Name
Title | ||
|---|---|---|
Mining source code elements for comprehending object-oriented systems and evaluating their maintainability |
Abstract | ||
|---|---|---|
Data mining and its capacity to deal with large volumes of data and to uncover hidden patterns has been proposed as a means to support industrial scale software maintenance and comprehension. This paper presents a methodology for knowledge acquisition from source code in order to comprehend an object-oriented system and evaluate its maintainability. We employ clustering in order to support semi-automated software maintenance and comprehension.A model and an associated process are provided, in order to extract elements from source code; K-Means clustering is then applied on these data, in order to produce system overviews and deductions. The methodology is evaluated on JBoss, a very large Open Source Application Server; results are discussed and conclusions are presented together with directions for future work. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1147234.1147240 | SIGKDD Explorations |
Keywords | Field | DocType |
k-means clustering,data mining,semi-automated software maintenance,large volume,object-oriented system,code mining,maintainability.,large open source,software maintenance,program comprehension,metrics,source code,application server,system overviews,mining source code element,clustering,industrial scale software maintenance,maintainability,k means clustering | Static program analysis,Data mining,Object-oriented programming,Computer science,Source code,KPI-driven code analysis,Software maintenance,Cluster analysis,Program comprehension,Maintainability | Journal |
Volume | Issue | Citations |
8 | 1 | 13 |
PageRank | References | Authors |
0.81 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiannis Kanellopoulos | 1 | 94 | 8.52 |
| Thimios Dimopulos | 2 | 13 | 0.81 |
| Christos Tjortjis | 3 | 173 | 24.40 |
| Christos Makris | 4 | 263 | 21.94 |