Research Publications
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Item Embargo CodeHarbor: A Code Analysis Tool(Springer Science and Business Media Deutschland GmbH, 2026) Dewmin T.Y; Kodithuwakku Y.S.; Dayananda I.H.M.B.L; Fernando K.R.A.W; De Silva D.I; Rathnayake S.As software systems grow more complex, developers face increasing challenges in maintaining and evolving codebases, often resulting in higher costs and longer development cycles. To address these issues, this study presents CodeHarbor, an intelligent tool that integrates machine learning with code analysis to simplify complex code segments. CodeHarbor calculates complexity metrics and offers personalized, context-aware suggestions for improving code quality. By automating code reviews, detecting anomalies, and recommending optimized refactoring strategies, it enables early issue resolution and enhances maintainability. The backend leverages artificial intelligence to identify patterns, enforce coding standards, and generate actionable insights, while the intuitive frontend provides real-time feedback, visualizations, and detailed improvement summaries. CodeHarbor also highlights repetitive patterns and compliance issues, helping developers track progress and reduce manual review effort. With its seamless integration of analysis and interface, CodeHarbor streamlines development workflows and promotes sustainable, high-quality software engineering.Item Embargo Mitigating Human Bias in Candidate Evaluation Through an AI-Driven Multimodal Assessment System(Springer Science and Business Media Deutschland GmbH, 2026) Gunarathna B.M; Thennakoon I.C; Anjalie S.; Pinsara D.; De Silva D.I; Gunathilake M.PThe recruitment process is often marred by human bias and inconsistent evaluations, especially when relying on traditional interview techniques. This paper introduces a scalable AI-driven, multimodal interview system designed to deliver objective and comprehensive assessments of job candidates. The proposed framework integrates natural language processing, computer vision, and code quality analysis to evaluate both technical skills and interpersonal attributes. It features four key components: (1) automated skill and professionalism assessment through resume parsing and behavioral analysis during a mock exam, (2) voice-based confidence evaluation using speech feature ex-traction and integrates natural language processing, (3) a gamified technical interview environment with real time stress detection via facial expression analysis, and (4) code complexity and maintainability analysis employing Cyclomatic Complexity, Cognitive Function Complexity, and Weighted Code Complexity metrics. Experimental results demonstrate that the system provides accurate, bias-reduced evaluations across technical and non-technical domains. By unifying behavioral and technical metrics, this approach offers a fair, efficient, and data driven alternative to conventional hiring methods.