This thesis investigates the gap between the theoretical ideals and practical realities of distributed exchange protocols in peer-to-peer sharing economies. While classical economic models and stylised trading mechanisms have been extensively studied, their assumptions often overlook the complexities and behavioural nuances of real-world agent-based markets. Conversely, more empirically grounded approaches tend to be highly case-specific, limiting their generalisability and disconnecting them from established economic concepts.
The central aim of this work is to develop and analyse an algorithmic economic model that more faithfully captures the dynamics of decentralised resource sharing — balancing fairness, efficiency, and resistance to manipulation, while retaining a useful level of abstraction.
In light of this purpose, the thesis performs a comparative theoretical analysis of centralised and distributed exchange mechanisms, and introduces a novel exchange market model that incorporates heterogeneous strategies, bounded rationality, and asynchronous timing. A series of numerical experiments evaluates the performance and robustness of different protocols under these features of sharing economies.
The results show that distributed, mixed-strategy protocols can achieve stable and desirable outcomes, however their success is sensitive to population diversity, limited information, and strategic behaviour. These findings highlight the importance of integrating behavioural aspects into protocol design, and provide insights towards building more robust models for the sharing economy.

One of our first observations working on this study was that all teams used their own form of Agile, borrowing elements from frameworks like Scrum, but ultimately adapting their collaboration methodology to fit their own projects and preferences. This led 2 of the 3 teams to have a mix of both technical and organisational subjects planned for discussion during meetings.

One of the groups went a step further and employed regular 2--7 hours long in-person working sessions (including breaks). The first hour of one of these sessions was recorded and analysed, since it was in preparation for an upcoming TA meeting and consisted predominantly of organisational discussions akin to a daily Scrum meeting. This made it harder for us to interpret their recording, as many of their discussion threads had started before and ended after our observation periods.

Computer Science education, particularly at the beginner level, often presents challenges due to vague and unhelpful error messages. This problem is particularly significant for students with low self-efficacy, leading to hindered learning experiences. Large Language Models (LLMs) offer a promising solution by generating more comprehensible and supportive error messages. This study aims to assess whether the rewriting of error messages using LLMs can improve self-efficacy among high school students, focusing on self-efficacy and study success as indicators of improved learning experiences. Through in-class experiments with 32 participants, the findings revealed that LLM rewritten error messages, although consistent with existing research, did not produce statistically significant effects. Therefore, more research is needed to evaluate their impact on learning outcomes and explore the most effective types of prompt. This research contributes to understanding the role of LLMs in educational settings, providing empirical insights into their effectiveness in real-world scenarios.

While artificial intelligence (AI) has undeniably ushered numerous solutions across various fields, the growing belief that AI can solve all problems overshadows their lack of transparency that comes along. Understanding how decisions are made and what has led to the output is crucial in critical systems to ensure accountability and trust.

This research proposes a complementary method leveraging inter-host distances that localise the outlying hosts, logs and the time frames, which require more advanced analysis. By relying on a variant of a prominent statistical method in the field of authorship attribution - Burrows Delta - the approach enhances transparency in identifying deviating hosts, logs and time frames. Hence, the proposed solution offers an understandable complementary method that preserves integrability by being a log-based method while enabling understandable pinpointing of the specific hosts, logs and time frames that warrant further advanced analysis. By providing insights into the behaviour of the hosts over time, a temporal summarisation for security analysts is provided, relaxing their need to go through all the log files to understand the hosts' behaviour.

The results show that a complementary method based on the textual content of the metadata of the logs provides alternative insight into the activities of the hosts than the attributes. Moreover, the behaviour defined by the proposed method requires less extensive lookup than the behaviour defined by attributes. The inter-host distances based on the textual content allow understandable localisation of the host behaviour over time. Hence, this research provides an understandable method that will summarise the behaviour of the hosts over time, which enables the localisation of the logs requiring more advanced, in-depth analysis, and thereby reducing the amount of logs security analists need to consider during a compromise.

Within the field of computer science (CS), women are under-represented in the workforce and education settings. As Massive Open Online Courses (MOOCs) grow in popularity, understanding the gender differences in reasons for enrolment and engagement remains crucial to improving learner outcomes. This study investigates why men and women enrol in introductory CS MOOCs and how they interact with these courses. This is done with data from four MOOCs offered by TU Delft between 2015 and 2022.

Using survey data for the learners' reasons for enrolment and clickstream data for their behavioural engagement, we applied k-means clustering to identify engagement patterns. Our analysis reveals that the three most important reasons for men and women are career-related, interest-related, and degree-related, in that order. Women are more likely to enrol for career-related reasons than men, while men are more driven by interest in the topic than women. Women also tend to show lower engagement levels compared to men, who are more likely to complete the courses. We found no significant association between reasons for enrolment and engagement for men and women.

