Seminar FSS 2020

Course Information:

  • Course ID: IE 704
  • Credit Points:
    • B.Sc. Wirtschaftsinformatik: 5 ECTS
    • M.Sc: Wirtschaftsinformatik: 4 ECTS
    • MMDS: 4 ECTS
    • Supervision: Dr. Christian Bartelt


  • Until Sunday, September 20, 2020 (23:59 CEST): Please register for the kick-off meeting by sending three preferred topics and a list of your completed courses (Transcript of Records, CV optional) via mail to Nils Wilken (
  • Tuesday, September 22, 2020: As we can only offer a limited amount of places, you will be informed whether you can participate in this seminar.
  • Friday, September 25, 2020: Latest possible drop-out date without a penalty (A drop-out after this date will be graded with 5.0)
  • Friday, September 25, 2020 (13:45 – 15:15 CET): Milestone 1 – Kick-Off Meeting (most probably as a digital meeting)
  • Sunday, November 22, 2020 (23:59 CET): Milestone 2 – Submission of final seminar paper
  • Sunday, November 29, 2020 (23:59 CET): Milestone 3 – Submission of reviews
  • Monday, November 30 – Wednesday, December 09, 2020: Milestone 4 – Presentation of your final seminar paper:
    • Thursday, December 03, 2020 (12:00 – 13:30 CET): Session 1
      • Static Code Analysis (SCA)
      • Dialogue Systems for Software Engineering: A survey
      • Goal- and Plan Recognition: State of the Art
    • Friday, December 04, 2020 (12:00 – 13:30 CET): Session 2
      • Random Forests in Dynamic Feature Spaces
      • A containerized architecture for Knowledge-driven Architecture Composition
      • Model-Driven Development Approaches for Microservices Architectures
    • Monday, December 07, 2020 (15:30 – 16:30 CET): Session 3
      • A Performance Test for a novel IoT-centric Reuse Approach
      • Explainable Artificial Intelligence (XAI)
  • Sunday, December 20, 2020 (23:59 CET): Milestone 5 – Submission of camera-ready seminar paper and document that indicates the differences between the first submitted version and the camera-ready version of the seminar paper


Important notes

  • Missing a mile-stone will result in a final grade of 5.0.
  • The four parts final paper version, camera-ready paper version, feedback (reviews + presentation feedback), and presentation will all be graded separately, where each part counts 25% of the final grade.
  • This seminar is open for Bachelor and Master Students focusing on “Business Informatics” and “Data Science”. Master students enrolled in the “Mannheim Master in Data Science” are also highly welcome to apply for this seminar.


Suggested Topics

TOPIC 1: Using Temporal Logic for AI-governed Multi-Agent Systems

Introduction: Multi-Agent Systems consist of multiple autonomous entities with individual goals, acting on a shared environment in an intertwined manner. To steer their collective behavior whilst maintaining autonomy, it is necessary for a governing instance to reason about agent strategies and to predict their future actions, which might not just be a straight-forward extrapolation of the current observations but also exhibit a temporal evolution of goals and strategies.

Goal and Objective: The goal of this seminar is to find out how temporal logic—in particular Linear Temporal Logic, but also related or extended logics—can be applied to create an AI-based governance capable of approximating an agent’s (potentially dynamic) strategic behavior from observed actions and subsequently choosing suitable counter-actions leading to a given system goal.

The expected result is a research paper showing relevant existing approaches and their respective benefits and drawbacks, as well as own ideas on how this problem can be defined, formalized and solved.

Starting Papers:

  • A. Artale (2010): Formal Methods, Lecture III: Linear Temporal Logic (Lecture Slides).
  • J. Tumova, D. Dimarogonas (2016). Multi-Agent Planning under Local LTL Specifications and Event-Based Synchronization. Automatica 70:239-248.
  • R. Alur, T. Henzinger, O. Kupferman (2002). Alternating-time temporal logic. J. ACM 49, 5 (September 2002), pp.672–713.


TOPIC 2: Dialogue Systems for Software Engineering: A survey

Introduction: Dialogue systems (more commonly referred to as chatbots) have gained increasing popularity over the past years particularly driven by the large progress of Natural Language Understanding (NLU) technology. Typically, dialogue systems consist of components such as NLU, a dialogue management and Natural Language Generation (NLG) component. The application area of these system has mainly been the automation of various customer services, however, recent research started to adopt these automatic and intelligent interaction systems to support different tasks in software engineering. For example, approaches have been proposed that extract user requirements autonomously from user interaction in Natural Language (NL)  or answer complex developer questions automatically. The goal of this seminar work is to provide a clear overview and discussion of approaches that employ dialogue systems to support different tasks in software engineering.

