ACM SIGKDD Explorations Newsletter
An improved methodology on information distillation by mining program source code
Data & Knowledge Engineering
Hierarchical Clustering for Software Architecture Recovery
IEEE Transactions on Software Engineering
Clustering for Monitoring Software Systems Maintainability Evolution
Electronic Notes in Theoretical Computer Science (ENTCS)
Fundamental Nano-Patterns to Characterize and Classify Java Methods
Electronic Notes in Theoretical Computer Science (ENTCS)
Comparison and evaluation of source code mining tools and techniques: A qualitative approach
Intelligent Data Analysis
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Data mining is a technology recently used in support of software maintenance in various contexts. Our works focuses on achieving a high level understanding of Java systems without prior familiarity with these. Our thesis is that system structure and interrelationships, as well as similarities among program components can be derived by applying cluster analysis on data extracted from source code. This paper proposes a methodology suitable for Java code analysis. It comprises of a Java code analyser which examines programs and constructs tables representing code syntax, and a clustering engine which operates on such tables and identifies relationships among code elements. We evaluate the methodology on a medium sized system, present initial results and discuss directions for further work.