Automated test generation is a critical area of research in software engineering, aiming to reduce manual effort while improving software reliability. While substantial work has focused on statically typed languages, dynamically typed languages such as JavaScript remain underexplored despite their widespread use and unique challenges. This thesis investigates the current status of JavaScript test generation by systematically evaluating state-of-the-art search-based and large language model-based tools.

We first analyze existing benchmarks to assess their coverage of representative language features, identifying gaps that limit the ability to fairly compare tool performance. We then construct a curated dataset of real-world JavaScript projects and evaluate the LLM-based tool TestPilot and the search-based tool SynTest using a combination of quantitative metrics (e.g., code coverage, pass rates) and feature based correlation analysis. Our results reveal that TestPilot tends to generate higher coverage (median 27.9\% vs 11.2\% branch coverage) and more readable tests but produces a larger number of failing or low-value test cases, while SynTest generates more stable and focused test suites yet can struggle with complex or dynamic code constructs. Our similarity analysis shows that each approach achieved unique coverage, suggesting complementary strengths.

This study highlights the need for standardized, language-aware benchmarks and introduces a curated dataset and evaluation framework for evaluating JavaScript test generation tools. By systematically comparing search-based and LLM-based approaches, this thesis offers insights into their respective strengths, limitations, and opportunities for hybrid strategies, advancing the state of automated testing for dynamically typed languages.

In large-scale engineering environments, efficient issue tracking is essential for timely problem resolution and knowledge reuse. However, manual classification and association of issue reports present scalability challenges, further complicated by inconsistent annotations and the absence of semantic linking mechanisms. This project investigates the application of Natural Language Processing and Artificial Intelligence to automate multi-label classification and discover meaningful semantic associations between technical issues. Over 70 model configurations were evaluated on a real-world industrial dataset, comparing classical models with transformer-based and deep learning approaches. DistilBERT achieved the highest Recall@5 (0.93), indicating strong performance in identifying relevant categories. Classical methods, such as TF-IDF combined with Logistic Regression, also performed well, offering a computationally efficient and interpretable option. For association discovery, approaches including lexical retrieval, embedding-based similarity, clustering-based filtering, and topic modelling were assessed using both quantitative metrics and expert review. Lexical (BM25) and embedding-based (SBERT + Cosine Similarity) methods offer complementary strengths, retrieving overlapping yet distinct sets of associations. Associations identified by both models were rated as useful in over 70% of cases by domain experts, suggesting that agreement between methods may serve as an indicator of relevance. While Copilot provided consistent relevance assessments, its ratings were often higher than those provided by human evaluators and did not always reflect their detailed assessments. These findings highlight the potential of combining lexical and semantic methods with human-in-the-loop validation to support scalable and accurate industrial applicability.

Federated Space Public Key Infrastructure (PKI) can offer a scalable foundation for secure and interoperable communications in collaborative space missions. Yet, its deployment faces challenges stemming from resource-constrained assets, architectural complexity, and the transition to post-quantum (PQ) cryptography. Current CCSDS space guidelines rely on the Internet X.509 profile, whose extensive feature set—if left unrestricted—can increase implementation complexity, certificate size (especially under PQ algorithms), and the risk of interoperability issues. In parallel, the IETF C509 Certificates draft emerges as a streamlined subset of X.509 with a compact encoding specifically tailored for constrained environments. This paper provides an empirical comparison between X.509 and C509 to inform space mission designers about the associated advantages and costs of each, specifically when PQ cryptography is incorporated into space PKIs. To help pave the way for interoperability in federated space missions, a minimal certificate profile for space PKI is proposed.

In addition, the work introduces the first open-source native C509 toolkit that supports PQ algorithms and evaluates open-source and proprietary certificate parsers. While the IETF C509 draft proposal reports a size reduction of over 50%, our evaluation confirms approximately 40% savings for classical certificates generated according to our proposed minimal certificate profile. For PQ certificates, the savings plateau at around 200 bytes, rendering the size gains negligible. However, revocation lists consistently achieve a 60% reduction for 30,000 entries, independent of the cryptographic scheme (PQ or traditional). To quantify and compare the software implementation complexity of X.509 and C509, we conduct software complexity analysis using well-established heuristic metrics (e.g., cyclomatic complexity, Halstead metrics, logical lines of code). The findings further highlight the relative simplicity of the C509 parser implementation in software. Defining a standardised certificate profile for federated space would advance interoperability; however, adopting C509 requires carefully balancing modest PQ size savings against software simplification and the uncertainties associated with a draft standard.

Probabilistic programming offers an intuitive and expressive way to define statistical models, rendering it particularly effective in modeling problems where uncertainty plays a crucial role.
As adoption increases and models become more expressive, the challenge of effective inference becomes increasingly pronounced.
Effective inference often requires tailoring algorithms to the structure of the underlying model. While many probabilistic programming systems allow users to implement custom inference strategies via programmable inference, this process remains largely manual and heavily reliant on domain-specific expertise, particularly for sampling-based methods.
This paper investigates the use of Satisfiability Modulo Theories (SMT) to automate the generation of tailored, observation-aware proposals for guiding inference within Metropolis-Hastings in Gen, a probabilistic programming system. By reformulating the search for high-likelihood traces as a constraint optimization problem, this work explores whether SMT-based solutions can improve proposal quality and convergence. Empirical results indicate that SMT-derived traces offer a promising starting point for inference but are less effective as an active search heuristic. These findings suggest a new direction for automated, structure-aware proposal generation in probabilistic programming.