Web Engineering Seminar (SS 2024)

Questions:

• How can distributed data be acquired trustworthy with goverance?
• What is a reasonable approach for introducing goverance to common web application architectures like MVC?
• What are limits and open challenges for goverance for trustworthy data acquisition?

Literature:

• https://dl.acm.org/doi/full/10.1145/3507910
• https://www.alexandria.unisg.ch/256718/1/_AAMAS_2019__A_Decade_in_Hindsight__The_Missing_Bridge_Between_Multi_Agent_Systems_and_the_World_Wide_Web.pdf
• https://link.springer.com/article/10.1007/s42979-022-01424-2

Questions:

• What are common ways to classify the one or several topics of a linked data ressource?
• How accurate and fast are those approaches?
• How to tackle heterogenity of the web when it comes to topic classifaction of linked data?

Literature:

• own research

Questions:

• What are Dark Patterns? Where can they be found? How are they defined? List commonly known taxonomies. What are Usability Smells and where is the difference to Dark Patterns?
• What are Conversational User Interfaces? Show us different kinds and how they work. Which Dark Patterns could be adapted to Chatbots? Which Dark Patterns could be new in Chatbots?
• You will get access to a dataset of negative user interactions with chatbots via a repository. Search additional samples and document the procedure (where did you search, which search terms did you use, list of all examples that you found, list of examples that you excluded, ...), add at least 20 new samples.
• Create a codebook (here, a table) where you list both the given and the newly added samples and code them according to the following criteria: Is it an established Dark Pattern? Is it a potential new Dark Pattern? Is it a Usability Smell? Is it neither? Are information missing for a decision? Both students should code the samples independently and then meet together with the advisor to discuss the results and unclear cases. Present the consolidated results in a demonstration.
• Reflect on the process: What did you learn? Where did you have problems? How could you solve them?

Literature:

• Own research
• Traubinger, V., Heil, S., Grigera, J., Garrido, A., Gaedke, M. (2024). In Search of Dark Patterns in Chatbots. In: Følstad, A., et al. Chatbot Research and Design. CONVERSATIONS 2023. Lecture Notes in Computer Science, vol 14524. Springer, Cham. https://doi.org/10.1007/978-3-031-54975-5_7
• Colin M. Gray, Nataliia Bielova, Cristiana Santos, and Thomas Mildner. 2023. An Ontology of Dark Patterns: Foundations, Definitions, and a Structure for Transdisciplinary Action. https://arxiv.org/abs/2309.09640
• Brignull, H.: Deceptive patterns. https://www.deceptive.design
• Gray, C.M., Sanchez Chamorro, L., Obi, I., Duane, J.N.: Mapping the landscape of dark patterns scholarship: A systematic literature review. In: Companion Pub- lication of the 2023 ACM Designing Interactive Systems Conference. pp. 188–193 (2023)
• Gray, C.M., Santos, C., Bielova, N.: Towards a preliminary ontology of dark pat- terns knowledge. In: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. pp. 1–9 (2023
• Grigera, J., Garrido, A., Rivero, J.M., Rossi, G.: Automatic detection of usability smells in web applications. International Journal of Human-Computer Studies 97, 129–148 (2017)
• Mathur, A., Kshirsagar, M., Mayer, J.: What makes a dark pattern... dark? Design attributes, normative considerations, and measurement methods. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. pp. 1–18 (2021

Questions:

• Create a corpus of Customer Service Chatbots that you analyze on certain criteria. The corpus should include at least 70 different chatbots. For this you will get access to a gitlab repository in which you can save your results. Build a strategy on how to systematically search for these customer service chatbots and describe this methodology.
• The corpus should include at least information about the corporation, it's field, the source, how the chatbot introduces itself, if available information on the trained data, possible access restrictions, information on the user interface, etc.
• Additionally have a look at the code and categorize it. The following questions can be used as an idea for criteria and added on - use the available literature to formulate specific criteria: Is the code openly accessible and readable? Are third party chatbots used? If yes, which ones are used? Are the chatbots included via API-call etc. from the outside? How are they called on in the code?

Literature:

• own research
• Adamopoulou, E., Moussiades, L. (2020). An Overview of Chatbot Technology. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology, vol 584. Springer, Cham. https://doi.org/10.1007/978-3-030-49186-4_31
• Adamopoulou, Eleni, and Lefteris Moussiades. "Chatbots: History, technology, and applications." Machine Learning with Applications 2 (2020): 100006. https://doi.org/10.1016/j.mlwa.2020.100006
• Akma, N., Hafiz, M., Zainal, A., Fairuz, M., Adnan, Z.: Review of chatbots design techniques. Int. J. Comput. Appl. 181, 7–10 (2018). https://doi.org/10.5120/ijca2018917606
• M. Baez, F. Daniel, F. Casati and B. Benatallah, "Chatbot Integration in Few Patterns," in IEEE Internet Computing, vol. 25, no. 3, pp. 52-59, 1 May-June 2021, doi: 10.1109/MIC.2020.3024605.

