Computer Science Dissertation Topics for UK Students

Computer Science Dissertation Topics for UK Students

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Selecting the Right Dissertation Topic: An Introduction

Selecting the right dissertation topic is one of the most critical decisions you’ll make as a Computer Science student in the UK. The pressure to choose something original, achievable, and academically rigorous can feel overwhelming, especially when you’re already juggling coursework, exams, and other commitments. Your dissertation topic forms the foundation of months of research, experimentation, and writing—making it essential to choose wisely.

The importance of selecting an appropriate computer science dissertation topic cannot be overstated. A well-chosen topic not only aligns with your academic interests and career aspirations but also demonstrates your ability to contribute meaningfully to your field. UK universities expect dissertation research to reflect current technological trends, address real-world problems, and showcase your capacity for independent scholarly inquiry. Whether you’re pursuing an undergraduate dissertation or postgraduate research, your topic should be specific enough to guide your research while remaining broad enough to allow for meaningful exploration and analysis.

Your dissertation represents a significant investment of time, effort, and intellectual energy. The topic you select will shape your academic experience during this crucial period, influencing which papers you read, which methodologies you employ, which tools you master, and ultimately, which insights you contribute to your field. This is why careful, deliberate topic selection deserves your full attention and consideration.

The Contemporary Computer Science Landscape in 2026

In 2026, the computer science landscape continues to evolve rapidly. Artificial intelligence and machine learning have moved from emerging technologies to essential components of modern software systems. Cybersecurity threats are becoming increasingly sophisticated, demanding innovative defensive strategies. Cloud computing architectures are reshaping how organisations store and process data. Blockchain technologies are revolutionising data integrity and security. Meanwhile, ethical considerations in technology development have never been more important.

The convergence of these technologies creates unprecedented opportunities for research and innovation. Machine learning algorithms now power everything from medical diagnostics to financial forecasting. Cloud-native architectures enable organisations to scale dynamically while maintaining security and compliance. Distributed systems require new approaches to consistency, availability, and fault tolerance. Edge computing brings computational power closer to data sources, reducing latency and enabling real-time applications.

For your dissertation, this dynamic landscape offers rich territory for exploration. The technologies transforming industry today represent the ideal subjects for academic research that has both theoretical depth and practical relevance. Your dissertation topic should reflect these contemporary concerns while demonstrating your technical expertise and research capability. By engaging with current challenges and emerging technologies, you position yourself at the forefront of your field and create research that matters beyond the academic institution.

How to Choose the Right Computer Science Dissertation Topic

Selecting your dissertation topic is a strategic decision that deserves careful consideration. Rather than rushing into the first interesting idea you encounter, take time to evaluate potential topics against several practical factors:

Align with Your Interests

Choose a topic that genuinely excites you—you’ll be researching it intensively for months, so passion matters considerably. Consider the areas of computer science that have engaged you most during your studies. Did you particularly enjoy modules on machine learning, security, distributed systems, or software engineering? Your dissertation is an opportunity to dive deep into areas that intrigue you, so leverage this opportunity to explore subjects you find genuinely interesting rather than selecting topics merely because they seem trendy.

Consider Available Resources

Ensure you have access to necessary datasets, software, tools, and literature to support your research. Some topics require substantial computational resources, specialised software, or access to proprietary datasets. Before committing to a topic, verify that your institution provides or can help you access required resources. Consider whether you can work with open-source tools and publicly available datasets, or whether you’ll need institutional support for hardware, software licenses, or data access agreements.

Check Supervisor Expertise

Your supervisor should have relevant expertise in your chosen area, providing guidance and support throughout your research journey. A supervisor familiar with your topic can offer invaluable direction, suggest methodological approaches, recommend relevant literature, and help you navigate challenges that arise. Before finalising your topic choice, discuss your ideas with potential supervisors to ensure they have sufficient expertise and availability to support your work effectively.