Goal and Objective: Overview and discussion of different state-of-the-art approaches that apply dilogue systems in the field Software Engineering and comparison between them

Starting Papers:

  • Du, T., Cao, J., Wu, Q., Li, W., Shen, B., & Chen, Y. (2019, November). CocoQa: question answering for coding conventions over knowledge graphs. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 1086-1089). IEEE.
  • Friesen, E., Bäumer, F. S., & Geierhos, M. (2018). CORDULA: Software Requirements Extraction Utilizing Chatbot as Communication Interface. In REFSQ Workshops.
  •  Rietz, T., & Maedche, A. (2019, September). LadderBot: A requirements self-elicitation system. In 2019 IEEE 27th International Requirements Engineering Conference (RE) (pp. 357-362). IEEE.
  •  Rietz, T. (2019, September). Designing a conversational requirements elicitation system for end-users. In 2019 IEEE 27th International Requirements Engineering Conference (RE) (pp. 452-457). IEEE.
  • Arruda, D., Marinho, M., Souza, E., & Wanderley, F. (2019, July). A Chatbot for Goal-Oriented Requirements Modeling. In International Conference on Computational Science and Its Applications (pp. 506-519). Springer, Cham.


TOPIC 3: A Performance Test for a novel IoT-centric Reuse Approach

Introduction: As people are diverse, so is the IoT world. The IoT diversity can be seen in their interfaces and payloads providing similar functionalities. For example, a light switch that controls a lamp (e.g. Phillips Hue) must know which payload (e.g. JSON) to send to which function (e.g. HTTP interface). However, if multiple device manufacturers (i.e. light switch and lamp) do not collaborate, glue code to integrate both devices is required.

Goal and Objective: In this seminar, you will evaluate a novel architecture-centric reuse approach for generating glue code automatically. Therefore, you will create and execute a performance data set for the knowledge-driven architecture composition method. At the end of your seminar, you have gain hands-on experience in performing a structured evaluation for your future studies (e.g. bachelor/master thesis). Furthermore, you gain first insights in IoT challenges from a software engineering perspective.

Starting Papers:

  • Knowledge-driven Architecture Composition:
  • Issarny, V., Bouloukakis, G., Georgantas, N., & Billet, B. (2016, October). Revisiting service-oriented architecture for the IoT: a middleware perspective. In International conference on service-oriented computing (pp. 3-17). Springer, Cham.
  • Eclipse IoT:


TOPIC 4: Comparison of Similarity Measures in their applicability to Vector Sequences in a network

Introduction: To achieve meaningful clustering and classification appropriate similarity measures are required. The key challenge is to choose the right similarity measure. Vector Sequence Networks are a new prototype approach to give a condensed view on the workflow of complex software systems.

Goal and Objective: Your task in this seminar will be to examine and evaluate the applicability of current similarity measures to vector sequence networks. The overarching goal is to find appropriate clustering procedures for similar sequences inside the network.

Starting Papers:

  • Sidorov, Grigori, et al. "Soft similarity and soft cosine measure: Similarity of features in vector space model." Computación y Sistemas 18.3 (2014): 491-504.S. Maes, S. Meganck, B.
  • Xia, Peipei, Li Zhang, and Fanzhang Li. "Learning similarity with cosine similarity ensemble." Information Sciences 307 (2015): 39-52.


TOPIC 5: Static Code Analysis (SCA)

Introduction: Static code analysis (SCA) is a common technique for checking programs for flaws security vulnerabilities. In contrast to other approaches (e.g., dynamic analysis), the static analysis is feasible in an early stage of the development lifecycle.

Goal and Objective: This seminar aims to examine SCA based on categories in which different approaches of SCA (tools) can get clustered. The starting papers offer a taxonomy [1] of criterion to start from and benchmarks on how to evaluate static analysis tools [2]. The seminarist should:

1. Introduce SCA in general

2. Compare the different (SCA) approaches found in the literature

3. Give details on their advantages and disadvantages

Starting Papers:

  • [1] Novak, J., & Krajnc, A. (2010, May). Taxonomy of static code analysis tools. In The 33rd International Convention MIPRO (pp. 418-422). IEEE.
  • [2] Nunes, P., Medeiros, I., Fonseca, J. C., Neves, N., Correia, M., & Vieira, M. (2018). Benchmarking static analysis tools for web security. IEEE Transactions on Reliability, 67(3), 1159-1175.


TOPIC 6: Explainable Artificial Intelligence (XAI)

Introduction: Currently, the explainability of AI techniques (XAI) is a widely discussed topic, both in AI research and in society. Missing or insufficient explanatory capabilities of AI-based systems are a serious limitation for the use of these techniques in many applications domains for various reasons.

Goal and Objective: In this seminar you will familiarize yourself with XAI approaches for AI techniques (like artificial neural networks) based on scientific literature as well as your own exploration.

