Chair of Software Engineering

Software Engineering Seminar

The chair SEDA organizes the joint Software Engineering Seminar for Bachelor and Master students in Winter semester 23/24. The goal of the seminar is to introduce students to the critical reading, understanding, summarizing, and presentation of scientific papers. Contents are selected topics from the field of software and systems engineering, in particular:

  • Systems Engineering for Cyber-Physical Systems
  • Safety, Security, Reliability and Availability
  • Risk-Assessment and -Minimization
  • Model-Based Safety Analysis

News and Announcements

05.07.2023Registration is now possible. Please fill out the form at this link
10.08.2023

Registration is now closed as we have more than enough students for the upcoming seminar in WS 2023/24.

All future seminar offerings can be found on OLAT.

The deadline for registration is31.08.2023. A final confirmation of who can participate in the seminar can only be given after the registration deadline. Usually, there are more registrations than available topics, which is why we will probably have to draw lots for the free places.

Note:- We can only offer topics according to the "first come, first served" principle. This means that those who applied well in advance have given the highest priority. 

Since the number of students who can attend is limited, we have identified the following seminars that you can attend instead, for the same specialization (Software Engineering, SE).

Timeline

Kickoff-Meeting23.10.23 (Slides)
Annotated table of contents20.11.23
First version of written papers18.12.23
Final version of written papers05.02.24
PresentationsTBD


At the kick-off meeting, the organization of the seminar is discussed and contact is made with the supervisors. After a few weeks, the participants prepare a table of contents (TOC) with some key points to the planned contents of the work. In the following, all participants create a written paper on their topic. The work should be discussed in regular meetings with the supervisors. The first version of the written paper is to be finished by 18.12.23 and will serve as a basis for final feedback from the supervisors. The final version of the paper is to be completed by 05.02.24. At the end the works will be presented at a final meeting.

Material

The seminar will be held in English. Bachelor students are free to choose between German or English

Paper

Please use themodified LNCS template for your paper. Your paper should be about 10 pages (bachelor) or 15 pages (master) long (not including figures).

Presentations

Please use our templates for PowerPoint. The duration of the presentations can be found in the schedule above.

Organizers

Topics Overview

Note: Click on a topic to open the detailed view.

Depending on the supervisors, not all topics can be worked on in all languages. Group work is possible for some topics.

Description :

Machine Learning methods generated some stunning results over the last decades and are increasingly influencing our all lives. Nevertheless there black box character still holds challenges in the the safety assessment. The technique of conformal prediction seems to provide, in theory at least, one mathematical solution for this problem. Nevertheless for ”non-restricted” input this technique can break down. Therefore we need to ask the question, if Conformal Prediction can produce reliable results in the context of Image Input data.

Literature :

Matteo Fontana, Gianluca Zeni, and Simone Vantini. Conformal prediction: a unified review of theory and new challenges, 2022.

more provided after topic assignment

Supervisor: Alexander Günther

Description :

Machine Learning methods generated some stunning results over the last decades and are increasingly influencing our all lives. Therefore the usage of latter ones might be also beneficial in risk assessment for safety critical systems. Nevertheless there black box character still holds challenges in there reliability and trustworthiness. Thus we need to ask the question, if Machine Learning methods can produce reliable results in the context of risk assessment.

Literature :

Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities.

More provided after topic assignment

Supervisor: Alexander Günther

Description :

This topic will provide an in-depth exploration of the risk features and their corresponding thresholds in both the situational space and within the Adaptive Cruise Control (ACC) system. The comprehensive examination of this space is critical to understand the myriad of risk features that could potentially affect the functionality of ACC. Defining thresholds for these risk features in the situational space is imperative for discerning deviations and optimizing the response of ACC systems to dynamic highway conditions. A literature survey must be conducted to meticulously define and evaluate thresholds for risk features inherent in ACC systems within highway scenarios, with the aim of developing a profound understanding of the interaction and impact of these features within situational spaces and system components. This research intends to facilitate the enhancement of ACC systems by improving their accuracy and reliability in diverse and dynamic highway environments.

Literature :

(1). Chia, W. M. D., Keoh, S. L., Michala, A. L., & Goh, C. (2021, April). Real-time recursive risk assessment framework for autonomous vehicle operations. In 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) (pp. 1-7). IEEE.

(2). Fahmy, H. M., Abd El Ghany, M. A., & Baumann, G. (2018). Vehicle risk assessment and control for lane-keeping and collision avoidance at low-speed and high-speed scenarios. IEEE Transactions on Vehicular Technology, 67(6), 4806-4818.

(3). Ammar, H. H., Nikzadeh, T., & Dugan, J. B. (2001). Risk assessment of software-system specifications. IEEE transactions on reliability, 50(2), 171-183.

