Janek Groß

Fraunhofer Institute for Experimental Software Engineering

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Lisa Jöckel

Fraunhofer Institute for Experimental Software Engineering

Michael Kläs

UNSW Sydney

Pascal Gerber

University of Bern

Marc P. Hauer

Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau

Jan Reich

Fraunhofer Institute for Experimental Software Engineering

Thomas Bauer

Fraunhofer Institute for Experimental Software Engineering

Rasmus Adler

Fraunhofer Institute for Experimental Software Engineering

Christof Schroth

Fraunhofer Institute for Experimental Software Engineering

Julien Siebert

Fraunhofer Institute for Experimental Software Engineering

John Molloy

University of York

Cooperative Institutions

Fraunhofer Institute for Experimental Software Engineering

Universidad de Navarra

Fraunhofer Institute for Cognitive Systems

Technical University of Munich

Fraunhofer Society

TRUMPF (Germany)

University of Kaiserslautern

Université Paris Cité

Canadian Nautical Research Society

Institut de physique du globe de Paris

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Architectural Patterns for Handling Runtime Uncertainty of Data-Driven Models in Safety-Critical Perception

Lecture notes in computer science (2022)

Janek Groß Rasmus Adler Michael Kläs Jan Reich Lisa Jöckel

Realization (probability)

10.1007/978-3-031-14835-4_19

Cite

Citations (4)

Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning

Lecture notes in computer science (2023)

Lisa Jöckel Michael Kläs Janek Groß Pascal Gerber Markus Scholz

Python

10.1007/978-3-031-49266-2_10

Cite

Citations (0)

Reliability Estimation of ML for Image Perception: A Lightweight Nonlinear Transformation Approach Based on Full Reference Image Quality Metrics

João-Vitor Zacchi Francesco Carella Priyank Upadhya Shanza Ali Zafar John Molloy

As machine learning (ML) models for image perception continue to advance, ensuring their robustness and reliability under various real-world scenarios remains a significant challenge. Image quality factors, such as blur, brightness, and other environmental conditions, can significantly affect the performance of these algorithms, leading to inaccurate detection and potential failures in critical applications. In this paper, we propose a comprehensive diagnosis framework that leverages image quality metrics to assess and enhance the performance of these algorithms. To accomplish this goal, we deliberately introduce disturbances in parameters such as brightness, saturation, and other relevant factors. Subsequently, we compute a set of full-reference image quality metrics to evaluate the image quality after the perturbations. Once we have obtained the metrics, we apply a nonlinear transformation to these values. Based on the transformed metrics, we create a regression model that predicts the detection Intersection over Union (IOU). To validate our framework, we conducted experiments using three state-of-the-art machine learning models for object detection and instance segmentation. The models were subjected to various scenarios with different levels of image quality perturbations. Our experimental results clearly demonstrate the possibility of establishing a strong correlation between image quality metrics and the performance of the algorithms.

Robustness

10.1109/mcsoc60832.2023.00036

Cite

Citations (0)

Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning?

Lecture notes in computer science (2023)

Lisa Jöckel Michael Kläs Janek Groß Pascal Gerber

Uncertainty Quantification

10.1007/978-3-031-40953-0_26

Cite

Citations (1)

Towards a Common Testing Terminology for Software Engineering and Data Science Experts.

arXiv (Cornell University) (2021)

Lisa Jöckel Thomas Bauer Michael Kläs Marc P. Hauer Janek Groß

Analytical quality assurance, especially testing, is an integral part of software-intensive system development. With the increased usage of Artificial Intelligence (AI) and Machine Learning (ML) as part of such systems, this becomes more difficult as well-understood software testing approaches cannot be applied directly to the AI-enabled parts of the system. The required adaptation of classical testing approaches and the development of new concepts for AI would benefit from a deeper understanding and exchange between AI and software engineering experts. We see the different terminologies used in the two communities as a major obstacle on this way. As we consider a mutual understanding of the testing terminology a key, this paper contributes a mapping between the most important concepts from classical software testing and AI testing. In the mapping, we highlight differences in the relevance and naming of the mapped concepts.

