Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Practical Software Maintenance: Best Practices for Managing Your Software Investment
Practical Software Maintenance: Best Practices for Managing Your Software Investment
Software Engineering
Software Engineering Economics
Software Engineering Economics
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Using Automatic Process Clustering for Design Recovery and Distributed Debugging
IEEE Transactions on Software Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
From System Comprehension to Program Comprehension
COMPSAC '02 Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment
Software Botryology, Automatic Clustering of Software Systems
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
Architectural Design Recovery Using Data Mining Techniques
CSMR '00 Proceedings of the Conference on Software Maintenance and Reengineering
Code Reading: The Open Source Perspective, Vol. 1
Code Reading: The Open Source Perspective, Vol. 1
Experiments with Clustering as a Software Remodularization Method
WCRE '99 Proceedings of the Sixth Working Conference on Reverse Engineering
Practices of Software Maintenance
ICSM '98 Proceedings of the International Conference on Software Maintenance
Identification of Data Cohesive Subsystems Using Data Mining Techniques
ICSM '98 Proceedings of the International Conference on Software Maintenance
Bunch: A Clustering Tool for the Recovery and Maintenance of Software System Structures
ICSM '99 Proceedings of the IEEE International Conference on Software Maintenance
Understanding Program Understanding
IWPC '00 Proceedings of the 8th International Workshop on Program Comprehension
Facilitating Program Comprehension by Mining Association Rules from Source Code
IWPC '03 Proceedings of the 11th IEEE International Workshop on Program Comprehension
Comprehending a Complex, Distributed, Object-Oriented Software System a Report from the Field
IWPC '99 Proceedings of the 7th International Workshop on Program Comprehension
Expert Maintainers' Strategies and Needs when Understanding Software: A Case Study Approach
APSEC '01 Proceedings of the Eighth Asia-Pacific on Software Engineering Conference
IWPC '04 Proceedings of the 12th IEEE International Workshop on Program Comprehension
Software Clustering Based on Dynamic Dependencies
CSMR '05 Proceedings of the Ninth European Conference on Software Maintenance and Reengineering
Clustering Data Retrieved from Java Source Code to Support Software Maintenance: A Case Study
CSMR '05 Proceedings of the Ninth European Conference on Software Maintenance and Reengineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Code Quality: The Open Source Perspective (Effective Software Development Series)
Code Quality: The Open Source Perspective (Effective Software Development Series)
Information Sciences: an International Journal
Evaluating data mining algorithms using molecular dynamics trajectories
International Journal of Data Mining and Bioinformatics
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This paper presents a methodology for knowledge acquisition from source code. We use data mining to support semi-automated software maintenance and comprehension and provide practical insights into systems specifics, assuming one has limited prior familiarity with these systems. We propose a methodology and an associated model for extracting information from object oriented code by applying clustering and association rules mining. K-means clustering produces system overviews and deductions, which support further employment of an improved version of MMS Apriori that identifies hidden relationships between classes, methods and member data. The methodology is evaluated on an industrial case study, results are discussed and conclusions are drawn.