Large Language Models (LLMs) have shown impressive performance in various domains, including software engineering. Code generation, a crucial aspect of software development, has seen significant improvements with the integration of AI tools. While existing LLMs have show very good performance in generating code for everyday tasks, their application in industrial settings and domain-specific contexts remains largely unexplored. This thesis investigates the potential of LLMs to generate code in proprietary, domain-specific environments, with a specific focus on the leveling department at ASML. The primary goal of this research is to assess the ability of LLMs to adapt to a domain they have not encountered before and to generate complex, interdependent code in a domain-specific repository. This involves evaluating the performance of LLMs in generating code that meets the specific requirements of ASML. To achieve this, the thesis investigates various prompting techniques, compares the performance of generic and code-specific LLMs, and examines the impact of model size on code generation capabilities. To evaluate the code generation capabilities of LLMs in repository-level scenarios, we introduce a new performance metric, build@k, designed to measure the effectiveness of generated code in compiling and building projects. The results showed that both prompting techniques and model size have a substantial influence on the code generation capabilities of LLMs. However, the performance difference between code-specific and generic LLMs was less pronounced and varied substantially across different model families.

Recommender systems are an essential part of online businesses in today's day and age. They provide users with meaningful recommendations for items and products. A frequently occurring problem in recommender systems is known as the long-tail problem. It refers to a situation in which a majority of the items in the data set have limited ratings due to which many recommender systems, especially collaborative filtering based methods, are not able to recommend these items, also known as long-tail items. Although popular items are easier to recommend, it has been noticed that long-tail items often generate a significant fraction of the revenue and therefore should also be recommended to users. This paper proposes a modified version of a collaborative filtering based recommender system aimed to reduce the effects of the long-tail recommendation problem (LTRP). The algorithm first splits the data set into the head H and the tail T and clusters the items from the tail. The average rating avg for each cluster is calculated and for all users and their unrated long-tail items, the rating for that item is set to avg with a probability of p. Now the standard collaborative filtering algorithm is run with the newly inserted ratings. The inserted ratings reduce the sparsity of the data set and therefore make it easier to recommend long-tail items. Empirical experiments on the 100K MovieLens data set indicate that the proposed algorithm recommends more long-tail items than the standard collaborative filtering algorithm, thus reducing the effects of the LTRP while maintaining the same or a slightly lower accuracy of the recommender system.