Starting Papers:

  • D. Carvalho, E. Pereira, and J. Cardoso. Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics, 8(8):832–865, 2019.
  • L. Gilpin, D. Bau, B. Yuan, A. Bajwa, M. Specter, and L. Kagal. Explaining Explanations: An Overview of Interpretability of Machine Learning. In 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pages 80–89, 2018.
  • Christoph Molnar. Interpretable Machine Learning. book/. 2019      


TOPIC 7: Model-Driven Development Approaches for Microservices Architectures

Introduction: There is a recent trend in the adoption of microservice architectures. The design and implementation of such architectures, however, is a complex task because of the high amount of involved autonomous stakeholders. This is especially true, if microservices are reused in many different use cases, as many functional and non-functional requirements have to be considered in a consistent way across the services, to achieve a use case specific API.

Model-driven development approaches may be beneficial in that regard, as they could reduce the involved complexity in such development processes.

Goal and Objective: For this seminar paper you should provide an overview and classification of the kinds of model-driven development approaches which are suited for the distributed development processes of microservice architectures which consider all the (non-)functional requirements to compose an API for different use cases. Additionally you should provide the advantages and disadvantage of the types of the found approaches.

Starting Papers:

  • F. Rademacher, S. Sachweh, und A. Zündorf, „Analysis of Service-oriented Modeling Approaches for Viewpoint-specific Model-driven Development of Microservice Architecture“, arXiv:1804.09946 [cs], Apr. 2018, Zugegriffen: Juli 10, 2020. [Online]. Verfügbar unter:
  • D. Ameller, X. Burgués, D. Costal, C. Farré, und X. Franch, „Non-functional requirements in model-driven development of service-oriented architectures“, Science of Computer Programming, Bd. 168, S. 18–37, Dez. 2018, doi: 10.1016/j.scico.2018.08.001.
  • F. Rademacher, J. Sorgalla, S. Sachweh, und A. Zündorf, „Viewpoint-Specific Model-Driven Microservice Development with Interlinked Modeling Languages“, in 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), Apr. 2019, S. 57–5709, doi: 10.1109/SOSE.2019.00018.


TOPIC 8: Random Forests in Dynamic Feature Spaces

Introduction: Random Forests have become a go to method for a lot of machine learning problems. Originating from Decision Trees, they were originally intended for offline learning settings. However, this seminar topic focuses on Random Forests for Online Learning. Random Forests provide good results consistently by combining several smaller Decision Trees for prediction. However, not only their performance but also their robustness make them interesting for an online learning problem.

Goal and Objective: In this topic, you will explore the state of the art literature for Random Forests and evaluate their fit for an online learning setting with regard to a dynamic feature spaces. In such dynamic feature spaces features are missing occasionally, vanish or new features emerge.

Starting Papers:

  • Amir Saffari, Christian Leistner, Jakob Santner, Martin Godec, and Horst Bischof, "On-line Random Forests," 3rd IEEE ICCV Workshop on On-line Computer Vision, 2009.


TOPIC 9: Goal- and Plan Recognition: State of the Art

Introduction: Recently, activity recognition became very popular, especially in the fields of ubiquitous and pervasive computing. The major application domains for these approaches are in health- and elderly care. However, these approaches only allow a supporting system to react to recognized incidents (for example a recognized fall). To enable such systems to also act proactively, it has to be able to reason about current goals and plans of an observed agent.
Goal recognition is defined as the problem to recognize the current goal(s) of an agent given a sequence of observations of this agent. Respectively, plan recognition is defined as the problem of recognizing the entire plan of an agent given a sequence of observations of the agent.

Goal and Objective: Over the past decades several different approaches to goal- and plan recognition evolved. Thereby, some rely on symbolic methods (like classical AI planning), whereas others make use of statistical methods (e.g., Bayesian Networks). The objective of this seminar is to provide an overview of the current state-of-the-art in the field of goal- and plan recognition and also highlight shortcomings of current approaches.

Starting Papers:

  • Albrecht, David W., et al. "Towards a Bayesian model for keyhole plan recognition in large domains." User Modeling. Springer, Vienna, 1997.
  • M. Ramírez and H. Geffner, “Probabilistic plan recognition using off-the-shelf classical plan-ners,”  in  Proceedings  of  the  Twenty-Fourth  AAAI  Conference  onArtificial Intelligence, ser. AAAI’10.   AAAI Press, 2010, p. 1121–1126
  • R. F. Pereira, N. Oren, and F. Meneguzzi, “Landmark-based approachesfor goal recognition as planning,” Artificial Intelligence, vol. 279, 2020,p. 103217
  • Y. Zeng, K. Xu, Q. Yin, L. Qin, Y. Zha, and W. Yeoh, “Inverse reinforce-ment learning based human behavior modeling for goal recognition indynamic local network interdiction,” in Workshops at the Thirty-SecondAAAI Conference on Artificial Intelligence, 2018.

To top

Postal adressVisitor adressEmailPhone
Institute for Enterprise Systems  L15,  Office Management
Schloss68131 Mannheim +49 621 181-3560
68131 MannheimGermany