Balance Novelty and Feasibility

Aim for topics that offer original contributions without being so ambitious that they exceed typical dissertation scope. A groundbreaking topic that requires resources beyond your reach or timescale beyond your dissertation period will only frustrate you. Similarly, a topic with minimal novelty won’t demonstrate the advanced scholarly thinking your institution expects. Seek the middle ground: research questions that are genuinely interesting, somewhat novel within your context, but achievable within your constraints.

Review Current Literature

Investigate what research already exists in your area to identify genuine gaps where your work can add value. A thorough literature review reveals what other researchers have explored, which questions remain unanswered, and which methodological approaches have proven effective. This investigation helps you identify research gaps—areas where little work has been done or where existing research has limitations your work could address. You’ll also discover which research questions are well-trodden and which offer fresher territory for investigation.

30 Computer Science Dissertation Topics for 2026

1. Machine Learning Algorithms for Early Detection of Cybersecurity Threats in Enterprise Network Systems

This research explores how supervised and unsupervised machine learning techniques can identify anomalous network behaviour indicative of cybersecurity threats before they escalate. Your dissertation would investigate which algorithms (random forests, neural networks, isolation forests) most effectively detect intrusion attempts, malware propagation, and data exfiltration. You could develop models trained on network traffic datasets, compare their performance against traditional rule-based detection systems, and evaluate their effectiveness in enterprise environments. This topic combines contemporary security concerns with machine learning applications, offering clear practical value.

2. Natural Language Processing Applications in Automated Customer Service Systems for Healthcare Organisations

This dissertation investigates NLP algorithms’ effectiveness in understanding patient enquiries, generating appropriate responses, and improving satisfaction in healthcare customer service contexts. You would develop or improve chatbot systems using transformer models, evaluate their accuracy in interpreting medical questions, and measure patient satisfaction improvements. The healthcare context adds ethical considerations around safety and accuracy, enriching your research. This topic demonstrates how NLP addresses real organisational challenges while maintaining necessary precision in sensitive domains.

3. Blockchain-Based Solutions for Secure Medical Record Management in the United Kingdom National Health Service

This study examines how distributed ledger technology can ensure data integrity, patient privacy, and interoperability across NHS healthcare providers while maintaining regulatory compliance. Your research would explore blockchain architectures suitable for healthcare data management, evaluate their performance and scalability, and address regulatory requirements (GDPR, NHS standards). This topic situates emerging technology within institutional constraints and regulatory frameworks, demonstrating sophisticated understanding of how technology must adapt to real-world contexts.

4. Deep Learning Models for Predictive Maintenance of Industrial IoT Devices in Manufacturing Environments

This research evaluates neural network architectures’ ability to predict equipment failures by analysing sensor data, reducing downtime and maintenance costs in manufacturing operations. You would develop deep learning models trained on IoT sensor streams, validate their predictive accuracy, and demonstrate cost savings from reduced unexpected failures. This application-focused topic shows how machine learning creates tangible business value while addressing substantial technical challenges in handling real-time sensor data at scale.

5. Federated Learning Approaches for Privacy-Preserving Data Analysis Across Multiple Financial Institutions

This dissertation explores how federated learning enables collaborative machine learning without centralising sensitive financial data, addressing privacy and regulatory concerns across banking sectors. Your research would investigate federated learning frameworks, evaluate their effectiveness for collaborative model training, and measure privacy preservation against attack scenarios. This topic combines machine learning innovation with practical privacy concerns, showing how technical solutions address regulatory requirements in regulated industries.

6. Software Engineering Practices for Developing Secure Cloud-Native Applications in Multi-Tenant Environments

This study investigates architectural patterns, containerisation strategies, and deployment methodologies that maintain security and isolation in cloud-native application development across UK enterprises. Your dissertation would examine secure microservices patterns, container security practices, and isolation mechanisms. This topic bridges software engineering with security considerations, showing how development practices must evolve to address challenges posed by cloud-native architectures.

