Dietmar Winkler

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Verified

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• Dipl.-Ing. Dr.
• Senior Researcher at TU Wien

The integration of Large Language Models (LLMs), such as ChatGPT and GitHub Copilot, into software engineering workflows has shown potential to enhance productivity, particularly in software testing. This paper investigates whether LLM support improves defect detection effectiveness during unit testing. Building on prior studies comparing manual an...

The convergence of Information Technology (IT) and Operational Technology (OT) in Industrial Control System (ICS) comes with severe cybersecurity challenges that increasingly pose threats to critical infrastructures. In this challenging environment, numerous standards and data sources exist that aim to facilitate the exchange of information and gui...

Context The readability of source code is key for understanding and maintaining software systems and tests. Although several studies investigate the readability of source code, there is limited research specifically on the readability of test code and related influence factors. Objective In this paper, we aim at investigating the factors that infl...

The increasing interconnection of Information Technology and Operational Technology in Industry 4.0 creates new challenges and requires new approaches to ensure that production processes are executed safely and securely. Production system safety and security have therefore become critical aspects as security incidents can lead to serious problems s...

Purpose Agile Production Systems Engineering (PSE) is characterised by parallel and iterative engineering of several disciplines. This multi-view engineering requires capabilities for tracing changes to support configuration management of PSE assets. Yet, traditional model transformation approaches in PSE do not preserve local views and hierarchies...

In agile Production Systems Engineering (PSE), multi-disciplinary teams work concurrently on various PSE artifacts in an iterative process that can be supported by common concept and Product-Process-Resource (PPR) modeling. However, keeping track of the interactions and effects of changes across engineering disciplines and their implications for ri...

Agile Production Systems Engineering (PSE) is a complex, collaborative, and knowledge-intensive process. PSE requires expert knowledge from various disciplines and the integration of discipline-specific perspectives and workflows. This integration is a major challenge due to fragmented views on the production system with a difficult a priori coordi...

The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, graph-pattern based query languages...

In Production Systems Engineering (PSE), domain experts aim at reusing partial system designs implemented as Industry 4.0 assets and software. However, the knowledge on assets is often scattered across engineering artifacts from multiple disciplines and domain experts, making it difficult to find reusable assets and map them to requirements. In thi...

Together with many success stories, promises such as the increase in production speed and the improvement in stakeholders' collaboration have contributed to making agile a transformation in the software industry in which many companies want to take part. However, driven either by a natural and expected evolution or by contextual factors that challe...

Industry 4.0 envisions adaptive production systems,i.e.,Cyber-Physical Production Systems (CPPSs), to manufacture products from a product line. Product-Process-Resource modeling represents the essential aspects of a CPPS. However,due to discipline-specific models, e.g., mechanical, electrical, and automation models, it is often unclear how to integ...

Together with many success stories, promises such as the increase in production speed and the improvement in stakeholders' collaboration have contributed to making agile a transformation in the software industry in which many companies want to take part. However, driven either by a natural and expected evolution or by contextual factors that challe...

Background. Systems modeling in Production Systems Engineering (PSE) is complex: Multiple views from different disciplines have to be integrated, while semantic differences stemming from various descriptions must be bridged. Aim. This paper proposes the Multi-view Modeling Framework (MvMF) approach and architecture of a model transformation pipelin...

Background. In Production Systems Engineering (PSE) models, which describe plants, represent different views on several engineering disciplines (such as mechanical, electrical and software engineering) and may contain up to 10,000s of instance elements, such as concepts, attributes and relationships. Validating these models requires an integrated m...

This book constitutes the refereed proceedings of the 13th Software Quality Days Conference, SWQD 2021, which was planned to be held in Vienna, Austria, during January 19–21, 2021. Due to the COVID-19 pandemic, the conference was cancelled and will be merged with SWQD 2022. The Software Quality Days (SWQD) conference started in 2009 and has grown t...

