MSc in Information Systems

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/2481

Students enrolled in the MSc in Information Systems programme are required to submit a thesis as a compulsory component of their degree requirements. This collection contains merit-based theses submitted by postgraduate students specialising in Information Systems. Abstracts are available for public viewing, while the full texts can be accessed on-site within the library.
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Theses and Dissertations of the Sri Lanka Institute of Information Technology (SLIIT) are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

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    PublicationOpen Access
    AI-DRIVEN SELF-HEALING TEST AUTOMATION FOR ENTERPRISE SOFTWARE SYSTEMS
    (Sri Lanka Institute of Information Technology, 2025) Jinarathna, H. D. R. J.
    Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) platforms are backbone tools for today’s businesses, helping teams around the company work together efficiently. But because these systems are huge and always changing, testing them gets tricky. Methods we’ve traditionally used—whether having testers run scripts by hand or running automated scripts—can’t keep up. To solve these problems, I’ve built a test automation framework that uses AI to repair itself as it runs. By incorporating Natural Language Processing (NLP) for spotting system changes and Reinforcement Learning (RL) for teaching tests to heal and get sharper, the framework learns to find when a test has broken, to fix the code on the fly, and to keep fine-tuning itself, so people hardly need to step in. I shaped the system by talking to QA engineers about the roadblocks and running pilot cases in a pretend ERP setup. Those conversations, plus the numbers, helped us tweak the design so it feels less like a lab gadget and more like a teammate. Early results are encouraging—flake tests bounce back 35% more often and testers spend 25% less time rewriting logic by hand. My research helps Software Quality Assurance Engineers, learners, and software businesses by offering an easy-to-understand, adaptable way to test big-company software. The results show how using smart technology can make software testing faster, cheaper, and better.
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    PublicationOpen Access
    The Impact Of School-Level ICT Education On The National Digitization Effort In Sri Lanka
    (Sri Lanka Institute of Information Technology, 2025-12) Alex, H.V.I.S
    The significant demand for digitally qualified workforces in Sri Lanka under the digitization programme, has placed considerable pressure on the supply chain, where School ICT education systems is the primary supplier. This study was to evaluate the national policy and the educational outcomes. The main objective of this research was to measure the gap between the demand and supply of digitally qualified work forces, and to develop and implement an ideal information system to assist in finding a solution to this complex issue. That is CoreAlign-Nexus. A mixed methods approach and analysis of the primary survey data collected from 101 students and 15 teachers was used to analyze the results of the study. The result revealed that 48% of students lacked access to computers which is an essential requirement to follow the practical aspects of their instruction. This revealed inequality in education, which is a failure. The analysis found that confidence acted as the determining factor for an individual to enter the job market; however, the education system was unsuccessful in supplying qualified individuals. Based upon different international policies, this AI artifact proved to be reliable and dynamic. The evaluations by a panel of seven experts concluded the following: that the artifact’s ability to diagnose the problems presented were very accurate (M = 4.86 / 5.00); but its ability to present solutions were considered to be unrealistic and impractical (M = 3.43 / 5.00). Based on the findings of this study, a “human-in-the-loop” model is recommended as the primary contribution of this research.
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    PublicationOpen Access
    Predicting Cryptocurrency Trade Count with Machine Learning Models
    (Sri Lanka Institute of Information Technology, 2025) Doluweera,C.Y.P
    This study examines the prediction of cryptocurrency trade counts using machine learning. Minute-level market data were cleaned, merged, and enriched with time features to form a reliable dataset. Two ensemble regressors, Random Forest and Gradient Boosting, were implemented alongside baselines, with performance judged mainly by Mean Absolute Error and supported by RMSE. Model tuning and cross validation were used to improve robustness, and results were visualized to compare errors, track actual versus predicted counts, and explain feature influence. Across the tested assets, Random Forest delivered the most consistent accuracy and generalizable results. Feature importance analysis showed trading volume in USD as the dominant driver of predictions, with additional value from simple temporal cues such as hour and day. Deep learning approaches were explored for their ability to capture non-linear and temporal patterns, but they required further stabilization to match the ensembles on this dataset. The work highlights both the promise and the limits of machine learning in a market that trades constantly and moves quickly. Models captured broad trends yet struggled with sharp spikes typical of high volatility periods. The thesis proposes practical next steps, including periodic retraining, integration of sentiment and external signals, and the use of explainable methods to improve transparency. These contributions offer a clear framework for real time trade count forecasting and for building adaptive tools to support decision making in digital asset markets.
