A Two-Layered Framework for the Discovery of Software Behavior: A Case Study

Cong LIU; Jianpeng ZHANG; Guangming LI; Shangce GAO; Qingtian ZENG

doi:10.1587/transinf.2017EDP7027

IEICE TRANSACTIONS on Information

A Two-Layered Framework for the Discovery of Software Behavior: A Case Study

Cong LIU, Jianpeng ZHANG, Guangming LI, Shangce GAO, Qingtian ZENG

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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 well-known 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.

Publication: IEICE TRANSACTIONS on Information Vol.E101-D No.8 pp.2005-2014

Publication Date: 2018/08/01

Publicized: 2017/08/23

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2017EDP7027

Type of Manuscript: PAPER

Category: Software Engineering

Authors

Cong LIU
  Shandong University of Science and Technology
Jianpeng ZHANG
  National Digital Switching System Engineering Technological Research and Development Center
Guangming LI
  National University of Defense Technology
Shangce GAO
  University of Toyama
Qingtian ZENG
  Shandong University of Science and Technology

Keyword

software behavior discovery, user behavior, plug-in calling behavior, process mining

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Cong LIU, Jianpeng ZHANG, Guangming LI, Shangce GAO, Qingtian ZENG, "A Two-Layered Framework for the Discovery of Software Behavior: A Case Study" in IEICE TRANSACTIONS on Information, vol. E101-D, no. 8, pp. 2005-2014, August 2018, doi: 10.1587/transinf.2017EDP7027.
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 well-known 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7027/_p

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@ARTICLE{e101-d_8_2005,
author={Cong LIU, Jianpeng ZHANG, Guangming LI, Shangce GAO, Qingtian ZENG, },
journal={IEICE TRANSACTIONS on Information},
title={A Two-Layered Framework for the Discovery of Software Behavior: A Case Study},
year={2018},
volume={E101-D},
number={8},
pages={2005-2014},
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 well-known 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.},
keywords={},
doi={10.1587/transinf.2017EDP7027},
ISSN={1745-1361},
month={August},}

Copy

TY - JOUR
TI - A Two-Layered Framework for the Discovery of Software Behavior: A Case Study
T2 - IEICE TRANSACTIONS on Information
SP - 2005
EP - 2014
AU - Cong LIU
AU - Jianpeng ZHANG
AU - Guangming LI
AU - Shangce GAO
AU - Qingtian ZENG
PY - 2018
DO - 10.1587/transinf.2017EDP7027
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2018
AB - 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 well-known 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.
ER -

IEICE TRANSACTIONS on Information