13 citations · 26 across the 12 of their papers we have counts for
13 papers
A Data Annotation Requirements Representation and Specification (DARS)
Yi Peng, Hina Saeeda, Hans-Martin Heyn +3
With the rise of AI-enabled cyber-physical systems, data annotation has become a critical yet often overlooked process in the development of these intelligent information systems.…
From Machine Learning Documentation to Requirements: Bridging Processes with Requirements Languages
Yi Peng, Hans-Martin Heyn, Jennifer Horkoff
In software engineering processes for machine learning (ML)-enabled systems, integrating and verifying ML components is a major challenge. A prerequisite is the specification of ML…
Tutorial Debriefing: Applied Statistical Causal Inference in Requirements Engineering
Julian Frattini, Hans-Martin Heyn, Robert Feldt +1
As any scientific discipline, the software engineering (SE) research community strives to contribute to the betterment of the target population of our research: software producers…
Causal Models in Requirement Specifications for Machine Learning: A vision
Hans-Martin Heyn, Yufei Mao, Roland Weiss +1
Specifying data requirements for machine learning (ML) software systems remains a challenge in requirements engineering (RE). This vision paper explores causal modelling as an RE a…
Automated Configuration Synthesis for Machine Learning Models: A git-Based Requirement and Architecture Management System
Abdullatif AlShriaf, Hans-Martin Heyn, Eric Knauss
This work introduces a tool for generating runtime configurations automatically from textual requirements stored as artifacts in git repositories (a.k.a. T-Reqs) alongside the soft…
VEDLIoT -- Next generation accelerated AIoT systems and applications
Kevin Mika, René Griessl, Nils Kucza +29
The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propo…
Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
Hans-Martin Heyn, Khan Mohammad Habibullah, Eric Knauss +4
Software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, speci…
Requirements Engineering for Automotive Perception Systems: an Interview Study
Khan Mohammad Habibullah, Hans-Martin Heyn, Gregory Gay +6
Background: Driving automation systems (DAS), including autonomous driving and advanced driver assistance, are an important safety-critical domain. DAS often incorporate perception…
An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications
Hans-Martin Heyn, Eric Knauss, Iswarya Malleswaran +1
Context and motivation: The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the t…
A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems
Hans-Martin Heyn, Eric Knauss, Patrizio Pelliccione
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…
VEDLIoT: Very Efficient Deep Learning in IoT
Martin Kaiser, Rene Griessl, Nils Kucza +33
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 deali…
Setting AI in context: A case study on defining the context and operational design domain for automated driving
Hans-Martin Heyn, Padmini Subbiash, Jennifer Linder +2
[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 dom…
Requirement Engineering Challenges for AI-intense Systems Development
Hans-Martin Heyn, Eric Knauss, Amna Pir Muhammad +5
Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applicat…