数据驱动的双层次软件过程挖掘方法

为了解决软件过程数据因活动信息及案例属性的缺失而无法应用传统过程挖掘方法的问题，以软件过程数据为研究对象，提出了一种双层次的软件过程挖掘方法.在活动层，提出加权结构连接向量模型对过程日志进行向量化，通过平均活动熵来确定过程日志模糊聚类的结果，将聚类结果作为活动信息支持后续挖掘工作的开展；在过程层，以启发式关系度量为基础，针对非完全循环进行研究，提出了过程层单触发序列循环划分的日志完备性条件，并进一步给出了循环归属的度量方法.基于大量真实软件过程数据的实验结果表明了双层次的软件过程挖掘方法的可行性及正确性.

Data-Driven Bilayer Software Process Mining

To address the issue of difficulty in applying the traditional process mining on software process data due to the deficiency of activity and case attribute, this paper focus on the software process data and proposes a bilayer software process mining approach. In the mining activity layer, a weighted structured linked vector mode is proposed to vectorize the process log. The result of fuzzy clustering, which can be regarded as activity information, is determined by the average activity entropy. In the process layer, based on the heuristic relation metrics, this paper studies the non-complete cycle situation and presents the single firing sequence of loop dividing condition of log completeness, and then proposes a method to measure the affiliation of loop. The real-world data sets are used to show the effectiveness and correctness of the proposed bilayer software process mining.