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    PublicationOpen Access
    IT Service Management Challenges During Healthcare Digital Transformation: Evidence from Electronic Medical Record Systems in Sri Lankan Government Hospitals
    (Sri Lanka Institute of Information Technology, 2025-01) Weerarathna,K.A.N
    This research investigates IT Service Management (ITSM) challenges during healthcare digital transformation through the implementation of Hospital Health Information Management Systems (HHIMS) in NorthWestern Province government hospitals, Sri Lanka. Using the ITIL v4 Service Value System framework, this mixed-methods study examines challenges across three key dimensions: Organizations and People, Information and Technology, and Partners and Suppliers. The study employed sequential explanatory mixed-methods design, collecting quantitative data from 26 IT professionals through structured questionnaires and qualitative insights from 12 healthcare professionals via semi-structured interviews. Statistical analysis revealed significant correlations between ITIL v4 dimensional challenges and digital transformation success, while thematic analysis identified critical implementation barriers including inadequate change management, infrastructure limitations, and multi-stakeholder coordination difficulties. Key findings indicate that Organizations and People dimension challenges (r = -0.68, p < 0.01) had the strongest negative impact on transformation success, followed by Information and Technology challenges (r = -0.61, p < 0.01) and Partners and Suppliers coordination issues (r = -0.59, p < 0.01). Qualitative analysis revealed that staff resistance stemmed from inadequate training (83% of respondents), poor digital literacy support, and system design misalignment with clinical workflows. The research contributes a contextual framework for understanding ITSM challenges in developing country healthcare systems and provides actionable recommendations for improving EMR implementation success. Policy implications include the need for comprehensive management strategies, enhanced technical infrastructure, and improved multi-agency governance structures.
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    PublicationOpen Access
    Evaluating Digital Banking’s Impact on Customer Preferences and Employee Efficiency in Sri Lanka
    (Sri Lanka Institute of Information Technology, 2025-12) Deshapriya,A.N.E.
    The fast expansion of digital banking in Sri Lanka has significantly transformed the financial services landscape, affecting both customer choices and internal operational processes within banks. This research investigates how digital platforms influence customer behavior by examining key aspects such as convenience, trust, system reliability, ongoing IT skill enhancement, and organizational culture. Simultaneously, it evaluates employee efficiency in relation to process automation, technological preparedness, digital competence, and overall performance. A mixed-methods approach is employed, combining customer surveys with interviews conducted among employees across various banking departments. Findings reveal that perceived usefulness, and data security are central to enhancing customer satisfaction, fostering trust, and driving digital adoption. For employees, efficiency improvements are strongly linked to adequate training, streamlined systems, and effective integration of digital tools, which together support sustainable digital transformation and compliance with regulatory standards. Based on these insights, the study recommends strengthening digital infrastructure, advancing employee training programs, improving user interfaces, and promoting digital inclusivity to build customer confidence. The outcomes provide practical guidance for banks, policymakers, and IT solution providers in advancing customer-focused and digitally empowered banking services in Sri Lanka.
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    PublicationEmbargo
    Enhancing the value proposition of priority Banking in SriLanaka
    (Sri Lanka Institute of Information Technology, 2025-10) NAWARATHNE,I.K.S.S.
    Priority banking has become a vital strategic focus for banks and financial institutions aiming to retain and grow affluent, high-net-worth client segments. In Sri Lanka, despite significant investments in branded premium offerings, evidence suggests that the actual value perceived by customers often falls short of expectations. Past studies highlight key issues, including a lack of personalization, insufficient digital integration, reactive relationship management, and limited lifestyle benefits. Customer feedback reinforces this gap, with many clients perceiving “priority” services as symbolic rather than substantive. This research investigates how Sri Lankan banks can enhance the value proposition of their priority banking services by aligning more closely with evolving elite customer expectations. Using a mixed-method approach, the study integrates surveys and interviews with relationship managers and priority clients in certain states and private banks in Sri Lanka, supported by secondary data from reports and academic studies. Findings reveal that while customers value exclusivity and trust, true loyalty is undermined by generic service delivery and limited proactive engagement. To address these gaps, this study proposes the P.E.A.K. Model, a strategic framework emphasizing Personalization (tailored financial and lifestyle solutions), Experience Integration (seamless digital- physical journeys), Anticipation (proactive, predictive engagement), and Knowledge Empowerment (financial literacy and advisory tools). The model underscores the importance of leveraging AI- driven personalization, omnichannel CRM, and well-trained relationship managers to create a more differentiated and meaningful client experience.
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    PublicationOpen Access
    Design and Validation of an AI-Enhanced Career Guidance Framework for Sri Lankan Secondary Education
    (Sri Lanka Institute of Information Technology, 2025-12) Karthiha, S.
