Due to emerging data-driven approaches in factory automation in the course of Industry 4.0, automated Production Systems must incorporate additional algorithms for data collection and processing tasks. However, strict real-time requirements, resource constraint devices, such as Programmable Logic Controllers or low-power edge devices, and network bandwidth limitations pose a challenge to selecting suitable algorithms for specific edge devices and vice-versa, also known as software-hardware co-design. Measuring the execution time of an algorithm or code snippet is therefore a crucial part of algorithm and hardware assessment and is incorporated in numerous benchmarks. However, this is not trivial since most existing time measurement methods are designed with specific devices in mind with limited portability to different hardware platforms. Thus this paper provides an overview of the properties of selected execution time measurement methods to support their feasible deployment in edge computing, including legacy systems. A time measurement code snippet for Beckhoff Programmable Logic Controllers and recommendations for implementing software-based timing functions for heterogeneous devices help shorten development times. Besides, comparing execution time measurement methods highlights the challenges of creating a generic cross-platform benchmark in future research.

Cyber-Physical Production Systems (CPPSs) are envisioned as next-generation adaptive production systems combining modern production techniques with the latest information technology. A CPPS creates a complex environment between different domains (mechanical, electrical, software engineering), requiring multidisciplinary solutions to tackle growing complexity issues and reduce (maintenance) effort. Software plays an increasingly important role in assuring an effective and efficient operation of CPPSs. However, software engineering methods applied for CPPSs seem to lag behind modern software engineering methods, where tremendous progress has been made in the last years. We initiated the Software Engineering in Cyber-Physical Production Systems Workshop (SECPPS-WS) to analyze and overcome this gap. After two instances with mostly academic participants, we conducted a full-day workshop with nine industry representatives from eight companies that develop and maintain CPPSs. Each industry representative presented their current work and challenges. We collected these challenges and condensed a categorized list of challenges backed by industry statements and literature. This paper presents the resulting list and pointers to (partial) solutions to offer guidance for academia and identify promising research opportunities in this area.

automated Production Systems (aPS) are highly complex mechatronic systems, partly operated for several decades. Particularly in the face of ever shorter innovation cycles in the context of Industry 4.0 and intensified global competition, it is essential for aPS manufacturers to develop high-quality, maintainable, and flexibly adaptable systems. Increasing the efficiency of aPS requires the implementation of sophisticated drive concepts that meet a variety of company-and process-specific requirements. However, the evaluation of design decisions of motion concepts in aPS and the challenges faced by industrial practitioners have so far barely been addressed in research. Therefore, this paper presents the results of a field study with six companies in the aPS manufacturing sector in Germany. The results shed light on the current state of industrial practice in motion systems, the challenges that are already solved well, and the issues that still show potential for improvement and, thus, serve as an outlook for future work in motion technology.

The amount of software and its development effort in machine and plant manufacturing is continuously increasing to currently up to 35-50% of the development personnel. However, achieving mature automation software that is adaptable to changed requirements during runtime for lifetimes of several decades still poses major challenges for machine and plant manufacturing companies. Previous industrial case studies show that companies deal with these challenges with different success. However, maturity levels are still missing to categorize and compare companies' automation software and development processes and, thus, enable a cross-company benchmark. Available approaches from computer science to categorize software maturity often do not consider the characteristics of automation software or only focus on individual aspects of maturity. Thus, this paper introduces the results of a large-scale questionnaire study with 61 German machine and plant manufacturing companies to enlarge an established maturity classification with quantitative and qualitative results on success factors in the design of automation software.

automated Production Systems (aPS) are highly complex, mechatronic systems that usually have to operate reliably for many decades. Standardization and reuse of control software modules is a core prerequisite to achieve the required system quality in increasingly shorter development cycles. However, industrial case studies in aPS show that many aPS companies still struggle with strategically reusing software. This paper proposes a metric-based approach to objectively measure the m aturity of i ndustrial IEC 61131-based co ntrol s oftwar e in aPS (MICOSE4aPS) to identify potential weaknesses and quality issues hampering systematic reuse. Module developers in the machine and plant manufacturing industry can directly benefit as the metric calculation is integrated into the software engineering workflow. An in-depth industrial evaluation in a top-ranked machine manufacturing company in food packaging and an expert evaluation with different companies confirmed the benefit of efficiently managing the quality of control software.

