Evolution in software product lines: Two cases
Journal of Software Maintenance: Research and Practice
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Software Product Line Engineering: Foundations, Principles and Techniques
Software Product Line Engineering: Foundations, Principles and Techniques
ACM Computing Surveys (CSUR)
Feature Diagrams and Logics: There and Back Again
SPLC '07 Proceedings of the 11th International Software Product Line Conference
Sample Spaces and Feature Models: There and Back Again
SPLC '08 Proceedings of the 2008 12th International Software Product Line Conference
Reverse engineering feature models
Proceedings of the 33rd International Conference on Software Engineering
On extracting feature models from product descriptions
Proceedings of the Sixth International Workshop on Variability Modeling of Software-Intensive Systems
Identifying improvement potential in evolving product line infrastructures: 3 case studies
Proceedings of the 16th International Software Product Line Conference - Volume 1
Code-based variability model extraction for software product line improvement
Proceedings of the 16th International Software Product Line Conference - Volume 2
Variability evolution and erosion in industrial product lines: a case study
Proceedings of the 17th International Software Product Line Conference
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As a Software Product Line (SPL) evolves with increasing number of features and feature values, the feature correlations become extremely intricate, and the specifications of these correlations tend to be either incomplete or inconsistent with their realizations, causing misconfigurations in practice. In order to guide product configuration processes, we present a solution framework to recover complex feature correlations from existing product configurations. These correlations are further pruned automatically and validated by domain experts. During implementation, we use association mining techniques to automatically extract strong association rules as potential feature correlations. This approach is evaluated using a large-scale industrial SPL in the embedded system domain, and finally we identify a large number of complex feature correlations.