Questions:

• Intro: Prompt engineering is an emerging discipline for developing and optimizing prompts to efficiently use language models (LLMs) to enable or assist applications and research topics in a wide variety of domains, including web engineering.
• Shortly introduce the basics of prompting.
• Explain and demonstrate common prompting techniques.
• Explain and (if already possible) demonstrate more complex prompting techniques currently used or discussed.
• Review currently available libraries and tools useful for prompt engineers.

Literature:

Questions:

• What is empirical Software Engineering Evaluation and how can it be done?
• Why is evaluation important in the research process?
• What is the difference between a qualitative and quantitative evaluation? (When do you use which one? What are advantages and disadvantages?)
• Prepare a list of evaluation methods and tools that can be used to evaluate software. Explain them and add relevant literature for these methods.
• Demonstrate one quantitative and one qualitative method. For this, find a feasible research question, conduct a survey on it with each of the two methods, compute the results, discuss and present them. You can choose a low level topic on your own that is related to Web Engineering. Use statistical methods to compute the results.

Literature:

• Own research
• Creswell, J. W. (2014). Research design : Qualitative, quantitative, and mixed methods approaches (4. ed., in). SAGE. https://katalog.bibliothek.tu-chemnitz.de/Record/0008891954
• Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., & Wesslén, A. (2012). Experimentation in Software Engineering. In Experimentation in Software Engineering (Vol. 9783642290). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-29044-2
• Chatzigeorgiou, A., Chaikalis, T., Paschalidou, G., Vesyropoulos, N., Georgiadis, C. K., & Stiakakis, E. (2015). A Taxonomy of Evaluation Approaches in Software Engineering. Proceedings of the 7th Balkan Conference on Informatics Conference - BCI ’15, 1–8. https://doi.org/10.1145/2801081.2801084
• Wainer, J., Novoa Barsottini, C. G., Lacerda, D., & Magalhães de Marco, L. R. (2009). Empirical evaluation in Computer Science research published by ACM. Information and Software Technology, 51(6), 1081–1085. https://doi.org/10.1016/j.infsof.2009.01.002

Questions:

• How is a scientific work, especially a thesis in computer science, structured? What sections should a thesis contain and what purpose do they have? Give an overview.
• What is the importance of an introduction? What should it contain? How long should it be? How is it related to the other sections in a scientific work?
• What makes a "good" motivation for your scientific work? Why is it important for the readers? What are methods to write it so the reader can relate to the writer?
• What is the scope of a scientific work? Why is it important? How should you include the scope in the introduction?
• What are current and well known best practices/guidelines/schemes/principles/advices etc.? Which evidence base (e.g. experimental studies) are supporting them? Present them.
• Choose a sufficient scientific work and work out a way to visually represent its whole structure. Show how the introduction, motivation and scope relate to the other parts.

Literature:

• Own research
• Peat, J., Elliott, E., Baur, L., & Keena, V. (2013). Scientific writing: easy when you know how. John Wiley & Sons. DOI:10.1002/9781118708019
• X Barbara Minto: The Pyramid Principle. Pearson Education, 2009.
• Mensh, B., & Kording, K. (2017). Ten simple rules for structuring papers. PLoS computational biology, 13(9), e1005619. DOI: https://doi.org/10.1371/journal.pcbi.1005619
• J. M. Setchell, “Writing a Scientific Report,” in Studying Primates: How to Design, Conduct and Report Primatological Research, Cambridge: Cambridge University Press, 2019, pp. 271–298.
• Blackwell, J., & Martin, J. (2011). A scientific approach to scientific writing. Springer Science & Business Media.
• Williams, J. M., & Bizup, J. (2014). Lessons in clarity and grace. Pearson.
• Oguduvwe, J. I. P. (2013). Nature, Scope and Role of Research Proposal in Scientific Investigations. IOSR Journal Of Humanities And Social Science (IOSR-JHSS), 17(2), 83-87. https://www.iosrjournals.org/iosr-jhss/papers/Vol17-issue2/L01728387.pdf

Questions:

• What guidelines and principles exist to safeguard Good Research Practice?
• How can these guidelines and principles be integrated into the research process?
• What is scientific misconduct/scientific malpractice?
• What is considered "high-quality research"? What are indicators thereof?

