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Home > Archive>Volume 29, Issue 11, 2018 >3455-3483. DOI:10.13328/j.cnki.jos.005304
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Data-Driven Bilayer Software Process Mining
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National Natural Science Foundation of China (61662085, 61862065); Project of Yunnan Provincial Department of Education Science Research Fund (2017ZZX227); Yunnan University Data-Driven Software Engineering Provincial Science and Technology Innovation Team Project (2017HC012); Alibaba Young Scholars Support Program

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    Abstract:

    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.

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朱锐,李彤,莫启,何臻力,于倩,王一荃.数据驱动的双层次软件过程挖掘方法.软件学报,2018,29(11):3455-3483

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History
  • Received:January 06,2017
  • Revised:April 14,2017
  • Online: June 08,2018
Links:Institute of Software, CASChina Computer FederationCASNSFC
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