Metrics for software quality in automated production systems as an indicator for technical debt

Lorena Capitán; Birgit Vogel-Heuser

doi:10.1109/COASE.2017.8256186

Metrics for software quality in automated production systems as an indicator for technical debt

Lorena Capitán, Birgit Vogel-Heuser

Chair of Automation and Information Systems

Technical University of Munich

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

Abstract

Technical debt (TD) is a critical issue in the software (SW) development industry that increases development costs over time and needs adaptation for production automation SW. This paper provides code metrics compliant with IEC 61131-3 programming languages for measuring the quality level of the system enabling the SW developer to evaluate TD compared to other existing applications. We introduce metrics, i.e. Halstead, McCabe, Kafura or MSUI to evaluate their applicability measuring code size, complexity and modularity in APS domain using two different expansion states of an application example.

Original language	English
Title of host publication	2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
Publisher	IEEE Computer Society
Pages	709-716
Number of pages	8
ISBN (Electronic)	9781509067800
DOIs	https://doi.org/10.1109/COASE.2017.8256186
State	Published - 1 Jul 2017
Event	13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China Duration: 20 Aug 2017 → 23 Aug 2017

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2017-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	13th IEEE Conference on Automation Science and Engineering, CASE 2017
Country/Territory	China
City	Xi'an
Period	20/08/17 → 23/08/17

Keywords

IEC 61131-3
PLC
automation
quality
quality model
software engineering
source code
technical debt

Access to Document

10.1109/COASE.2017.8256186

Cite this

Capitán, L., & Vogel-Heuser, B. (2017). Metrics for software quality in automated production systems as an indicator for technical debt. In 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017 (pp. 709-716). (IEEE International Conference on Automation Science and Engineering; Vol. 2017-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2017.8256186

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title = "Metrics for software quality in automated production systems as an indicator for technical debt",

abstract = "Technical debt (TD) is a critical issue in the software (SW) development industry that increases development costs over time and needs adaptation for production automation SW. This paper provides code metrics compliant with IEC 61131-3 programming languages for measuring the quality level of the system enabling the SW developer to evaluate TD compared to other existing applications. We introduce metrics, i.e. Halstead, McCabe, Kafura or MSUI to evaluate their applicability measuring code size, complexity and modularity in APS domain using two different expansion states of an application example.",

keywords = "IEC 61131-3, PLC, automation, quality, quality model, software engineering, source code, technical debt",

author = "Lorena Capit{\'a}n and Birgit Vogel-Heuser",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 13th IEEE Conference on Automation Science and Engineering, CASE 2017 ; Conference date: 20-08-2017 Through 23-08-2017",

year = "2017",

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doi = "10.1109/COASE.2017.8256186",

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Capitán, L & Vogel-Heuser, B 2017, Metrics for software quality in automated production systems as an indicator for technical debt. in 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017. IEEE International Conference on Automation Science and Engineering, vol. 2017-August, IEEE Computer Society, pp. 709-716, 13th IEEE Conference on Automation Science and Engineering, CASE 2017, Xi'an, China, 20/08/17. https://doi.org/10.1109/COASE.2017.8256186

Metrics for software quality in automated production systems as an indicator for technical debt. / Capitán, Lorena; Vogel-Heuser, Birgit.
2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017. IEEE Computer Society, 2017. p. 709-716 (IEEE International Conference on Automation Science and Engineering; Vol. 2017-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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KW - quality model

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Metrics for software quality in automated production systems as an indicator for technical debt

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