Literature:

• Own research
• The European Code of Conduct for Research Integrity http://www.allea.org/wp-content/uploads/2017/03/ALLEA-European-Code-of-Conduct-for-Research-Integrity-2017-1.pdf
• Guidelines for Safeguarding Good Research Practice https://www.dfg.de/download/pdf/foerderung/rechtliche_rahmenbedingungen/gute_wissenschaftliche_praxis/kodex_gwp_en.pdf
• Open Research Data and Data Management Plans https://erc.europa.eu/sites/default/files/document/file/ERC_info_document-Open_Research_Data_and_Data_Management_Plans.pdf

Questions:

• What are Dark Patterns? Where can they be found? How are they defined? Use recent taxonomies to give a short overview.
• What is the current state of the art of Dark Patterns in Online Shopping? Conduct a literature research and present your results: How many publications are available? When were they published? Which methodology was used to define the found Dark Patterns? Which ones were found and how were they categorized?
• Choose 10 well-known online retailers (amazon, temu, ...) and analyze them on their use of Dark Patterns. Create a codebook (can be a table) where you list the retailers, the situations in which you find possible Dark Patterns and how these Dark Patterns could be categorized. For this, first prepare the list of retailers and the situations in which possible Dark Patterns may occur. After that, code this list according to a Dark Patterns taxonomy. Both students should code the samples independently and then meet together with the advisor to discuss the results and unclear cases. Present the consolidated results in a demonstration.
• For the coding regard the following questoins: Is it an established Dark Pattern? Which one? Is it a potential new Dark Pattern? Is information missing for a decision?
• Reflect on the process: What did you learn? Where did you have problems? How could you solve them?

Literature:

• Own research
• Colin M. Gray, Nataliia Bielova, Cristiana Santos, and Thomas Mildner. 2023. An Ontology of Dark Patterns: Foundations, Definitions, and a Structure for Transdisciplinary Action. https://arxiv.org/abs/2309.09640
• Brignull, H.: Deceptive patterns. https://www.deceptive.design
• Gray, C.M., Sanchez Chamorro, L., Obi, I., Duane, J.N.: Mapping the landscape of dark patterns scholarship: A systematic literature review. In: Companion Pub- lication of the 2023 ACM Designing Interactive Systems Conference. pp. 188–193 (2023)
• Mathur, A., Kshirsagar, M., Mayer, J.: What makes a dark pattern... dark? Design attributes, normative considerations, and measurement methods. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. pp. 1–18 (2021
• Arunesh Mathur, Gunes Acar, Michael J. Friedman, Eli Lucherini, Jonathan Mayer, Marshini Chetty, and Arvind Narayanan. 2019. Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 81 (November 2019), 32 pages. https://doi.org/10.1145/3359183
• Sin, R., Harris, T., Nilsson, S., & Beck, T. (2022). Dark patterns in online shopping: do they work and can nudges help mitigate impulse buying? Behavioural Public Policy, 1–27. doi:10.1017/bpp.2022.11
• Voigt, C., Schlögl, S., Groth, A. (2021). Dark Patterns in Online Shopping: of Sneaky Tricks, Perceived Annoyance and Respective Brand Trust. In: Nah, F.FH., Siau, K. (eds) HCI in Business, Government and Organizations. HCII 2021. Lecture Notes in Computer Science(), vol 12783. Springer, Cham. https://doi.org/10.1007/978-3-030-77750-0_10

Questions:

• What is the initial problem to be solved with R2RML?
• How is R2RML different from other mappings, such as RDB2RDF?
• What use cases is R2RML intended for, and what limitations exist?

Literature:

• https://www.w3.org/TR/r2rml/

Questions:

• • What is trustworthy AI? What are the characteristics that need to exist in AI to be trustworthy? How to measure the trustworthiness of AI?
• • Why is trustworthy AI recommended when it is used in the healthcare domain rather than standard AI?
• • Demonstrate the differences between trustworthy and untrustworthy AI by showing an example of both AI’s in chatbots.

Literature:

• https://dl.acm.org/doi/full/10.1145/3555803
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7133476/
• https://link.springer.com/article/10.1007/s44204-023-00063-5
• https://www.sciencedirect.com/science/article/pii/S1532046420302835
• https://dl.acm.org/doi/full/10.1145/3546872
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10651407/

Questions:

• • What is machine unlearning? And in which domains can it be applied?
• • What algorithms are used in machine unlearning? Explain the differences between Machine learning and Machine unlearning.
• • Find a tutorial related to machine unlearning implementation and demonstrate it.

