Similarity Analysis of Control Software Using Graph Mining

Fahimipirehgalin, Mina; Fischer, Juliane; Bougouffa, Safa; Vogel‐Heuser, Birgit

doi:10.1109/indin41052.2019.8972335

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…The analyzed system is implemented using ST and C++. Fahimipirehgalin et al [46] presented a call-graph based approach that allows detect similarities between two software structures, e.g., POUs, which support the detection of clones with a pre-selection of potentially similar artifacts. In the field of clone detection on LD Nedvěd et al [47] presents a tool that can detect differences between LD implementation based on their PLCOpenXML representation.…”

Section: Intra Variant Clone Detection (Classic Clone Detection)mentioning

confidence: 99%

Custom-Tailored Clone Detection for IEC 61131-3 Programming Languages

Rosiak¹,

Schlie²,

Linsbauer³

et al. 2021

Preprint

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Automated production systems (aPS) are highly customized systems that consist of hardware and software. Such aPS are controlled by a programmable logic controller (PLC), often in accordance with the IEC 61131-3 standard that divides system implementation into so-called program organization units (POUs) as the smallest software unit and is comprised of multiple textual (Structured Text (ST)) and graphical (Function Block Diagram (FBD), Ladder Diagram (LD), and Sequential Function Chart(SFC)) programming languages that can be arbitrarily nested.A common practice during the development of such systems is reusing implementation artifacts by copying, pasting, and then modifying code. This approach is referred to as code cloning. It is used on a fine-granular level where a POU is cloned within a system variant. It is also applied on the coarse-granular system level, where the entire system is cloned and adapted to create a system variant, for example for another customer. This ad hoc practice for the development of variants is commonly referred to as clone-and-own. It allows the fast development of variants to meet varying customer requirements or altered regulatory guidelines. However, clone-and-own is a non-sustainable approach and does not scale with an increasing number of variants. It has a detrimental effect on the overall quality of a software system, such as the propagation of bugs to other variants, which harms maintenance.In order to support the effective development and maintenance of such systems, a detailed code clone analysis is required. On the one hand, an analysis of code clones within a variant (i.e., clone detection in the classical sense) supports experts in refactoring respective code into library components. On the other hand, an analysis of commonalities and differences between cloned variants (i.e., variability analysis) supports the maintenance and further reuse and facilitates the migration of variants into a software productline (SPL).In this paper, we present an approach for the automated detection of code clones within variants (intra variant clone detection) and between variants (inter variant clone detection) of IEC61131-3 control software with arbitrary nesting of both textual and graphical languages. We provide an implementation of the approach in the variability analysis toolkit (VAT) as a freely available prototype for the analysis of IEC 61131-3 programs. For the evaluation, we developed a meta-model-based mutation framework to measure our approach's precision and recall. Besides, we evaluated our approach using the Pick and Place Unit (PPU) and Extended Pick and Place Unit (xPPU) scenarios. Results show the usefulness of intra and inter clone detection in the domain of automated production systems.

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Section: Intra Variant Clone Detection (Classic Clone Detection)mentioning

confidence: 99%