Relevance

Test strategy

Source

Cite

Citations (0)

Towards a Common Testing Terminology for Software Engineering and Data Science Experts

Lecture notes in computer science (2021)

Lisa Jöckel Thomas Bauer Michael Kläs Marc P. Hauer Janek Groß

Relevance

Test strategy

10.1007/978-3-030-91452-3_19

Cite

Citations (4)

A systematic review of Python packages for time series analysis

arXiv (Cornell University) (2021)

Julien Siebert Janek Groß Christof Schroth

This paper presents a systematic review of Python packages with a focus on time series analysis. The objective is to provide (1) an overview of the different time series analysis tasks and preprocessing methods implemented, and (2) an overview of the development characteristics of the packages (e.g., documentation, dependencies, and community size). This review is based on a search of literature databases as well as GitHub repositories. Following the filtering process, 40 packages were analyzed. We classified the packages according to the analysis tasks implemented, the methods related to data preparation, and the means for evaluating the results produced (methods and access to evaluation data). We also reviewed documentation aspects, the licenses, the size of the packages' community, and the dependencies used. Among other things, our results show that forecasting is by far the most frequently implemented task, that half of the packages provide access to real datasets or allow generating synthetic data, and that many packages depend on a few libraries (the most used ones being numpy, scipy and pandas). We hope that this review can help practitioners and researchers navigate the space of Python packages dedicated to time series analysis. We will provide an updated list of the reviewed packages online at https://siebert-julien.github.io/time-series-analysis-python/.

Python

10.48550/arxiv.2104.07406

Cite

Citations (3)

Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts.

arXiv (Cornell University) (2021)

Lisa Jöckel Thomas Bauer Michael Kläs Marc P. Hauer Janek Groß

Analytical quality assurance, especially testing, is an integral part of software-intensive system development. With the increased usage of Artificial Intelligence (AI) and Machine Learning (ML) as part of such systems, this becomes more difficult as well-understood software testing approaches cannot be applied directly to the AI-enabled parts of the system. The required adaptation of classical testing approaches and development of new concepts for AI would benefit from a deeper understanding and exchange between AI and software engineering experts. A major obstacle on this way, we see in the different terminologies used in the two communities. As we consider a mutual understanding of the testing terminology as a key, this paper contributes a mapping between the most important concepts from classical software testing and AI testing. In the mapping, we highlight differences in relevance and naming of the mapped concepts.

Relevance

Test strategy

Source

Cite

Citations (1)

A Systematic Review of Packages for Time Series Analysis

Julien Siebert Janek Groß Christof Schroth

This paper presents a systematic review of Python packages with a focus on time series analysis. The objective is to provide (1) an overview of the different time series analysis tasks and preprocessing methods implemented, and (2) an overview of the development characteristics of the packages (e.g., documentation, dependencies, and community size). This review is based on a search of literature databases as well as GitHub repositories. Following the filtering process, 40 packages were analyzed. We classified the packages according to the analysis tasks implemented, the methods related to data preparation, and the means for evaluating the results produced (methods and access to evaluation data). We also reviewed documentation aspects, the licenses, the size of the packages' community, and the dependencies used. Among other things, our results show that forecasting is by far the most frequently implemented task, that half of the packages provide access to real datasets or allow generating synthetic data, and that many packages depend on a few libraries (the most used ones being numpy, scipy and pandas). We hope that this review can help practitioners and researchers navigate the space of Python packages dedicated to time series analysis. We also provide an updated list of the reviewed packages online.

Python

10.3390/engproc2021005022

Cite

Citations (9)

Towards a Common Testing Terminology for Software Engineering and Data Science Experts

arXiv (Cornell University) (2021)

Lisa Jöckel Thomas Bauer Michael Kläs Marc P. Hauer Janek Groß

Relevance

Test strategy

10.48550/arxiv.2108.13837

Cite

Citations (0)