Eric Knauss

University of Gothenburg | GU · Computer Science and Engineering

Large-scale systems development commonly faces the challenge of managing relevant knowledge between different organizational groups, particularly in increasingly agile contexts. Here, there is a conflict between coordination and group autonomy, and it is challenging to determine what necessary coordination information must be shared by what teams o...

Marine in-situ data is collected by sensors mounted on fixed or mobile systems deployed into the ocean. This type of data is crucial both for the ocean industries and public authorities, e.g., for monitoring and forecasting the state of marine ecosystems and/or climate changes. Various public organizations have collected, managed, and openly shared...

The integration of human factors (HF) knowledge is crucial when developing safety-critical systems, such as automated vehicles (AVs). Ensuring that HF knowledge is considered continuously throughout the AV development process is essential for several reasons, including efficacy, safety, and acceptance of these advanced systems. However, it is chall...

Due to the technical complexity and social impact, automated vehicle (AV) development challenges the current state of automotive engineering practice. Research shows that it is important to consider human factors (HF) knowledge when developing AVs to make them safe and accepted. This study explores the current practices and challenges of the automo...

Research shows that many of the challenges currently encountered with agile development are related to requirements engineering. Based on design science research, this paper investigates critical challenges that arise in agile development from an undefined requirements strategy. We explore potential ways to address these challenges and synthesize t...

The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that behaviour. Runtime moni...

Driving automation systems, including autonomous driving and advanced driver assistance, are an important safety-critical domain. Such systems often incorporate perception systems that use machine learning to analyze the vehicle environment. We explore new or differing topics and challenges experienced by practitioners in this domain, which relate...

In today’s rapidly evolving technological landscape, the success of tools and systems relies heavily on their ability to meet the needs and expectations of users. User-centered design approaches, with a focus on human factors, have gained increasing attention as they prioritize the human element in the development process. With the increasing compl...

Good data quality is crucial for any data-driven system’s effective and safe operation. For critical safety systems, the significance of data quality is even higher since incorrect or low-quality data may cause fatal faults. However, there are challenges in identifying and managing data quality. In particular, there is no accepted process to define...

The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lie...

Background: Driving automation systems (DAS), including autonomous driving and advanced driver assistance, are an important safety-critical domain. DAS often incorporate perceptions systems that use machine learning (ML) to analyze the vehicle environment. Aims: We explore new or differing requirements engineering (RE) topics and challenges that pr...

[Context and motivation] The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for tha...

Software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, specifically the training and validation of the machine learning components, require large annotated datasets. An industry of data and annotation services has emerged to serve...

Background: Driving automation systems (DAS), including autonomous driving and advanced driver assistance, are an important safety-critical domain. DAS often incorporate perceptions systems that use machine learning (ML) to analyze the vehicle environment. Aims: We explore new or differing requirements engineering (RE) topics and challenges that pr...

Context and motivation: The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that...

Artificial intelligence (AI) in its various forms finds more and more its way into complex distributed systems. For instance, it is used locally, as part of a sensor system, on the edge for low-latency high-performance inference, or in the cloud, e.g. for data mining. Modern complex systems, such as connected vehicles, are often part of an Internet...

Artificial intelligence (AI) in its various forms finds more and more its way into complex distributed systems. For instance, it is used locally, as part of a sensor system, on the edge for low-latency high-performance inference, or in the cloud, e.g. for data mining. Modern complex systems, such as connected vehicles, are often part of an Internet...

In order to increase the ability to build complex, software-intensive systems, as well as to decrease time-to-market for new functionality, automotive companies aim to scale agile methods beyond individual teams. This is challenging, given the specifics of automotive systems that are often safety-critical and consist of software, hardware, and mech...

Context: Many companies have been adopting data-driven applications in which products and services are centered around data analysis to approach new segments of the marketplace. Data ecosystems rise from data sharing among organizations premeditatedly. However, this migration to this new data sharing paradigm has not come that far in the marine dom...