7. Explainable Artificial Intelligence Frameworks for Building Trust in Autonomous Decision-Making Systems

This research examines interpretability techniques that make AI decision processes transparent to stakeholders, particularly critical for high-stakes applications in healthcare, finance, and criminal justice. Your work would investigate SHAP values, LIME, attention mechanisms, and other explainability techniques, evaluating how effectively they communicate model reasoning to non-technical stakeholders. This topic addresses crucial concerns around AI transparency and accountability, reflecting growing emphasis on responsible AI development.

8. Quantum Computing Applications for Optimising Vehicle Routing Problems in Logistics and Supply Chain Networks

This dissertation evaluates quantum algorithms’ potential to solve complex combinatorial optimisation problems more efficiently than classical approaches in modern logistics operations. You would investigate quantum algorithms for vehicle routing, compare their theoretical advantages with classical approaches, and explore near-term quantum hardware capabilities. This forward-looking topic demonstrates engagement with emerging computing paradigms while addressing practical optimisation challenges.

9. Computer Vision Systems for Real-Time Detection of Manufacturing Defects Using Convolutional Neural Networks

This study develops and tests CNN architectures trained on manufacturing defect datasets to identify quality issues faster and more accurately than traditional manual inspection methods. Your research would involve dataset creation or curation, CNN architecture design and optimisation, and real-time deployment considerations. This application-focused topic demonstrates how computer vision creates measurable improvements in industrial processes.

10. Edge Computing Architectures for Reducing Latency in Real-Time Internet of Things Applications

This research compares edge computing deployment strategies with traditional cloud computing to demonstrate latency improvements in time-sensitive IoT applications. Your dissertation would evaluate edge computing frameworks, measure latency reductions across various application scenarios, and analyse trade-offs between edge and cloud approaches. This infrastructure-focused topic shows how architectural decisions fundamentally impact application performance.

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11. Adversarial Attack Detection and Mitigation Strategies for Deep Neural Networks in Critical Systems

This dissertation investigates methods for identifying adversarial examples, understanding attack mechanisms, and implementing defences to protect neural networks from malicious manipulation. Your research would examine adversarial attacks against neural networks, develop detection mechanisms, and evaluate defensive strategies. This security-focused topic addresses critical vulnerabilities in AI systems, particularly important for safety-critical applications.

12. Microservices Architecture Patterns for Scalable and Resilient Application Development in DevOps Environments

This study examines containerisation, service discovery, and orchestration patterns that enable development teams to build scalable, maintainable applications using microservices approaches. Your dissertation would investigate microservices patterns, evaluate their effectiveness for different application types, and measure impacts on development velocity and system reliability. This architectural topic shows how design patterns influence both development processes and system characteristics.

13. Privacy-Preserving Data Mining Techniques for Extracting Insights from Sensitive Consumer Datasets

This research explores differential privacy, homomorphic encryption, and anonymisation methods that enable meaningful data analysis while protecting individual privacy in commercial contexts. Your work would implement privacy-preserving techniques, measure their effectiveness for protecting privacy while maintaining analytical utility, and evaluate their practical applicability. This topic addresses fundamental tensions between data utility and privacy protection.

14. Graph Neural Networks for Social Network Analysis and Community Detection in Large-Scale Online Platforms

This dissertation develops and tests GNN architectures to identify community structures, predict network evolution, and understand interaction patterns in large social networks. Your research would implement graph neural network models, train them on social network datasets, and evaluate their effectiveness for community detection and network analysis. This topic demonstrates how specialised neural network architectures address specific data structures effectively.

15. Reinforcement Learning Applications for Autonomous Vehicle Navigation and Decision-Making in Urban Environments

This study implements RL algorithms that enable vehicles to learn optimal driving strategies through simulated and real-world interactions while ensuring passenger safety and regulatory compliance. Your dissertation would develop reinforcement learning models for autonomous navigation, validate safety properties, and evaluate their effectiveness in complex urban scenarios. This ambitious topic combines machine learning with critical safety considerations in robotics applications.

