The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
소프트웨어를 실행하는 동안 엄청난 양의 데이터가 기록될 수 있습니다. 실행 데이터를 활용하면 실제 소프트웨어 실행을 설명하는 동작 모델을 발견할 수 있습니다. 잘 알려진 오픈 소스 프로세스 마이닝 툴킷인 ProM은 다양한 프로세스 마이닝 기술을 통합하고 광범위한 영역에서 다양한 애플리케이션을 활용합니다. 사용자 경험과 소프트웨어 성능 관점 모두에서 더 나은 ProM 소프트웨어를 개발하는 방법은 매우 중요합니다. 이 목표를 달성하려면 ProM의 사용법과 사용자 작업에 어떻게 응답하는지에 대한 유용한 통찰력을 제공할 수 있는 ProM의 실제 실행 동작을 조사해야 합니다. 본 논문은 이러한 문제를 해결하기 위한 효과적인 접근 방식을 제안하는 것을 목표로 한다. 이를 위해 먼저 기존 ProM 프레임워크를 계측하여 아키텍처를 변경하지 않고 실행 로그를 캡처합니다. 그런 다음 사용자 상호 작용 동작과 플러그인 호출 동작을 별도로 특성화하여 정확한 ProM 동작 검색을 지원하기 위해 XNUMX계층 프레임워크가 도입되었습니다. 다음으로 사용자 상호작용 행위 모델과 플러그인 호출 행위 모델을 획득하기 위한 세부 탐색 기법을 제안한다. 제안된 모든 접근 방식이 구현되었습니다.
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
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
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
부
@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},}
부
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 -