Research shows that many of the challenges currently encountered with agile development are related to requirements engineering. Based on design science research, this paper investigates critical challenges that arise in agile development from an undefined requirements strategy. We explore potential ways to address these challenges and synthesize t...

Context: Agile methods have become mainstream even in large-scale systems engineering companies that need to accommodate different development cycles of hardware and software. For such companies, requirements engineering is an essential activity that involves upfront and detailed analysis which can be at odds with agile development methods. Objecti...

Using models for requirements engineering (RE) is uncommon in systems engineering, despite the widespread use of model-based engineering in general. One reason for this lack of use is that formal models do not match well the trend to move towards agile developing methods. While there exists work that investigates challenges in the adoption of requi...

The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the...

Artificial Intelligence (AI), and especially machine learning can be used to find statistical patterns in datasets with thousands of variables with ease. But an understanding of causality is difficult to learn for a machine. For humans however, realising causal relations is often not a difficult process, as we can refer to experience or scientific...

In order to increase the ability to build complex, software-intensive systems, as well as to decrease time-to-market for new functionality, automotive companies aim to scale agile methods beyond individual teams. This is challenging, given the specifics of automotive systems that are often safety-critical and consist of software, hardware, and mech...

[Context and motivation] For automated driving systems, the operational context needs to be known in order to state guarantees on performance and safety. The operational design domain (ODD) is an abstraction of the operational context, and its definition is an integral part of the system development process. [Question / problem] There are still maj...

[Context and motivation] For automated driving systems, the operational context needs to be known in order to state guarantees on performance and safety. The operational design domain (ODD) is an abstraction of the operational context, and its definition is an integral part of the system development process. [Question/problem] There are still major...

Context and motivation] Good quality of data is crucial for effective and safe operation of any system. For safety critical systems such as automated driving, the significance of data quality is even higher since wrong or low-quality data may cause fatal faults. [Ques-tion / problem] However, there are challenges in identifying and managing data qu...

Context . In automotive, stage-gate processes have previously been the norm, with architecture created mainly during an early phase and then used to guide subsequent development phases. Current iterative and Agile development methods, where the implementation evolves continuously, changes the role of architecture. Objective . We investigate how ar...

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...

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...

[Context & motivation] Driven by the need for faster time-to-market and reduced development lead-time, large-scale systems engineering companies are adopting agile methods in their organizations. This agile transformation is challenging and it is common that adoption starts bottom-up with agile software teams within the context of traditional compa...

Practitioners are poorly supported by the scientific literature when managing traceability information models (TIMs), which capture the structure and semantics of trace links. In practice, companies manage their TIMs in very different ways, even in cases where companies share many similarities. We present our findings from an in-depth focus group a...

Distributed software development is more difficult than co-located software development. One of the main reasons is that communication is more difficult in distributed settings. Defined processes and artifacts help, but cannot cover all information needs. Not communicating important project information, decisions and rationales can result in duplic...

Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper w...

Context Agile methods have become mainstream even in large-scale systems engineering companies that need to accommodate different development cycles of hardware and software. For such companies, requirements engineering is an essential activity that involves upfront and detailed analysis which can be at odds with agile development methods. Objecti...

Context: Software engineering researchers and practitioners rely on empirical evidence from the field. Thus, education of software engineers must include strong and applied education in empirical research methods. For most students, the master's thesis is the last, but also most applied form of this education in their studies. Problem: Especially t...

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context : The relevance of Requirements Engineering (RE) research to practitioners is vital for a long-term dissemination of research results to everyday practice. Some authors have speculated about a mismatch between research and practice in the RE di...

The relevance of Requirements Engineering (RE) research to practitioners is vital for a long-term dissemination of research results to everyday practice. Some authors have speculated about a mismatch between research and practice in the RE discipline. However, there is not much evidence to support or refute this perception. This paper presents the...

Large-scale systems development commonly faces the challenge of managing relevant knowledge between different organizational groups, particularly in increasingly agile contexts. In previous studies, we found the importance of analyzing methodological islands (i.e., groups using different development methods than the surrounding organization) and bo...