Literature:

• https://arxiv.org/abs/1912.03817
• https://arxiv.org/abs/2108.11577
• https://arxiv.org/abs/2311.02240
• https://arxiv.org/abs/2103.03279
• https://ieeexplore.ieee.org/document/10097553

Questions:

• Introdcution: Prompt engineering involves crafting text in a manner that can be comprehended and processed by a generative AI model. A prompt serves as the natural language description outlining the task to be executed by the AI. This process is integral to effectively instructing AI systems, enhancing their understanding and performance in various applications.
• - An introduction to prompt engineering
• - Best practices in prompt engineering
• - Prompt Engineering with multimodal data, e.g. images, tables, audio, video, web pages, etc. How prompt engineering differs when handled with different data types
• - Experiments on prompt engineering with multimodal data, e.g. images, tables, audio, video, web pages, etc.
• - What are the issues and open challenges in prompt engineering with multimodal data.

Literature:

Questions:

• What is the current state of Web Engineering research? To answer this question, systematically analyze all publications of the 2 venues listed under literature as detailed below. Your primary information used should be the title, authors/affiliations, keywords, and abstract.
• For each publication, capture title, authors/affiliations, keywords, and abstract, venue, year, (for conference papers) name of track/workshop, (for journal articles) volume number, (for journal articles) issue number, (for journal articles) name of issue, page numbers in proceedings/issue, length of the publication, current number of citations of the publication, URL of online resource.
• Based on your raw data collection, analyze the following aspects: 1. What are the main topics of research interest and in which areas of the Web Engineering field, along with the number of publications belonging to them? 2. What authors are publishing in these venues, from which affiliations, from which countries, along with the number of publications for each of these? 3. Which are the most cited articles (relative to their age), which topics/areas receive the most citations, which authors/affiliations/countries receive the most citations? 4. Considering the time dimension, are there any visible trends for aspects 1-3 over the 5 years considered?
• Visualize your data and insights and provide the raw data in re-usable form (CSV).

Literature:

• Venue 1: ICWE Proceedings of last 5 complete years (2019-2023)
• Venue 2: JWE Journals Issues of last 5 complete years (2019-2023)
• For citation counts use: Google Scholar
• Tool for analysis and inspiration for your data visualization: https://www.connectedpapers.com/

Questions:

• Provide an overview of the current state of using Generative AI with Large Language Models such as ChatGPT, Bard etc. as a tool to structure and write scientific texts (workshop/conference papers, journal articles, bachelor/master/phd theses).
• What are existing guidelines/regulations of publishers/universities? How does the usage of Generative AI need to be highlighted in the resulting texts?
• Outline the current discussion on their usage as a tool vs. authorship, intellectual property and quality concerns.
• How will the availability of Large Language Models impact academia in research and in education in the coming years?
• Experiment with a suitable model (e.g. ChatGPT, Bard), using it as a tool for writing different parts of a hypothetical master thesis (situation, motivation, problem analysis, research objectives/questions and scope, requirements, state of the art, solution draft, evaluation/experimentation plan). Ask your supervisor for the specific thesis task. Try different levels of prompts (thesis title only, title and a short description of your own, title and a detailed task description). Observe, which prompts you need, how you improve them iteratively, which ideas you have to provide, and the quality/completeness/suitability of the output.

Literature:

• https://www.nature.com/articles/d41586-023-00107-z
• https://www.acm.org/publications/policies/new-acm-policy-on-authorship
• https://www.frontiersin.org/guidelines/author-guidelines
• https://us.sagepub.com/en-us/nam/chatgpt-and-generative-ai-0
• https://www.jmir.org/2023/1/e51584
• https://www.pnas.org/post/update/pnas-policy-for-chatgpt-generative-ai
• https://dl.acm.org/doi/10.1145/3593013.3594067
• https://provost.harvard.edu/guidelines-using-chatgpt-and-other-generative-ai-tools-harvard
• https://ethics.berkeley.edu/privacy/appropriate-use-chatgpt-and-similar-ai-tools
• https://ctl.utexas.edu/5-things-know-about-chatgpt
• https://www.uni-goettingen.de/de/674738.html
• https://hss-opus.ub.ruhr-uni-bochum.de/opus4/frontdoor/index/index/docId/9734
• https://www.tu.berlin/bzhl/ressourcen-fuer-ihre-lehre/ressourcen-nach-themenbereichen/ki-in-der-hochschullehre
• https://www.scribbr.de/ki-tools-nutzen/chatgpt-universitaere-richtlinien/
• https://www.vkkiwa.de/ki-ressourcen/
• https://digital.uni-hohenheim.de/fileadmin/einrichtungen/digital/Generative_AI_and_ChatGPT_in_Higher_Education.pdf