Browsing by Author "Senger, Tobias"

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Open Access
Enhancing automotive safety through an ADAS violation dashboard
(2024) Senger, Tobias
Autonomous Driving (AD) is an active area of research in which Advanved Driver Assistance Systems (ADAS) play an important role. Ensuring the safety of ADAS systems is critical. However, most ADAS systems nowadays make use of Deep Learning or other types of Machine Learning. Formally verifying these systems to ensure their safety is hardly possible. For this reason, Radic explored the use of Runtime Monitoring (RM) to ensure the safety of ADAS systems by detecting violations of several specified Safety Requirements (SR) at runtime. After performing a test run with the system, she manually analyzed the causes of each series of violations in the extracted Violations Report. As this was laborious and time-consuming, this thesis should explore available approaches and techniques to automatically derive the root causes of violation series. To do this, we first perform an exploratory literature search. This allows us to identify that the most suitable approach to address our problem is Root Cause Analysis (RCA) using Language Models (LMs), Large Language Models (LLMs), Knowledge Graphs (KGs), or a combination of them. We perform a Rapid Review (RR) to find concrete techniques for this approach. We then conduct a narrative data synthesis to explore the techniques retrieved with our RR. This allows us to derive a plan to automatically analyze the causes of SR violations in a Violations Report. Our solution is then incorporated into a web-based safety dashboard application. This application enables our safety engineers to configure ADAS use cases, test tracks, and test runs. Then, the safety engineer can select a test run to display an interactive view of the test run. The safety engineer can then select individual violation series and analyze their root causes using our automated RCA solution based on LLMs. To evaluate the effectiveness of our system, we conduct a simple experiment. This experiment shows that our system already achieves comparable performance to a human baseline provided by Radic. Our system, therefore, represents a valuable tool for safety engineers to identify and repair safety-critical problems in ADAS systems in the context of AD. We also propose modified variants of our system that allow researchers to improve our automated RCA system in the future, e.g., by incorporating a KG.
Open Access
A unified open- and closed-source software requirements dataset
(2022) Senger, Tobias
Requirements Engineering (RE) has proven to be an important factor for the success of a software project. The common use of natural language for writing requirements often results in problems that should be detected and avoided early. For this reason, we want to build automatic tools to support the process of specifying requirements using Deep Learning (DL). However, training robust DL models is very data-intensive and the RE community still suffers from a lack of large-scale requirement datasets that are easy to use. Therefore, the goal of this study is to create such a dataset that can be used for various tasks in the RE domain. To do this, we collect functional and non-functional requirements from a large number of both open and closed source software projects and combine them into a unified dataset using a simple data format. We then train a DL model for automatically classifying functional and non-functional requirements to show the potential of our dataset for training efficient DL models. We compare its performance with a state-of-the-art model and students at the University of Stuttgart. We also examine the differences between the open and closed source requirements in our dataset and compare the textual corpus of our dataset with common English datasets and corpora. Our studies showed that our model outperforms both the state-of-the-art model and most of the students. Further, we observed remarkable differences between the open and closed source requirements and found that our requirements use a unique vocabulary compared to common English texts.