Practitioners are poorly supported by the scientific literature when managing traceability information models (TIMs), which capture the structure and semantics of trace links. In practice, companies manage their TIMs in very different ways, even in cases where companies share many similarities. We present our findings from an in-depth focus group a...

https://arxiv.org/abs/2008.07879 Large-scale companies commonly face the challenge of managing relevant knowledge between different organizational groups, particularly in increasingly agile contexts. In previous studies, we found the importance of analyzing methodological islands (i.e., groups using different development methods than the surroundi...

Traceability is a key enabler of various activities in automotive software and systems engineering and required by several standards. However, most existing traceability management approaches do not consider that traceability is situated in constantly changing development contexts involving multiple stakeholders. Together with an automotive supplie...

Traceability is a key enabler of various activities in automotive software and systems engineering and required by several standards. However, most existing traceability management approaches do not consider that traceability is situated in constantly changing development contexts involving multiple stakeholders. Together with an automotive supplie...

Large-scale system development companies are increasingly adopting agile methods. While this adoption may improve lead-times, such companies need to balance two trade-offs: (i) the need to have a uniform, consistent development method on system level with the need for specialised methods for teams in different disciplines(e.g., hardware, software,...

Traceability is crucial for many activities in software and systems engineering including monitoring the development progress, and proving compliance with standards. In practice, the use and maintenance of trace links are challenging as artifacts undergo constant change, and development takes place in distributed scenarios with multiple collaborati...

The automotive domain is rapidly changing in the last years. Among the different challenges OEMs (i.e. the vehicle manufacturers) are facing, vehicles are evolving into systems of systems. In fact, over the last years vehicles have evolved from disconnected and “blind” systems to systems that are (i) able to sense the surrounding environment and (i...

In large-scale automotive companies, various requirements engineering (RE) practices are used across teams. RE practices manifest in Requirements Information Models (RIM) that define what concepts and information should be captured for requirements. Collaboration of practitioners from different parts of an organization is required to define a suita...

https://arxiv.org/abs/2001.01598

Automotive companies increasingly adopt scaled agile methods to allow them to deal with their organisational and product complexity. Suitable methods are needed to ensure safety when developing automotive systems. On a small scale, R-Scrum and SafeScrum are two concrete suggestions for how to develop safety-critical systems using agile methods. How...

Traditionally, software APIs (application programming interfaces) have been viewed from a technical perspective, as a means to separate implementation from functional calls, and as a way to define a contract of software functionality. The technical benefits of APIs have been reported in numerous studies. Several reports from industry offer useful p...

Automotive companies increasingly adopt scaled agile methods to allow them to deal with their organisational and product complexity. Suitable methods are needed to ensure safety when developing automotive systems. On a small scale, R-Scrum and SafeScrum® are two concrete suggestions for how to develop safety-critical systems using agile methods. Ho...

Automotive manufacturers have historically adopted rigid requirements engineering processes. This allowed them to meet safety-critical requirements when producing a highly complex and differentiated product out of the integration of thousands of physical and software components. Nowadays, few software-related domains are as rapidly changing as the...

Several studies report that the use of model-centric methods in the automotive domain is widespread and offers several benefits. However, existing work indicates that few modelling frameworks explicitly include requirements engineering (RE), and that natural language descriptions are still the status quo in RE. Therefore, we aim to increase the und...

Recent developments in agile methods at scale and continuous delivery have successfully removed major bottlenecks that have, so far, limited the speed at which software can be developed, delivered, and evaluated by customers and end users. Now, the ability to manage requirements and related knowledge in continuous software engineering has become a...

http://regot.chalmers.se/wp-content/uploads/2019/04/2019_JSME_Wohlrab.pdf Agile methods are increasingly introduced in automotive companies in the attempt to become more efficient and flexible in the system development. The adoption of agile practices influences communication between stakeholders and makes companies rethink the management of artif...

Practitioners struggle with creating and evolving an architecture when developing complex and safety-critical systems in large-scale agile contexts. A key issue is the trade-off between upfront planning and flexibility to embrace change. In particular, the coordination of interfaces is an important challenge, as interfaces determine and regulate th...