Name
Abstract | ||
|---|---|---|
For some time now, researchers have been seeking to place software measurement on a more firmly grounded footing by establishing a theoretical basis for software comparison. Although there has been some work on trying to employ information theoretic concepts for the quantification of code documents, particularly on employing entropy and entropy-like measurements, we propose that employing the Similarity Metric of Li, Vitanyi, and coworkers for the comparison of software documents will lead to the establishment of a theoretically justifiable means of comparing and evaluating software artifacts. In this paper, we review previous work on software measurement with a particular emphasis on information theoretic aspects, we examine the body of work on Kolmogorov complexity (upon which the Similarity Metric is based), and we report on some experiments that lend credence to our proposals. Finally, we discuss the potential advantages derived from the application of this theory to areas in the field of software engineering. I. INTRODUCTION |
Year | Venue | Keywords |
|---|---|---|
2007 | SEKE | software engineering,software measurement |
Field | DocType | Citations |
Data mining,Software artifacts,Kolmogorov complexity,Computer science,Software,Software metric,Software quality,Software measurement,Credence,Software sizing | Conference | 9 |
PageRank | References | Authors |
0.47 | 34 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tom Arbuckle | 1 | 9 | 0.81 |
| Adam Balaban | 2 | 9 | 0.47 |
| Dennis Peters | 3 | 193 | 15.71 |
| Mark Lawford | 4 | 189 | 24.72 |