has yet to become a reality that would enable software engineering research to have a strong impact on modern software practice. Nevertheless, recent advances in the Mining Software Repositories (MSR) field show great promise and provide strong support for realizing SI in the near future. This position paper summarizes the state of practice and research of SI, and lays out future research directions for mining software engineering data to enable SI.