Bad Smells in Industrial Automation: Sniffing out Feature Envy

Rick Rabiser; Lisa Sonnleithner; Alois Zoitl

doi:10.1109/SEAA56994.2022.00061

Bad Smells in Industrial Automation: Sniffing out Feature Envy

LIT Cyber-Physical Systems Lab

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

Abstract

Bad Smells are sub-optimal software structures or patterns. They can obstruct the understandability of a software system and cause maintenance issues. As a result, it is critical to avoid Bad Smells. While the subject is well-researched in software engineering, it remains an unresolved issue in industrial automation, e.g., when developing control software in the context of a Cyber-Physical Production System (CPPS). In this short paper, we present possible detection methods for Feature Envy, a smell that indicates bad modularization of a software system. We explain how these methods can be applied to analyze control software developed in IEC 61499. We present first results as well as next steps.

Original language	English
Title of host publication	Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022
Editors	Gustavo M. Callico, Regina Hebig, Andreas Wortmann
Place of Publication	New York City, USA
Publisher	IEEE
Pages	346-349
Number of pages	4
ISBN (Electronic)	9781665461528
ISBN (Print)	978-1-6654-6152-8
DOIs	https://doi.org/10.1109/SEAA56994.2022.00061
Publication status	Published - Aug 2022

Publication series

Name	Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Fields of science

202017 Embedded systems
102022 Software development
102025 Distributed systems
102029 Practical computer science
202003 Automation
202041 Computer engineering
102 Computer Sciences

JKU Focus areas

Digital Transformation

Access to Document

10.1109/SEAA56994.2022.00061

Cite this

Rabiser, R., Sonnleithner, L., & Zoitl, A. (2022). Bad Smells in Industrial Automation: Sniffing out Feature Envy. In G. M. Callico, R. Hebig, & A. Wortmann (Eds.), Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022 (pp. 346-349). (Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022). IEEE. https://doi.org/10.1109/SEAA56994.2022.00061

Rabiser, Rick ; Sonnleithner, Lisa ; Zoitl, Alois. / Bad Smells in Industrial Automation: Sniffing out Feature Envy. Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022. editor / Gustavo M. Callico ; Regina Hebig ; Andreas Wortmann. New York City, USA : IEEE, 2022. pp. 346-349 (Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022).

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title = "Bad Smells in Industrial Automation: Sniffing out Feature Envy",

abstract = "Bad Smells are sub-optimal software structures or patterns. They can obstruct the understandability of a software system and cause maintenance issues. As a result, it is critical to avoid Bad Smells. While the subject is well-researched in software engineering, it remains an unresolved issue in industrial automation, e.g., when developing control software in the context of a Cyber-Physical Production System (CPPS). In this short paper, we present possible detection methods for Feature Envy, a smell that indicates bad modularization of a software system. We explain how these methods can be applied to analyze control software developed in IEC 61499. We present first results as well as next steps.",

keywords = "IEC 61499, measure, metric, bad smell, CPS, Feature Envy, code smell, distributed control system, anti-pattern, design smell, CPPS",

author = "Rick Rabiser and Lisa Sonnleithner and Alois Zoitl",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.",

year = "2022",

month = aug,

doi = "10.1109/SEAA56994.2022.00061",

language = "English",

isbn = "978-1-6654-6152-8",

series = "Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022",

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Rabiser, R , Sonnleithner, L & Zoitl, A 2022, Bad Smells in Industrial Automation: Sniffing out Feature Envy. in GM Callico, R Hebig & A Wortmann (eds), Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022. Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022, IEEE, New York City, USA, pp. 346-349. https://doi.org/10.1109/SEAA56994.2022.00061

Bad Smells in Industrial Automation: Sniffing out Feature Envy. / Rabiser, Rick ; Sonnleithner, Lisa ; Zoitl, Alois.
Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022. ed. / Gustavo M. Callico; Regina Hebig; Andreas Wortmann. New York City, USA: IEEE, 2022. p. 346-349 (Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022).

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

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T1 - Bad Smells in Industrial Automation: Sniffing out Feature Envy

AU - Rabiser, Rick

AU - Sonnleithner, Lisa

AU - Zoitl, Alois

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N2 - Bad Smells are sub-optimal software structures or patterns. They can obstruct the understandability of a software system and cause maintenance issues. As a result, it is critical to avoid Bad Smells. While the subject is well-researched in software engineering, it remains an unresolved issue in industrial automation, e.g., when developing control software in the context of a Cyber-Physical Production System (CPPS). In this short paper, we present possible detection methods for Feature Envy, a smell that indicates bad modularization of a software system. We explain how these methods can be applied to analyze control software developed in IEC 61499. We present first results as well as next steps.

AB - Bad Smells are sub-optimal software structures or patterns. They can obstruct the understandability of a software system and cause maintenance issues. As a result, it is critical to avoid Bad Smells. While the subject is well-researched in software engineering, it remains an unresolved issue in industrial automation, e.g., when developing control software in the context of a Cyber-Physical Production System (CPPS). In this short paper, we present possible detection methods for Feature Envy, a smell that indicates bad modularization of a software system. We explain how these methods can be applied to analyze control software developed in IEC 61499. We present first results as well as next steps.

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KW - measure

KW - metric

KW - bad smell

KW - CPS

KW - Feature Envy

KW - code smell

KW - distributed control system

KW - anti-pattern

KW - design smell

KW - CPPS

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BT - Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022

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Rabiser R , Sonnleithner L , Zoitl A. Bad Smells in Industrial Automation: Sniffing out Feature Envy. In Callico GM, Hebig R, Wortmann A, editors, Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022. New York City, USA: IEEE. 2022. p. 346-349. (Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022). doi: 10.1109/SEAA56994.2022.00061

Bad Smells in Industrial Automation: Sniffing out Feature Envy

Abstract

Publication series

UN SDGs

Fields of science

JKU Focus areas

Access to Document

Other files and links

Christian Doppler Laboratory for Mastering Variability in Software-intensive Cyber-physical Production Systems (CDL VaSiCS)

Cite this

Bad Smells in Industrial Automation: Sniffing out Feature Envy

Abstract

Publication series

UN SDGs

Fields of science

JKU Focus areas

Access to Document

Other files and links

Projects

Christian Doppler Laboratory for Mastering Variability in Software-intensive Cyber-physical Production Systems (CDL VaSiCS)

Cite this