Supervisor: Anil Patel

Description :

This research endeavor aims to traverse the extensive and varied landscape of risk assessment methodologies, offering a meticulous inquiry into their multidimensional aspects. The exploration focuses on analyzing the principles, frameworks, and applications of diverse risk assessment methodologies. It considers the qualitative and quantitative dimensions, evaluating their relevance and applicability at design time and runtime, and assessing both internal and external risk dimensions. The study also distinguishes between deterministic and data-driven approaches, elucidating their respective merits and demerits. By adopting a comprehensive perspective, the research aims to contribute insightful findings and enhanced understandings to the academic discourse surrounding risk assessment strategies, thereby facilitating more informed and resilient decision-making processes in various domains.

Literature :

(1). Chia, W. M. D., Keoh, S. L., Goh, C., & Johnson, C. (2022). Risk assessment methodologies for autonomous driving: A survey. IEEE transactions on intelligent transportation systems, 23(10), 16923-16939.

(2). Hegde, J., & Rokseth, B. (2020). Applications of machine learning methods for engineering risk assessment–A review. Safety science, 122, 104492.

Supervisor: Anil Patel

Description :

The   growing   interest   in  automated vehicles reveals a gap in traditional safety approaches, which oft en rely on human drivers as a safety measure. The complexity of Automated Driving Systems (ADS) challenges existi ng safety standards like ISO 26262. Industry experts are calling for new safety paradigms and risk models to address these challenges. Advanced methods, such as model-based automated tools, are emerging to assess functi onal safety in this new context. These developments highlight the need for updated safety approaches and regulati ons for autonomous driving. This seminar aims at understanding the functi onal safety and its related concepts. Exploring the open ends and challenges with respect to automated driving systems.

Literature :

  • Functional Safety of Automated Driving: Does ISO 26262 meet the challenges?
  • Automated Functional Safety Analysis of Automated Driving Systems
  • Why functional safety experts worry about automotive systems having increasing autonomy
  • Safety goals and functional safety requirements for actuation systems  of automated vehicles
  • Enabling functi onal safety ASIL compliance for autonomous driving soft ware systems

Supervisor: Nikita Bhardwaj Haupt

Description :

Ensuring complete safety cases is a key challenge for autonomous vehicles, parti cularly within the Operati onal Design Domain (ODD) where the Automated Driving System (ADS) functi ons. The text off ers four strategies to prevent ADS from exiti ng its ODD, and introduces use cases and a framework to categorize operati ng conditi ons. This modular approach allows for the conti nuous delivery of ADS features. This seminar aims to answer questi ons: What are the most eff ecti ve methods for defi ning and confi ning an ODD  in  ADS? How can use cases be  integrated into  safety models to  ensure  that autonomous   vehicles   operate   safely   within   their   ODD?,   and   what   frameworks   are available or in development for categorizing operati ng conditi ons in the ODD?

Literature :

  • Towards an Operational Design Domain That Supports the Safety Argumentati on of an Automated Driving System
  • SAE J3016:201806 - SURFACE VEHICLE RECOMMENDED PRACTICE - Taxonomy and Defi niti ons for Terms Related to Driving Automati on Systems for On-Road Motor Vehicles
  • Definition and identificati on of system boundaries of highly automated driving
  • An   automated   vehicle   safety   concept   based   on   runti me   restricti on   of   the operati onal design domain
  • How many operati onal design domains, objects, and events?”

Supervisor: Nikita Bhardwaj Haupt

Topic Application

To apply for seminar topics, please proceed as follows:

  1. In the list above, select the topics you would like to work on. We recommend that you select more than one topic, as not everyone can get their preferred topic. The selection of several topics increases the chance of receiving a topic.

    Sort your selection in descending order by priority, as shown in the following example: T5 > T4 > T1

    Here, the topic T5 is the first choice, T4 the second choice and T1 the third choice. You can list as many topics as you like.

  2. Optional: If group work is possible on one or more of your selected topics and you already know fellow students with whom you would like to work, please let us know.

    To do this, list the names of your fellow students in a second line, as shown in the following example: Name1, Name2

    This information is independent of your choice of topics from step 1. It is sufficient if your fellow students apply for a topic identical to yours. Our algorithm prefers to form groups of students who know each other. You can also apply alone for topics with group work and will then be randomly matched with other students.

  3. Please let us know if you need a grade for the seminar. In case of doubt, clarify this question with your examination office. Usually, most students only receive an ungraded certificate. Grade: no

Send us this information in a brief E-Mail until Mon, 02.10.2023 at 12 hours. We will do our best to include as many of you as possible.

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