Cyber-Physical Production Systems (CPPS) are long-living and variant-rich systems. Challenges and trends in the context of Industry 4.0 such as a high degree of customization, evolution, and different disciplines involved, e.g., mechanics, electrics/electronics and software, cause a high amount of variability. Mastering the variability of functional control software, e.g., different control variants of an actuator type, is itself a challenge in the development and reuse of CPPS software. This task becomes even more complex when considering the variability of human-machine interface (HMI) software and extra-functional software such as operating modes, diagnosis or fault handling. Moreover, the interdependencies between functional, extra-functional and HMI software pose an additional challenge for variability management and the planned reuse of these software parts. This paper illustrates the challenges in documenting the dependencies of these software parts including their variability using family models. Additionally, the current state-of-practice in industry, which was derived in questionnaires and interview studies, is shown and compared to the potential of increasing the software's reusability and thus its flexibility in the context of Industry 4.0 by concepts using the object-oriented extension of IEC 61131–3.

automated Production Systems (aPS) represent a special type of mechatronic system characterized by a high complexity in hardware and software, lifetimes of up to several decades, and strong coupling of the involved disciplines, i.e., mechanics, electrics/electronics, and control software. Questionnaire studies have proven to be a valuable means of identifying obstacles in industrial software development that hinder the implementation of emerging technologies in the context of Industry 4.0 and, thus, the realization of Cyber-Physical Production Systems. However, the heterogeneity of different industrial sectors in terms of, e.g., the type of production processes or legal specifications, makes it difficult to compare the maturity of control software development across companies, even within the same industry. A software maturity benchmark for companies operating in similar industries is, however, a prerequisite for assessing a company’s strengths and weaknesses and thereby applying tailored solutions to improve the software and, thus, the competitiveness. This paper describes the results of a questionnaire study in two international companies operating in the same industrial sector. Specifically, location-, company-, and industry-specific maturity characteristics are analyzed and discussed, and it is concluded that the selected questionnaire is capable of identifying deviations and similarities in software maturity, even in an international environment, given that the companies are prefiltered based on the covered supply chain step. Hence, assuming a sufficiently large number of participants per industry, it enables a questionnaire-based industry comparison in future work, which was not possible until now.

Automated Production Systems (aPS) are highly complex, long-living mechatronic systems, which are usually programmed according to the standard IEC 61131-3. Control soft-ware development in aPS has to cope with challenges that sharply differ from classical high-level language programming, such as the assurance of maintainability for up to five decades or hard real-time requirements, e.g., to prevent damage to humans in case of a fault. In computer science, object-oriented programming is an established means to increase software reusability and maintainability. However, this approach is seldom used in IEC 61131-3-compliant control software, although the object-oriented extension of the standard (OO-IEC) has been available since 2013. Companies using OO-IEC generally benefit from a drastic reduction in the complexity of their control software. However, certain constellations of OO-IEC elements could lead to runtime problems, which is a crucial problem regarding the hard real-time requirements of aPS to guarantee safety. Therefore, this paper introduces two metrics to detect runtime-critical control software parts and optimization potentials without executing the code or performing time measurements. Using an industrial example, the metrics are evaluated with PLC experts of two companies, who confirmed the reliability and the advantages of the metrics.

Automated production systems (aPS) are often in operation for several decades. During their lifetime they undergo various changes and modifications due to the varying customer requirement, market or technology environment. In order to keep the system development and maintenance process under control, it is extremely important to be able to assess the effect of a change on the system and its cost. This paper introduce an approach for cost estimation of change occurring to the aPS using process, product and resources viewpoint on the system together with the capability of the resources. The estimation of the cost is supported by reusing the previous changes cost and effort that are collected in a knowledge base. The approach is evaluated using a lab size industrial demonstrator.

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.

Successful innovation processes require communication between different disciplines, such as mechanical and electrical engineering or software development. However, such communication is hampered by differences in individual mental models. This paper presents two studies that investigate mental models of engineering students. The studies apply SA/RT and card sorting to analyze mental models and were conducted at the beginning of their academic education to understand influences on discipline-specific mental models that are observed in practice. The studies indicate that mental models of engineering students are typically based on structural properties of the machines. A second study identified nine dimensions that classify mental models of students. Future work that facilitates the analysis of mental models on a larger scale is proposed. The aim of this work is to understand these differences and inform educational mechanisms that teach mental model flexibility and perspective change.

Automated production systems become more and more complex. This makes it increasingly difficult to keep track of performed changes and already executed test cases. This endangers the systems quality as the risk of missing important test cases while planning the test execution is high, especially for testers with little experience. To face this challenge, testers should be supported by an automatic test prioritisation based on metrics in selecting the right test cases for the test execution. In industry, many different test prioritisation criteria and strategies are used for this purpose. In an industrial interview, experts discussed and ranked prioritisation criteria that are currently used within the respective companies. As a result, this paper presents the cactus prioritisation model, which graphically resembles the industrial ranking and weighting of the criteria. Based on the prioritisation cactus and its criteria, a simple prioritisation metric is introduced to determine the utility of each test case regarding the system under test. The test cases are prioritised according to their descending utility. Furthermore, approaches and metrics to realise the different individual prioritisation criteria are proposed.