Effective LLM-based automated program repair (APR) methods can lead to massive cost reductions and have improved significantly in recent times. However, the validity of many APR evaluations as they are conducted at this point is at risk due to data leakage: Prior research has shown that LLMs can memorize solutions to problems if the evaluation benchmark overlaps with the training set, leading to overinflated results.

In this study, we examine the potential of using metamorphic transformations to mitigate the effects of data leakage. For this, we create a variant benchmark for two popular, well-established benchmarks Defects4J and GitBug-Java, and evaluate the APR performance of several LLMs on these benchmarks and their transformed counterparts. In addition, we investigate to what extent our results align with data leakage metrics from other studies.

Our results show that state-of-the-art LLMs for code repair exhibit significant performance degradation (Up to 4.1% for Claude-3.7-Sonnet) on a metamorphically transformed Defecsts4J benchmark. Moreover, we find a significant correlation between our results and the negative log-likelihood as a metric of data leakage. Our results demonstrate the potential of using metamorphic transformations to mitigate the overinflation of evaluation results due to data leakage. We recommend that researchers report results on both original and metamorphically transformed benchmarks in future evaluations.

Reliable communication algorithms have existed for a while that assumed either a global authentication model backed by public key infrastructure or peer-to-peer authentication using shared session keys between pairs of neighboring nodes. Real-life networks, however, do not settle for only one or the other. Instead, they are dynamic, heterogeneous, and often composed of a mix of authentication capabilities across different nodes.

Recent work has introduced hybrid models that combine both authenticated links and authenticated processes to better reflect these real-world conditions. These models aim to preserve the strong correctness guarantees of Byzantine-resilient protocols while reducing their communication overhead and improving practical deployability.

This thesis builds on these ideas by introducing DualRC, a reliable broadcast protocol that explicitly supports hybrid authentication environments. It also presents a novel routed version of the protocol designed to shift complexity away from intermediate nodes and toward the sender and receiver, thereby improving scalability and efficiency. Alongside the theoretical contribution, this work includes the first implementation of both variants of DualRC and evaluates their performance across a range of network conditions, trust assumptions, and deployment scenarios.

The goal of this thesis is to demonstrate that reliable communication in partially trusted networks is not only possible but can be efficient, practical, and scalable—provided that protocol design embraces the diversity and complexity of modern distributed systems.

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.

In this project, the topic of collaboration in computer science education was researched, specifically regarding the correlation between prior programming experience and task distribution in student software development projects. Students selfreported prior programming experience, including their years of study, years of coding experience, their experience rank from 1-10, and their experience compared to their classmates via a survey. A thematic analysis of transcriptions of weekly sprint meetings was then conducted to capture students’ reasoning behind task allocation. Additionally, a quantitative analysis correlated self-reported experience levels with Git metrics including task types (taken from tags), task difficulty (weight), and task reassignment frequency (assignees). While the quantitative analyses found no significant correlation between prior experience and task distribution, the thematic analysis found that students often cited prior experience, in addition to personal interest and time constraints as common reasons for task distribution decisions. These findings suggest that task allocation is affected by multiple factors. While prior experience may have an influence, other factors are likely more impactful.

Cyclomatic complexity often serves as a proxy for code quality. While it has been previously studied within education, its relationship with prior programming experience remains unclear. This study analyzed the correlation between different experience metrics, including total years of programming experience, years of experience in industry and in academia, and cyclomatic complexity in student projects. A thematic analysis of task difficulty further contextualized the findings. Results indicated mostly no significant correlations between prior experience and complexity metrics, with only two significant correlations found. While total years of experience and experience in industry showed to be negatively correlated, experience gained in academia showed no consistent findings. The findings suggest that experience alone does not explain complexity scores, indicating that when used as an evaluation method in collaborative projects the results may be influenced by contextual factors such as project settings and task types

Malvertising is a significant threat, in which attackers leverage online advertisements to deceive users and distribute scams, phishing pages, and malware. While prior research has largely focused on low-tier ad networks and high-risk websites, this study examines brand impersonation in mainstream search advertising platforms, specifically Google Ads.

We queried Google with brand-related search terms, capturing and analyzing the advertisements displayed to assess the scale and nature of impersonation. Over a 24-day period, our scraper collected a dataset of 52k ads across 605 brands, extracting key features such as advertiser identity, redirection chains, and landing page content.

Using a combination of manual inspection and six brand-agnostic heuristics, we identify various forms of abuse, including phishing pages, tech support scams, and a previously undocumented category, affiliate brand bidding. This last technique, in which affiliates place search ads to divert users through affiliate links, affects at least 189 brands in our dataset.

In total, 4,160 ads (7.9%) were flagged as abusive, 3781 of which involved affiliate brand bidding. Our results further reveal that verified Google Ads accounts are being rented or resold, enabling systematic evasion of identity checks. These findings expose enforcement gaps in Google’s ad review and verification systems.