Name
Abstract | ||
|---|---|---|
During the execution of software, tremendous amounts of data can be recorded. By exploiting the execution data, one can discover behavioral models to describe the actual software execution. As a wellknown open-source process mining toolkit, ProM integrates quantities of process mining techniques and enjoys a variety of applications in a broad range of areas. How to develop a better ProM software, both from user experience and software performance perspective, are of vital importance. To achieve this goal, we need to investigate the real execution behavior of ProM which can provide useful insights on its usage and how it responds to user operations. This paper aims to propose an effective approach to solve this problem. To this end, we first instrument existing ProM framework to capture execution logs without changing its architecture. Then a two-layered framework is introduced to support accurate ProM behavior discovery by characterizing both user interaction behavior and plug-in calling behavior separately. Next, detailed discovery techniques to obtain user interaction behavior model and plug-in calling behavior model are proposed. All proposed approaches have been implemented. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1587/transinf.2017EDP7027 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
software behavior discovery, user behavior, plug-in calling behavior, process mining | Computer vision,Software behavior,Computer science,Human–computer interaction,Artificial intelligence | Journal |
Volume | Issue | ISSN |
E101D | 8 | 1745-1361 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cong Liu | 1 | 128 | 14.67 |
| jianpeng zhang | 2 | 139 | 19.42 |
| Guangming Li | 3 | 4 | 3.10 |
| Shangce Gao | 4 | 486 | 45.41 |
| Qingtian Zeng | 5 | 242 | 43.67 |