User Behavior Discovery from Low-Level Software Execution Log
User Behavior Discovery from Low-Level Software Execution Log
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从低级软件执行日志发现用户行为
DOI：
10.1002/tee.22727
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发表时间：
2018
影响因子：
1
通讯作者：
Cheng Jiujun
中科院分区：
文献类型：
--
作者：
Liu Cong;Wang Shi;Gao Shangce;Zhang Feng;Cheng Jiujun
在软件执行过程中，可以记录软件的执行日志。这提供了有关软件运行时分析的有价值的信息，基于这些信息，软件开发人员和供应商可以找到有关用户行为的有用见解，并实现软件可用性改进。然而，记录的软件执行日志通常是在这样一个细粒度（通常在方法调用级别），它不能反映真实的用户行为显式。为此，我们提出了一种监督学习方法，从低级软件执行日志中检测高级用户操作行为。更具体地说，我们首先为每个用户操作构建行为模式（用Petri网表示）。然后，我们应用基于对齐的匹配方法，该方法将一组行为模式和软件执行日志作为输入，以生成抽象的用户操作日志。接着，利用现有的进程发现方法，从获取的用户操作日志中发现用户行为模型。所提出的方法得到了开源流程挖掘工具包ProM中实现的工具的支持。通过实验分析，我们证明了所提出的方法有利于发现有见地的用户行为，具有高精度的软件执行日志。© 2018日本电气工程师协会。由John Wiley & Sons公司出版
During the execution of software, its execution log can be recorded. This provides valuable information on software runtime analysis, based on which software developers and vendors can find useful insights into how users behave and enable software usability improvements. However, the recorded software execution log usually is in such a fine granularity (usually in the method‐call level) that it cannot reflect real user behavior explicitly. To this end, we propose a supervised learning approach to detect high‐level user operation behavior from low‐level software execution log. More specifically, we first construct a behavioral pattern (represented by Petri nets) for each user operation. Then, we apply an alignment‐based matching approach which takes a set of behavior patterns and a software execution log as input to generate an abstract user operation log. Next, existing process discovery approaches are used to discover user behavior model from the obtained user operation log. The proposed approaches are supported by tools implemented in the open‐source process mining toolkit ProM. By experimental analysis, we demonstrate that the proposed approach facilitates the discovery of insightful user behavior with high accuracy from the software execution log. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.