Faculty of Computing
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4776
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Item Embargo HireGenius: Automated Interviewing System for Software Engineers(Springer Science and Business Media Deutschland GmbH, 2026-08-01) Hewamadduma N.A.; Nalinka G.K; Mahawaththa N.T.M.A.S.M; Rosa S.R.T.L; De Silva D.I.; Gunathilake P.Recruiting the right software engineers is a critical challenge, with traditional manual screening being time-consuming, subjective, and often inconsistent. Recruiters typically rely on Curriculum vitae reviews and interviews, which lack the depth needed for evaluating technical roles. For software engineers, it is essential to assess programming skills, academic performance, and personality traits. To overcome these limitations, this study developed an automated candidate selection and interview system using artificial intelligence, natural language processing, and deep learning. Ensemble learning and artificial intelligence models incorporating natural language processing were used to rank candidates and predict job match percentages. Top-ranked individuals were further evaluated through analysis of GitHub profiles, LinkedIn activity, and academic transcripts using machine learning and natural language processing techniques. Each candidate’s technical skills, experience, and education were assessed to generate accurate shortlists for technical interviews. These shortlisted candidates then participated in an automated interview process powered by advanced natural language processing and deep learning. A gamified human resource interview system was introduced, leveraging a machine learning model and structured scoring criteria to identify the best-fit candidates while streamlining and enhancing the hiring process.Item Embargo Evaluating Large Language Models for Software Testing: A Systematic Review of Metrics and Practices(Springer Science and Business Media Deutschland GmbH, 2026) Perera V.I.T; De Silva D.I.The recent advancements in Large Language Models (LLMs) present substantial potential for revolutionizing software testing practices, particularly through automated test case generation. This review synthesizes contemporary research on LLM-driven software testing methods, with a specific focus on evaluation metrics. A systematic literature review was conducted using databases like IEEE Xplore, ResearchGate, and Google Scholar, targeting literature published between 2020 and 2024, specifically focusing on LLM-based test case generation. Selection criteria included relevance to automated testing and practical application insights. This review analyzes 15 key studies that span multiple test domains, and the key findings reveal significant advancements in using LLMs for diverse testing types, including unit, property-based, security, and user acceptance testing. Despite substantial benefits, issues such as test case validity, reliability, and prompt engineering complexity remain challenging. The review concludes with recommendations for developing a standardized metric-driven evaluation framework for better assessing LLM-generated tests. This comprehensive approach aims to effectively measure and optimize the practical utility and reliability of LLM-generated software tests, ultimately guiding future research directions and improving adoption within the software industry. The key contribution of this review is a comprehensive metric-focused evaluation of LLM-driven software testing techniques offering a foundation for developing standardize evaluation methodologies and practical testing frameworks.Item Embargo A Game Centric E-Learning Application For Preschoolers(Institute of Electrical and Electronics Engineers Inc., 2025) Kulasekara D.A.M.N.; Nipun P.G.I.; Dombawela H.M.D.L.B.A; Manilka G.S; Manilka G.S; De Silva D.I.This research explores the potential of advanced technologies such as pose detection (PD), augmented reality (AR), object detection (OD), and voice recognition (VR) in creating a game-centric e-learning application for preschoolers. The proposed application, Kidstac, integrates cognitive and physical development through interactive activities with real world interaction, addressing gaps in traditional e-learning methods that often neglect physical engagement. The app features real-time feedback mechanisms and structured modules like virtual zoo explorations, exercise games, treasure hunts, and pronunciation activities. Testing results indicate significant improvements in motor skills, knowledge retention, problem-solving abilities, and language proficiency. These findings demonstrate the effectiveness of blending physical and digital learning experiences to enhance early childhood education. The study establishes a foundation for scalable, activity-based learning tools, emphasizing the holistic development of young learners.