Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.datak.2006.06.002 | Data Knowl. Eng. |
Keywords | Field | DocType |
software maintenance issues,improved methodology,improved version,mms apriori,industrial case study,associated model,knowledge acquisition methods,program comprehension,mining program source code,source code,systems specific,k-means clustering,information distillation,association rules mining,member data,knowledge acquisition,data/code mining,software maintenance,object oriented,data mining,association rule mining,k means clustering | Static program analysis,Data mining,Data stream mining,Information retrieval,Computer science,Source code,Association rule learning,KPI-driven code analysis,Software maintenance,Cluster analysis,Program comprehension,Database | Journal |
Volume | Issue | ISSN |
61 | 2 | 0169-023X |
Citations | PageRank | References |
8 | 0.58 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiannis Kanellopoulos | 1 | 94 | 8.52 |
| Christos Makris | 2 | 129 | 20.22 |
| Christos Tjortjis | 3 | 173 | 24.40 |