    Career guidance is a decisive factor that contributes to the educational and professional path of students, but in Sri Lanka, the practices that are currently being used are mostly manual, fragmented, and unfair. This paper forms a conceptual model of an Artificial Intelligence (AI)-driven career guidance system that provides students with personalized educational and professional advice to high school students. The study is based on a mixed-methods research framework that combines both quantitative data collected in the form of the survey of 379 educational professionals working in nine provinces and the qualitative information gathered in the form of the interviews with ten experts. Key determinants of career decision-making were identified in a quantitative analysis and found to include academic performance, aptitude, personal interests, and socio-economic background, and six interrelated dimensions were identified in a qualitative thematic analysis: human-dominated guidance, family and cultural influence, perceived fairness of AI systems, ethical and infrastructural barriers, hybrid human-AI collaboration, and equity of access. The presented framework integrates these insights into a hybrid structure, which will combine AI-based analytics with advisory and parental judgment and will be culturally sensitive and ethically valid. The model is more accurate, inclusive, and efficient because it matches student profiles with the trends of the labour-market. The results prove that AI is capable of being more of a supplement to the human knowledge and can be used to increase access to data-driven counselling in even schools with scarce resources. The research has theoretical value in that it links AI technology to the career development theory and practical in that it will provide a replicable solution to the modernization of school guidance ecosystem in Sri Lanka by policymakers and educators. It ends with implementation recommendations on data ethics, transparency and capacity building to attain equitable and evidence based educational and career decisions support.
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    PublicationOpen Access
    Customer Risk Profiling System
    (Sri Lanka Institute of Information Technology, 2025-12) GUNASEKARA, G.A.R.
    This thesis addresses the critical need for advanced, ethical risk quantification in the motor insurance sector, currently hampered by fragmented data and limited cross-company fraud visibility. The primary objective was to design and validate a Customer Risk Profiling System (CRPS) that integrates heterogeneous data sources and utilizes Machine Learning (ML) for dynamic risk scoring. The methodology involved aggregating data streams including claims, premiums, policy history, and external PEP/AML compliance scores and employing Gradient Boosted Trees (GBTs) to achieve a high classification Accuracy of $0.7561$ and a ROC-AUC of $0.8982$. Empirical findings confirmed the predictive power of behavioral features over linear demographic metrics, validating the choice of non-linear ensemble models. The CRPS successfully segments customers into Low, Medium, and High-Risk tiers, enabling targeted intervention. Crucially, the system embeds Explainable AI (XAI) using SHAP values and a continuous Feedback Loop to maintain accuracy against concept drift, ensuring auditability and ethical governance against potential bias. The study concludes by proposing the Insurance National Grid (NIG), a centralized platform designed to connect all insurers to the regulator. The NIG would enforce data standardization and enable cross-company fraud detection, magnifying the CRPS's impact from a firm-specific tool to a national strategic asset, thereby promoting market efficiency, compliance, and sustained sector resilience.
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    PublicationOpen Access
    Challenges and Opportunities in Mobile Banking Adoption among Young Adults in Sri Lanka
    (Sri Lanka Institute of Information Technology, 2025-09) Liyanage S L
    This study examines the challenges and opportunities associated with mobile banking adoption among young adults in Sri Lanka. Mobile banking currently provides accessibility, convenience and financial inclusion, yet its adoption remains inconsistent due to issues such as security concerns, lack of trust, digital literacy gaps and infrastructure limitations. While mobile banking provides numerous benefits, such as 24/7 access to financial services, reduced dependency on physical bank branches and increased financial inclusion, significant barriers still need to be addressed. The purpose of this research is to identify barriers, examine user perceptions and propose strategies that can enhance adoption. A structured questionnaire will be administered among young adults in different geographic settings (urban, suburban, rural). To guarantee a thorough understanding, both mobile banking users and non-users will be included. Data collected will be analyzed to highlight trends in adoption, perceptions of security and the role of financial literacy. It is anticipated that the results would help banks, policymakers and tech companies create mobile banking systems in Sri Lanka that are safer, easier to use, and more inclusive. By addressing existing challenges and leveraging opportunities, the research aims to foster a more inclusive and technology-driven financial ecosystem that aligns with global banking trends.
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    PublicationOpen Access
    AI-Driven Help Desk Integration: Enhancing Customer Support with Chatbots, Sentiment Analysis, and SLA Automation
    (Sri Lanka Institute of Information Technology, 2025-11) Nimnadi Dilsika
    This research investigates the integration of Artificial Intelligence (AI) into help desk systems to improve customer service efficiency, accuracy, and overall satisfaction. The proposed AI-driven help desk framework combines three intelligent components: a chatbot for instant and automated responses, a sentiment analysis engine to detect and interpret customer emotions, and a Service Level Agreement (SLA) management module that ensures real-time tracking of response and resolution performance. Using a dataset of 40,000 simulated support tickets, the system was evaluated for key metrics such as response time, SLA compliance, and customer satisfaction levels. The results demonstrated notable improvements, including faster response rates, higher SLA adherence, and enhanced emotional understanding in customer interactions. Overall, the study confirms that AI integration transforms traditional help desks into proactive, data-driven, and emotionally intelligent service environments. Future advancements will focus on predictive SLA modeling, multilingual capabilities, and multimodal sentiment analysis for broader adaptability.