PI: Sandareka Wickramanayake
We propose to advance automated program repair by leveraging natural language processing techniques, iterative repair strategies, and explainable AI frameworks to enhance accuracy, robustness, and interpretability of repair systems.
PI: Nisansa de Silva
We propose to advance aspect-based sentiment analysis (ABSA) by developing enhanced models, datasets, and evaluation methods that improve the accuracy and applicability of sentiment classification across multiple domains.
PI: Nisansa de Silva
We propose to advance natural language processing for low-resource settings by leveraging data augmentation, transfer learning, and model adaptation techniques to overcome the scarcity of annotated data and linguistic resources.
Theses: MSc Minor Component Research
Sadeep Gunathilaka, "Automated User Review Analysis To Facilitate Potential Mobile Application Evolution", University of Moratuwa, 2025
Conference Papers
Mineth Weerasinghe, Himindu Kularathne, Methmini Madhushika, Danuka Lakshan, Nisansa de Silva, Adeesha Wijayasiri, and Srinath Perera, "From Monolith to Microservices: A Comparative Evaluation of Decomposition Frameworks", in Recent Trends and Challenges in Information Systems and Technologies, 2027, pp. 309--320. doi: 10.1007/978-3-032-32032-2_29
Kaushal Kahapola, Sharada Galappaththi, Dinith Ranasinghe, Ridwan Shariffdeen, Nisansa de Silva, Srinath Perera, and Sandareka Wickramanayake, "JavaBackports: A Dataset for Benchmarking Automated Backporting in Java", in 23rd International Conference on Mining Software Repositories (MSR), 2026, pp. 712-716. doi: 10.1145/3793302.3793331
Sadeep Gunathilaka and Nisansa de Silva, "Automatic Analysis of App Reviews Using LLMs", in Proceedings of the Conference on Agents and Artificial Intelligence, 2025, pp. 828-839. doi: 10.5220/0013375600003890 
Sadeep Gunathilaka and Nisansa De Silva, "Aspect-based Sentiment Analysis on Mobile Application Reviews", in 2022 22nd International Conference on Advances in ICT for Emerging Regions (ICTer), 2022, pp. 183--188. doi: 10.1109/ICTer58063.2022.10024070