16. API Security Vulnerabilities and Protection Mechanisms in Microservice-Based Application Ecosystems

This research identifies common API security weaknesses in microservice architectures and evaluates authentication, rate limiting, and encryption strategies that mitigate exploitation risks. Your work would analyse API vulnerability patterns, implement and test protective mechanisms, and measure their effectiveness against attack scenarios. This security-focused topic shows how traditional security concerns evolve as architectural patterns change.

17. Transfer Learning Applications in Medical Image Analysis for Diagnostic Support in UK Healthcare Settings

This dissertation applies pre-trained neural networks to medical imaging datasets, demonstrating how transfer learning reduces training time while maintaining diagnostic accuracy across healthcare institutions. Your research would implement transfer learning approaches, validate diagnostic accuracy, and evaluate their practical deployment in healthcare settings. This application-focused topic shows how pre-trained models accelerate development while maintaining performance in specialised domains.

18. Containerisation Technologies for Improving Software Development Workflows and Deployment Efficiency in Enterprise Settings

This study measures how containerisation with Docker and Kubernetes optimises development velocity, reduces deployment failures, and improves resource utilisation in enterprise organisations. Your dissertation would measure metrics around deployment frequency, failure rates, and resource efficiency before and after containerisation adoption. This process-focused topic shows how technological choices influence development team effectiveness.

19. Sentiment Analysis Using Transformer Models for Brand Monitoring and Customer Feedback Analysis

This research applies state-of-the-art NLP transformer architectures to analyse customer opinions at scale, enabling businesses to monitor brand perception and identify emerging issues rapidly. Your work would fine-tune transformer models on sentiment analysis tasks, evaluate their effectiveness against baseline approaches, and demonstrate practical applications in brand monitoring. This topic shows how advanced NLP techniques address business intelligence challenges.

20. Distributed Systems Consistency Models and Their Impact on Database Performance and Reliability

This dissertation evaluates different consistency guarantees (strong, eventual, causal) in distributed databases, measuring trade-offs between performance, availability, and data correctness. Your research would implement or analyse distributed database systems with different consistency models, measure their performance characteristics, and evaluate reliability under various failure scenarios. This foundational topic addresses core challenges in distributed systems design.

21. Cryptographic Protocols for Secure Communication in Post-Quantum Computing Scenarios

This study examines lattice-based and other quantum-resistant cryptographic algorithms, assessing their suitability for protecting sensitive communications against future quantum computing threats. Your dissertation would analyse quantum-resistant cryptographic approaches, evaluate their performance and security properties, and assess their readiness for widespread deployment. This forward-looking topic addresses emerging security challenges as quantum computing develops.

22. Software Testing Automation Using Machine Learning to Generate and Prioritise Test Cases

This research develops ML models that automatically generate effective test cases and prioritise testing efforts, improving code coverage and reducing testing time in complex software projects. Your work would implement machine learning approaches for test case generation, develop prioritisation algorithms, and measure their effectiveness in reducing testing time while maintaining coverage. This topic combines software engineering with machine learning techniques.

23. User Experience Optimisation in Mobile Applications Through Behavioural Analytics and A/B Testing

This dissertation uses analytics tools and statistical testing methods to measure how interface changes impact user engagement, conversion rates, and retention in mobile applications. Your research would conduct A/B tests on mobile app interfaces, analyse user behavioural data, and demonstrate measurable improvements in key metrics. This UX-focused topic shows how data-driven approaches optimise user experience systematically.

24. Natural Language Generation Systems for Automatic Code Comment and Documentation Generation

This study applies sequence-to-sequence models to automatically generate clear, accurate code comments and documentation, reducing documentation burden on developers and improving code maintainability. Your dissertation would develop or improve documentation generation systems, evaluate their effectiveness, and measure developer productivity improvements. This topic addresses practical challenges in software maintenance and knowledge transfer.

25. Anomaly Detection in Time-Series Data Using Unsupervised Machine Learning for Network Monitoring

This research develops and compares unsupervised algorithms (clustering, autoencoders, isolation forests) to detect unusual patterns in network traffic, system performance, and application behaviour. Your work would implement anomaly detection approaches, test them on network monitoring datasets, and evaluate their effectiveness in identifying genuine issues versus false positives. This operational topic shows how machine learning improves system reliability monitoring.