The verification of Extended Entity Relationship (EER) diagrams and other conceptual models that capture the design of information systems is crucial to ensure reliable systems. To scale up verification processes to larger groups of experts, Human Computation techniques were used focusing primarily on closed tasks, which constrain the number and va...

Empirical software engineering (ESE) focuses on gathering evidence through measurements and experiments involving humans and software systems (software products, processes, and resources). While empirical studies often include considerable human effort for study planning, execution, and data analysis, human computation (HC) methods, such as crowdso...

This book constitutes the refereed proceedings of the 12th Software Quality Days Conference, SWQD 2020, held in Vienna, Austria, in January 2020. The Software Quality Days (SWQD) conference started in 2009 and has grown to the biggest conference on software quality in Europe with a strong community. The program of the SWQD conference is designed to...

Background. In Production Systems Engineering (PSE), the planning of production systems involves domain experts from various domains, such as mechanical, electrical and software engineering collaborating and modeling their specific views on the system. These models, describing entire plants, can reach a large size (up to several GBs) with complex r...

In the parallel engineering of large and long-running automation systems, such as Production Systems Engineering (PSE) projects, engineering teams with different backgrounds work in a so-called Round-Trip Engineering (RTE) process to iteratively enrich and refine their engineering artifacts, and need to exchange data efficiently to prevent the dive...

Context and background: In Production Systems Engineering (PSE), software and systems testing are success-critical along the production automation life cycle to identify defects early and efficiently. Although test automation concepts enable continuous integration and tests during engineering and maintenance, tool chains are often hardwired, less f...

Flexible manufacturing systems, as a vision of Industry 4.0, depend on the collaboration of domain experts coming from different engineering disciplines. These experts often depend on (interdisciplinary) results from previous engineering phases and require an explicit representation of knowledge on relationships between products and production syst...

Context and Background. Software testing and test automation are important activities in software development where frequent requirements changes and the fast delivery of software increments are supported by traditional and agile development processes. Test reports are often used as “proof of evidence” for executed software tests. However, the prac...

Context. In Production Systems Engineering (PSE), experts of different domains collaborate in loosely coupled engineering processes to collectively plan and develop an Automation System (AS). Due to limited collaboration capabilities of discipline-specific tools, engineering knowledge is often lost and needs to be recovered manually with considerab...

One of the most important issues in scenario-based software architecture evaluation is the development of scenarios for characterizing a desired set of quality attributes by holding meetings of stakeholders. As such meetings are costly to arrange, studying the effectiveness of such meetings is an important research question. In this paper, we repor...

The testing of automation applications has become a crucial pillar of every production systems engineering (PSE) project with the proliferation of cyber-physical systems (CPSs). In light of new attack vectors against CPSs, caused, inter alia, by increased connectivity, security aspects must be considered throughout the PSE process. In this context,...

Background: Replication is essential to build knowledge in empirical science. Experiment replications reported in the software engineering context present variabilities on their design elements, e.g., variables, materials. The understanding of these variabilities is required to plan experimental replications within a research program. However, the...

Context. Modern software engineering approaches that rely on continuous and automated testing, like Agile Software Engineering and the late DevOps movement, require integrated and fully functional testing tool chain environment, to efficiently identify defects in software artifacts. Such an environment includes the implementation of established tes...

Context: Requirements Engineering (RE) has established itself as a software engineering discipline over the past decades. While researchers have been investigating the RE discipline with a plethora of empirical studies, attempts to systematically derive an empirical theory in context of the RE discipline have just recently been started. However, su...

Production system operators need support for collecting and pre-processing data on production systems consisting of several system components, as foundation for optimization and defect detection. Traditional approaches based on hard-coded programming of such run-time data collection systems take time and effort, and require both domain and technolo...

Conceptual domain models, such as taxonomies, knowledge graphs or Extended Entity Relationship (EER) diagrams are core to all information systems. The task of verifying the correctness of these models is of high interest to the knowledge and software engineering communities and attracted the first solution approaches using human computation. Yet, s...

