Data mining source code to facilitate program comprehension: experiments on clustering data retrieved from C++ programs

This paper presents ongoing work on using data mining to discover knowledge about software systems thus facilitating program comprehension. We discuss how this work fits in the context of tool supported maintenance and comprehension and report on applying a new methodology on C++ programs. The overa...

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Bibliographic Details
Main Authors: Kanellopoulos, Y., Tjortjis, C.
Format: Conference Proceeding
Language:English
Subjects:
Application software
Applied sciences
Character recognition
Computer languages
Computer science
control theory
systems
Data mining
Data models
Data processing. List processing. Character string processing
Documentation
Exact sciences and technology
Information retrieval
Memory organisation. Data processing
Object oriented modeling
Software
Software engineering
Software maintenance
Software systems
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Description
Summary:This paper presents ongoing work on using data mining to discover knowledge about software systems thus facilitating program comprehension. We discuss how this work fits in the context of tool supported maintenance and comprehension and report on applying a new methodology on C++ programs. The overall framework can provide practical insights and guide the maintainer through the specifics of systems, assuming little familiarity with these. The contribution of this work is two-fold: it provides a model and associated method to extract data from C++ source code which is subsequently to be mined, and evaluates a proposed framework for clustering such data to obtain useful knowledge. The methodology is evaluated on three open source applications, results are assessed and conclusions are presented. This paper concludes with directions for future work.
ISSN:1092-8138
DOI:10.1109/WPC.2004.1311063