The Statechart Workbench: Enabling Scalable Software Event Log Analysis using Process Mining

被引:0
|
作者
Leemans, Maikel [1 ]
van der Aalst, Wil M. P. [1 ]
van den Brand, Mark G. J. [1 ]
机构
[1] Eindhoven Univ Technol, Eindhoven, Netherlands
关键词
Reverse Engineering; Process Mining; Behavior Exploration; Performance Analysis; Usage Analysis; Deviation Analysis; Program Analysis; Model-driven Analysis;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
To understand and maintain the behavior of a (legacy) software system, one can observe and study the system's behavior by analyzing event data. For model-driven reverse engineering and analysis of system behavior, operation and usage based on software event data, we need a combination of advanced algorithms and techniques. In this paper, we present the Statechart Workbench: a novel software behavior exploration tool. Our tool provides a rich and mature integration of advanced (academic) techniques for the analysis of behavior, performance (timings), frequency (usage), conformance and reliability in the context of various formal models. The accompanied Eclipse plugin allows the user to interactively link all the results from the Statechart Workbench back to the source code of the system and enables users to get started right away with their own software. The work can be positioned in-between reverse engineering and process mining. Implementations, documentation, and a screen-cast (https://youtu.be/xR4XfU3E5mk) of the proposed approach are available, and a user study demonstrates the novelty and usefulness of the tool.
引用
收藏
页码:502 / 506
页数:5
相关论文
共 50 条
  • [1] Using process mining for Git log analysis of projects in a software development course
    Macak, Martin
    Kruzelova, Daniela
    Chren, Stanislav
    Buhnova, Barbora
    EDUCATION AND INFORMATION TECHNOLOGIES, 2021, 26 (05) : 5939 - 5969
  • [2] Using process mining for Git log analysis of projects in a software development course
    Martin Macak
    Daniela Kruzelova
    Stanislav Chren
    Barbora Buhnova
    Education and Information Technologies, 2021, 26 : 5939 - 5969
  • [3] Event Log Preprocessing for Process Mining: A Review
    Marin-Castro, Heidy M.
    Tello-Leal, Edgar
    APPLIED SCIENCES-BASEL, 2021, 11 (22):
  • [4] The Development of the Process Mining Event Log Generator (PMELG) Tool
    Hawkins, Steven R.
    Pickerd, Jeffrey
    Summers, Scott L.
    Wood, David A.
    ACCOUNTING HORIZONS, 2023, 37 (04) : 85 - 95
  • [5] Auditor Choices during Event Log Building for Process Mining
    Jans, Mieke
    JOURNAL OF EMERGING TECHNOLOGIES IN ACCOUNTING, 2019, 16 (02) : 59 - 67
  • [6] Building a valuable event log for process mining: an experimental exploration of a guided process
    Jans, Mieke
    Soffer, Pnina
    Jouck, Toon
    ENTERPRISE INFORMATION SYSTEMS, 2019, 13 (05) : 601 - 630
  • [7] Log Design for Storing Seismic Event Characteristics Using Process, Text, and Opinion Mining Techniques
    Diaz-Rodriguez, Oswaldo E.
    Perez, Maria
    2018 FIFTH INTERNATIONAL CONFERENCE ON EDEMOCRACY & EGOVERNMENT (ICEDEG), 2018, : 281 - 285
  • [8] Semantics-based event log aggregation for process mining and analytics
    Amit V. Deokar
    Jie Tao
    Information Systems Frontiers, 2015, 17 : 1209 - 1226
  • [9] A Framework for Event Log Generation and Knowledge Representation for Process Mining in Healthcare
    Gatta, Roberto
    Vallati, Mauro
    Lenkowicz, Jacopo
    Casa, Calogero
    Cellini, Francesco
    Damiani, Andrea
    Valentini, Vincenzo
    2018 IEEE 30TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI), 2018, : 647 - 654
  • [10] Enhancement in Process Mining Model by Repairing Noisy Behavior in Event Log
    Shahzadi, Shabnam
    Emam, Walid
    Shahzad, Usman
    Iftikhar, Soofia
    Ahmad, Ishfaq
    Sharma, Gaurav
    IEEE ACCESS, 2024, 12 : 82938 - 82948