Technical Debt indication in PLC Code for automated Production Systems: Introducing a Domain Specific Static Code Analysis Tool

Bougouffa, Safa; Dong, Quang Huan; Diehm, Sebastian; Gemein, Fabian; Vogel‐Heuser, Birgit

doi:10.1016/j.ifacol.2018.06.239

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…hardware, software, electrical/electronics) following the V-Model, and (2) risk trace matrix and corresponding internal guidelines following GAMP 5. While TD in the classical software engineering domain can be identified with code analysis, the work on TD in the aPS domain is still limited [6]. Recent work on TD identification in the aPS domain still focuses on software discipline [7]. Thus, a systematic method is lacking to identify TD in other fields in the aPS domain.…”

Section: Introductionmentioning

confidence: 99%

Including validation of process control systems’ engineering into the technical debt classification

Dong,

Vogel-Heuser,

Neumann

2024

Forsch Ingenieurwes

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The Technical Debt (TD) concept depicts a situation in which a technical compromise is selected despite a better solution available. The associated risks to TD may be critical if TD is overlooked or not fixed properly. For instance, the product might not be used safely (in the healthcare supplies industry) or be contaminated (in the food & beverage sector) due to a design fault not adequately addressed. This work extends the TD classification for process automation systems that need to undergo a validation process according to Good Automated Manufacturing Practice (GAMP), which is required for pharmaceutical production or food processing, for example. The approach analysed industrial engineering documents to identify such TD types and sub-types. Three selected TD use cases confirmed by industry experts are reported. The presented meta-analysis approach on engineering documents can be employed as a TD identification method for such process control systems. The TD classification for process automation was thereby enlarged for an especially critical type of machine that may harm the health of many humans.

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Section: Introductionmentioning

confidence: 99%