26. Augmented Reality Applications in Software Development Education and Technical Training Programmes

This dissertation evaluates how AR technologies enhance learning outcomes in computer science education by providing immersive visualisations of abstract concepts and virtual hands-on experiences. Your research would develop AR educational applications, conduct pedagogical studies measuring learning improvements, and evaluate student engagement and comprehension. This educational technology topic combines AR implementation with learning science.

27. Semantic Web Technologies for Knowledge Representation and Information Integration Across Heterogeneous Data Sources

This study explores ontologies, RDF frameworks, and semantic reasoning to integrate and query data across disconnected systems while maintaining meaning and relationships across domains. Your dissertation would develop semantic web frameworks for specific domains, implement knowledge representation schemas, and demonstrate their effectiveness for cross-system data integration. This topic addresses challenges in data interoperability and semantic consistency.

28. Performance Optimisation of Deep Learning Models for Edge Deployment on Mobile and IoT Devices

This research investigates model compression, quantisation, and pruning techniques that enable efficient deployment of sophisticated neural networks on resource-constrained edge devices. Your work would implement optimization techniques, measure their impacts on model size and inference latency, and evaluate accuracy trade-offs. This performance-focused topic shows how machine learning techniques adapt to resource constraints in edge environments.

29. Secure Software Development Lifecycle Implementation in Agile Development Methodologies

This dissertation examines how security practices (threat modelling, code review, vulnerability scanning) integrate effectively into rapid agile development cycles without compromising release speed. Your research would analyse secure development practices, measure their integration impacts on agile teams, and evaluate effectiveness in identifying security vulnerabilities. This process-focused topic bridges security and software engineering practices.

30. Interpretable Machine Learning Methods for Feature Importance Analysis in High-Dimensional Datasets

This study implements SHAP values, LIME, and permutation importance techniques to explain which features drive predictions in complex models, improving model transparency and trustworthiness. Your dissertation would implement multiple interpretability techniques, compare their effectiveness, and demonstrate their utility in high-dimensional contexts. This topic addresses growing demands for model explainability across applications.

Why These Computer Science Dissertation Topics Matter in 2026

The topics presented in this guide represent the cutting edge of computer science research that UK universities expect from dissertations in 2026. They address real challenges facing the technology industry while remaining achievable within standard dissertation timescales. Whether you’re interested in the theoretical foundations of computing or the practical application of emerging technologies, these topics provide a springboard for meaningful, original research.

Current trends in computer science emphasise the intersection of multiple disciplines: machine learning meets cybersecurity; cloud architecture intersects with IoT; artificial intelligence enables new software engineering practices. The most compelling dissertation topics reflect these convergences, allowing you to demonstrate sophisticated understanding of how different technologies work together to solve complex problems.

As you explore these computer science dissertation topics, remember that your choice reflects not just your academic interests but also your professional trajectory. Employers and industry leaders increasingly value candidates who have engaged deeply with practical applications of theoretical concepts. A dissertation focused on real-world problems—whether improving security, enhancing user experience, or optimising performance—demonstrates that you understand how computer science creates tangible value. For further insights into related areas, explore our resources on writing Chapter 5 of your dissertation and our comprehensive computer science project topics guide.

Beyond the technical aspects, your dissertation demonstrates research capability, independent inquiry, and the ability to contribute meaningfully to scholarly discourse. These skills—research design, critical analysis, technical implementation, and scholarly communication—extend far beyond your chosen topic. They form the foundation for continued professional development throughout your career, whether in academia, industry, or emerging technology sectors.

The 30 topics provided here have been specifically selected to meet UK university standards while reflecting industry relevance. Each addresses contemporary challenges, offers scope for original contributions, and aligns with current research interests across computer science. However, these represent starting points rather than prescriptive selections. Your own interests, available resources, and supervisor expertise should guide your final topic selection. Feel free to adapt, combine, or extend these topics to better match your specific circumstances and aspirations.