Software Inspection is an important approach to find defects in Software Engineering (SE) artifacts. While there has been extensive research on traditional software inspection with pen-and-paper materials, modern SE poses new environments, methods, and tools for the cooperation of software engineers. Technologies, such as Human Computation (HC), pr...

Requirements Engineering (RE) has established itself as a software engineering discipline during the past decades. While researchers have been investigating the RE discipline with a plethora of empirical studies, attempts to systematically derive an empirically-based theory in context of the RE discipline have just recently been started. However, s...

[Context] Models play an important role in Software and Systems Engineering processes. Reviews are well-established methods for model quality assurance that support early and efficient defect detection. However, traditional document-based review processes have limitations with respect to the number of experts, resources, and the document size that...

Anlagen im Industrie 4.0 Umfeld erfordern den Fachbereiche übergreifenden Austausch von Engineering-Daten. Der Einsatz heterogener Software-Werkzeuglandschaften bestimmt jedoch heute den Engineering-Prozess automatisierter Systeme und wird dies auch noch viele Jahre tun. In der Kombination von standardisierten Datenformaten wie AutomationML und gee...

Die Zusammenarbeit von Fachexperten in einem heterogenen Entwicklungsumfeld im Industrie 4.0 Kontext bringt neben dem verstärkten Bedarf an effizientem Datenaustausch auch Herausforderungen und neue Möglichkeiten an Fachbereiche übergreifenden Maßnahmen der Qualitätssicherung zur Verbesserung der Projekt-, Prozess- und Produktqualität mit sich. Die...

The application of software process models in industry includes traditional processes, agile processes, and process variants that aim at balancing traditional and agile with focus on specific industry needs. To investigate the characteristics of such hybrid software and system development approaches that combine agile and traditional approaches the...

The application of best-practice software inspection processes for early defect detection requires considerable human effort. Crowdsourcing approaches can support inspection activities (a) by distributing inspection effort among a group of human experts and (b) by increasing inspection control. Thus, the application of crowdsourcing techniques aims...

[Context] Software inspections are an efficient mean to improve quality. Learning Styles (LS) have been used to measure an individual’s characteristic to acquire and process information according to different dimensions. For requirements inspections, inspection teams with different LSs showed more effective at detecting defects. [Goal] The goal of...

[Context] Models play an important role in Software and Systems Engineering processes. Reviews are well-established methods for model quality assurance that support early and efficient defect detection. However, traditional document-based review processes have limitations with respect to the number of experts, resources, and the document size that...

Traditional Software Inspection is a well-established approach to identify defects in software artifacts and models early and efficiently. However, insufficient method and tool support hinder efficient defect detection in large software models. Recent Human Computation and Crowdsourcing processes may help to overcome this limitation by splitting co...

In multi-disciplinary engineering (MDE) projects, information models play an important role as inputs to and outputs of engineering processes. In MDE projects, engineers collaborate from various disciplines, such as mechanical, electrical, and software engineering. These disciplines use general-purpose and domain-specific models in their engineerin...

This book constitutes the refereed proceedings of the 18th International Conference on Product-Focused Software Process Improvement, PROFES 2017, held in Innsbruck, Austria, in November/December 2017. The 17 revised full papers presented together with 10 short papers, 21 workshop papers. 3 posters and tool demonstrations papers, and 4 tutorials wer...

This book constitutes the refereed proceedings of the 9th Software Quality Days Conference, SWQD 2017, held in Vienna, Austria, in January 2017. The SWQD conference offers a range of comprehensive and valuable information by presenting new ideas from the latest research papers, keynote speeches by renowned academics and industry leaders, profession...

