Introduction and Evaluation of Complexity Metrics for Network-based, Graphical IEC 61131-3 Programming Languages

被引:0
|
作者
Wilch, Jan [1 ]
Fischer, Juliane [1 ]
Neumann, Eva-Maria [1 ]
Diehm, Sebastian [2 ]
Schwarz, Michael [2 ]
Lah, Eric [3 ]
Wander, Matthias [3 ]
Vogel-Heuser, Birgit [1 ]
机构
[1] Tech Univ Munich, Inst Automat & Informat Syst, Garching, Germany
[2] Schneider Elect Automat GmbH, Marktheidenfeld, Germany
[3] SOMIC Packaging machines GmbH & Co KG, Amerang, Germany
关键词
automated Production Systems; software metrics; IEC; 61131-3; Function Block Diagram; graphical programming languages; automatic code analysis; SOFTWARE;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The development of automated Production Systems (aPS) is an interdisciplinary process, where an increasing part of the system's functionality is realized in the respective control software. Such software projects commonly utilize programming languages standardized in IEC 61131-3. To measure, improve, and maintain source code while also promoting trust in its capabilities, an objective assessment of its characteristics is necessary. Software metrics are a means for such an evaluation. While there is an abundance of metrics available from the classical software engineering domain, these metrics focus on textual programming languages. IEC 61131-3, however, defines graphical languages, which are not targeted by renowned concepts in computer science. Besides, former research demonstrates that software engineering metrics for textual languages need adaption to be applicable in the aPS domain. Thus, this paper introduces a metrics suite consisting of adapted and newly developed measures, which focus on the graphical IEC 61131-3 language Function Block Diagram. The results are prototypically implemented in one of the leading integrated development environments for IEC 61131-3 and then evaluated regarding their understandability and applicability by practitioners at a German aPS manufacturer.
引用
收藏
页码:417 / 423
页数:7
相关论文
共 46 条
  • [41] Performance and Computational Complexity Evaluation for Neural Network-Based Short-Reach Optical Links
    Xu, Zhaopeng
    Sun, Chuanbowen
    Manton, Jonathan H.
    Shieh, William
    2020 CONFERENCE ON LASERS AND ELECTRO-OPTICS PACIFIC RIM (CLEO-PR), 2020,
  • [43] Neural network-based design and evaluation of performance metrics using adaptive line enhancer with adaptive algorithms for auscultation analysis
    Rajkumar, S.
    Sathesh, K.
    Goyal, Neeraj Kumar
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (18): : 15131 - 15153
  • [44] Neural network-based design and evaluation of performance metrics using adaptive line enhancer with adaptive algorithms for auscultation analysis
    S. Rajkumar
    K. Sathesh
    Neeraj Kumar Goyal
    Neural Computing and Applications, 2020, 32 : 15131 - 15153
  • [45] Evaluation of motion artifacts in brain magnetic resonance images using convolutional neural network-based prediction of full-reference image quality assessment metrics
    Sagawa, Hajime
    Itagaki, Koji
    Matsushita, Tatsuhiko
    Miyati, Tosiaki
    JOURNAL OF MEDICAL IMAGING, 2022, 9 (01)
  • [46] Graph neural network based visual programming recommender system for 3D CAD shape evaluation
    Hasebe, Tatsuya
    Katayama, Erika
    JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING, 2024, 18 (05):