These findings highlight the need for gender-sensitive course design strategies to enhance engagement and completion rates. Providing mentorship opportunities, fostering peer interaction platforms, and highlighting role models in the field could also help create a more inclusive learning environment. Future research should explore specific learner challenges and incorporate a more comprehensive engagement model.

Projects in a software engineering (SE) curriculum have many advantages for students. For example, students gain teamwork and project management skills. Involving an external stakeholder can enhance the learning experience further. For instance, students have the opportunity to practice stakeholder management. Moreover, working on a project with a real external stakeholder can help them make informed career choices. This external stakeholder can be a company, a government institution, or an organization from civil society, called ‘client’ from here onwards. In this work, we focus on startups to investigate the collaboration between startups and students in SE projects. Furthermore, we developed and introduced a design thinking workshop.
We did the research in a project course at Delft University of Technology. It is the capstone course near the end of the major in Computer Science & Engineering. In this course, students take on a project for a ‘client’. This external stakeholder brings in the project’s objective. Every student group has a unique project from different clients.
We started by analysing these projects from the client’s perspective. We interviewed several star- tups that previously participated in the course. Additionally, we sent a survey to all clients from the previous two years. We incorporated the findings of the interviews and survey with literature. From this, we concluded that startups potentially suffer from a lack of design upfront. This can lead to the creation of an undesirable product. To address this, we developed and introduced a design thinking workshop to our course. Design Thinking is a method for problem analysis and problem-solving. We evaluated the workshop by interviewing teaching assistants who supervised student groups that had participated in the workshop. Additionally, we surveyed the students who participated in the workshop and teaching assistants who joined a test run of the workshop.
From the results of the interviews with startups and survey of previous clients, we synthesized di- mensions of success for projects with students. We found three dimensions in which startups measure the success of a project. The output of a project can be important to them in terms of the 1) product that is developed or what was 2) learned during the project. Additionally, whether students are successfully 3) recruited after the project can also be a consideration when assessing success. Furthermore, we synthesized dimensions of product success and preconditions to success in these projects. We found five dimensions of product success and five preconditions to success.
The design thinking workshop was given to students in the first week they started their project. It constituted half a day during which the students learned to apply the Design Sprint method, a method for operationalizing design thinking in a structured way.
After the projects ended, we evaluated the impact of the workshop. The students and their teaching assistants were both generally positive about it. The teaching assistants, who also supervised non- workshop groups, noticed an improved early process. Furthermore, they noticed that some of their non-workshop groups fell into traps that could have possibly been prevented if the group had joined the workshop and applied the tools. This leads us to believe that the workshop should be extended to all students in the course.
Implication for startups   Spend time thoroughly understanding the problem before engineering a costly and time-consuming prototype. A design sprint is a low-cost way to discover if the problem is sufficiently understood.
Implications for companies involved in SE project courses   If properly motivated, students can create a useful product or prototype, the project does not have to be just a recruitment exercise.
Implications for teachers (of SE project courses with external stakeholders)   Add a design sprint workshop for all students at the start of the course. This thesis shows that adding such a workshop is feasible and improves students’ early process.
Implications for students  Do a design sprint before formulating requirements; this will deepen the understanding of the problem to solve and improve the process, so that it yields a product that is more aligned with the user’s needs.
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Group work is an integral part of Computing Education, but introduces problems when students either fail to participate enough or dominate, not allowing their team members to contribute. This research focuses on identifying the participation level of the students, allowing the teacher or teaching assistant who monitors the group throughout the project to identify and resolve problems. The software metrics that can be extracted from the code repository are often used in this monitoring process. Therefore, this study investigates the correlation between the software metrics and the participation level. Furthermore, this study aims to understand the correlation between programming experience and the participation level in the project.

To this end, four project groups were observed over several weeks, followed by interviews with their teaching assistants. A questionnaire assessed each student’s programming experience and collected data about confounding factors. A selection of software metrics to collect was made based on the literature and the teaching assistant interviews. Per contributor, we collected and analysed the number of lines of code, amount of issue comments, amount of MR comments, amount of commits, cumulative cyclomatic complexity, maximum cyclomatic complexity, amount of pipelines triggered, and pipeline failure ratio.

Of the twenty students in the observed groups, eight were classified as high participation, and six were identified as low participation. No significant correlation was found between participation level and gender, age, nationality, GPA, or group familiarity. Programming experience also did not predict participation, suggesting that a lack of experience is not a valid reason for low engagement. Furthermore, the number of lines of code students wrote, the number of commits students made, and the number of comments that students posted on merge requests are correlated with the participation level. This means these metrics can be used for monitoring student software groups and can indicate more than just code contribution.

CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) have been in use for a long time on the web to block bots from accessing services. Many Different types of CAPTCHAs exist in various shapes and forms. As traditional CAPTCHAs became increasingly susceptible to attacks, mainly with the rise of artificial intelligence, there were efforts to enhance their complexity. As a side effect, this also increased the difficulty for legitimate users. Then attackers improved their methods, and the CAPTCHAs were broken once again, while the regular user keeps getting harder challenges. Over the years, this cycle has continued, leading to today’s CAPTCHAs, which are not only insecure but also difficult to solve for regular users, defeating the purpose of having a CAPTCHA in the first place. What makes AI attacks so successful in CAPTHCA systems is their ability to perform the given task with high accuracy. AI bots are now faster and more successful in solving CAPTCHAs than humans.

In this paper, we propose SHAPECAP (Shape Analysis and Precision Exploiting CAPTCHA), a novel interactive CAPTCHA system that aims for a user-friendly solution that is also secure against AI-backed attackers. The design of our solution involves a canvas on which shapes are moving randomly. The user can choose a specific shape at the start of the challenge and use the mouse pointer to follow it. There will be small variations of the chosen shape in order to confuse the user. If the user follows the correct shape, they will complete the challenge faster, while following the variation shapes will take longer. Based on the mouse movement data collected throughout the challenge, we can confirm whether the user is a human or a bot. Our aim is to find a pattern that shows humans are more likely to follow irregular shapes. We exploit the precision of machines and use it against them while capitalising on the natural tendency of humans to make errors.

Continuous integration (CI) is a software engineering practice that promotes frequent code integration into a shared repository, improving the productivity within development teams as well as the quality of the software being developed. While CI adoption has gained traction, studies have examined its effective implementation and associated challenges. The idea that multiple contextual factors influence the adoption of CI prompts an exploration of suitable descriptive metrics for describing the CI practices employed. This paper aims to explore the metrics that best depict the level of maturity of a project, addressing the question: "What metrics can be used to describe the maturity level of a project?". With a lack of a comprehensive maturity framework, we leverage GitHub's API in an attempt to analyze various metrics to be used to create a framework for filtering projects.
Our findings indicate that project maturity cannot be captured by a single metric, but rather a combination of metrics reflecting different aspects throughout the project's lifecycle. Activity levels, including commits and pull requests, popularity indicators like stargazers, forks, and contributors, as well as repository size and age, emerge as primary indicators of maturity. By combining these metrics, a unified framework for categorizing mature projects can be established and further developed.

While continuous integration has already been proven to positively affect software development, little is known about how it should be implemented based on project context. This paper investigates how CI pipelines are configured by analysing data mined from software projects on GitHub. This re- search has shown the continued rise of the CI plat- form GitHub Actions, which enables developers to broaden CI pipelines’ functionality due to great in- tegration into GitHub. Moreover, key differences between how jobs within pipelines are structured in Travis CI and GitHub Actions are outlined. These results can be used in future research, which will be aimed at connecting project context to CI setup with the goal of informing developers on maturing their CI configuration.

The Continuous Integration (CI) practice, has been rapidly growing and developing ever since it's introduction. This practice has been constantly providing benefits to developers such as early bug detection and feedback to development teams. In this study, we aim to identify the descriptive metrics that best illustrate the performance of the CI build stage, regarded as heart of the development process.

We conduct a small case study on repositories utilizing GitHub Actions, a CI tool that is relatively unexplored. Within this context, we classify projects using two performance indicators: build breakages and build durations. We examine two distinct sets of metrics in our analysis. The first set being build level metrics, which are closely linked to the build stage. The second set including project level metrics.

Our findings suggest that patterns traditionally associated with low breakages and durations are applicable to repositories employing GitHub Actions. However, understanding the relationship between project level metrics demands a more comprehensive approach, necessitating a thorough analysis of the project context for a holistic understanding of build performance.

Continuous Integration (CI) is a software development technique that enhances software quality and development efficiency, but its implementation usually depends on the project's context. This creates an opportunity for studying real-world CI projects on GitHub, focusing on their CI metrics and best practices. In this paper, we explore various methods to extract the topics from CI software projects on GitHub. This data can then be used to group projects and facilitate an in-depth analysis within specific contexts and application domains, such as CI build success rates in machine learning or React Native projects. We explore the definition of a software topic, as it shows significant granularity variations in related studies. We examine existing tools and other potential topic modeling approaches, compare varying types of textual data from GitHub that could be used as inputs for these tools, and report on interesting insights from initial trials with the developed tool. Our research led us to use GitHub topic labels as topic definitions due to their relevance and prior research focus. We also evaluated three topic extraction tools - LASCAD, a Multi-label Linear Regression classifier, and ChatGPT - incorporating the last two into our CI project mining tool. Additionally, we included two tool-independent approaches: GitHub's search function with the ability to filter repositories by topics and existing project topic labels. Lastly, to test the practicality of the tool, we mined 4899 public repositories and briefly investigated workflow metrics of projects grouped into six arbitrary topics.