Many software projects fail due to problems in requirements engineering (RE). The goal of this paper is analyzing a specific and relevant RE problem in detail: incomplete/hidden requirements. We replicated a global family of RE surveys with representatives of software organizations in Austria and Brazil. We used the data to (a) characterize the cri...

This chapter introduces key concepts of the Industrie 4.0 vision, focusing on variability issues in traditional and cyber-physical production systems (CPPS) and their engineering processes . Four usage scenarios illustrate key challenges of system engineers and managers in the transition from traditional to CPPS engineering environments. We derive...

In Multi-Disciplinary Engineering (MDE) environments, engineers coming from different disciplines have to collaborate. Typically, individual engineers apply isolated tools with heterogeneous data models and strong limitations for collaboration and data exchange. Thus, projects become more error-prone and risky. Although Quality Assurance (QA) metho...

[Context] Many software projects fail due to problems in requirements engineering (RE). [Goal] The goal of this paper is analyzing a specific and relevant RE problem in detail: incomplete/hidden requirements. [Method] We replicated a global family of RE surveys with representatives of software organizations in Austria and Brazil. We used the data t...

This book constitutes the refereed proceedings of the scientific track of the 8th Software Quality Days Conference, SWQD 2016, held in Vienna, Austria, in January 2016. The SWQD conference offers a range of comprehensive and valuable information by presenting new ideas from the latest research papers, keynote speeches by renowned academics and indu...

Die Zusammenarbeit von Fachexperten in einem heterogenen Entwicklungsumfeld im Industrie 4.0 Kontext bringt neben dem verstärkten Bedarf an effizientem Datenaustausch auch Herausforderungen und neue Möglichkeiten an Fachbereiche übergreifenden Maßnahmen der Qualitätssicherung zur Verbesserung der Projekt-, Prozess- und Produktqualität mit sich. Die...

Mechatronic design processes in automation systems engineering projects require concurrent engineering of various disciplines operating within technical, commercial, and project management constraints. Unfortunately, early and efficient quality assurance of technical parameters, which affect risk-relevant constraints in the mechatronic design proce...

Developing large systems engineering projects require combined efforts of various engineering disciplines. Each engineering group uses specific engineering tools and data model concepts representing interfaces to other disciplines. However, individual concepts lack in completeness and include strong limitations regarding interoperability and data e...

Large systems engineering projects involve the cooperation of various stakeholders from different engineering disciplines. Individual stakeholders apply various tools and related data storage approaches that (a) might hinder seamless interoperability and (b) include limited capability to support data versioning. Project-level concepts enable the ma...

In a multi-disciplinary engineering project, such as the parallel engineering of industrial production plants, domain experts want to efficiently monitor project-level constraints that depend on technical parameter values in local engineering models. However, the heterogeneous representations of constraint parameters in these engineering models mak...

Anlagen im Industrie 4.0 Umfeld erfordern den Fachbereiche übergreifenden Austausch von Engineering-Daten. Der Einsatz heterogener Software-Werkzeuglandschaften bestimmt jedoch heute den Engineering-Prozess automatisierter Systeme und wird dies auch noch viele Jahre tun. In der Kombination von standardisierten Datenformaten wie AutomationML und gee...

[Context] In Automation Systems Engineering (ASE) Environments, engineers coming from different disciplines, have to collaborate. Individual engineers, e.g., from electrical, mechanical, or software domains, apply domain-specific tools and related data models that hinder efficient collaboration due to limited capabilities for interaction and data e...

[Context] Software engineering researchers conduct systematic literature reviews (SLRs) to build bodies of knowledge (BoKs). Unfortunately, relevant knowledge collected in the SLR process is not publicly available, which considerably slows down building BoKs incrementally. [Objective] We present and evaluate the Systematic Knowledge Engineering (S...

Engineering project consortia represents the collaboration of different groups of partners to achieve a common goal, e.g., developing innovative products. This collaboration may be challenging because of different terminologies and different tools and data models used by the project partners. In this work, we present the concept of a Project Consor...