DRC 2024

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/29875

Browse

Search Results

Now showing 1 - 10 of 25
  • No Thumbnail Available
    Item
    COMPARATIVE ANALYSIS OF REGIONAL NATIONAL RESEARCH AND EDUCATION NETWORK (NREN) CONSORTIUMS: COMPARING MEMBERSHIP, GOVERNANCE, FINANCING, AND REGIONAL CONTEXTS IN THE GLOBAL SCENARIO
    (The Library, University of Kelaniya, Sri Lanka., 2024) Withanage, S. D.; Ragel, R. G.; Gunasekera, K.
    This research undertakes a comprehensive comparative analysis of regional National Research and Education Network (NREN) consortiums, examining critical elements such as membership criteria, governance models, financing mechanisms, and the influence of regional contexts. The objective is to delineate best practices and provide recommendations to enhance the effectiveness, inclusivity, and sustainability of NREN consortiums globally. The analysis reveals that NREN consortiums vary significantly in their membership criteria, with some offering multiple membership categories beyond the core NREN members. Governance models predominantly feature a Board of Directors, although community governance models are also practiced. Financing mechanisms primarily rely on membership and service fees, with some consortiums in lower-income regions benefiting from grant funding. Regional contexts play a crucial role in the formation and operation of NREN consortiums. Political stability and economic strength are notably higher in American and European regions compared to African and Asian regions, where conditions vary widely. Technological advancement and digital connectivity are also more developed in American and European regions, while cultural and social factors exhibit significant diversity across all regions. Based on these findings, the study recommends enhancing membership criteria to be more inclusive, adopting effective governance models, diversifying financing mechanisms, and tailoring strategies to regional contexts.
  • No Thumbnail Available
    Item
    THE ROLE OF AI IN SOFTWARE TEST AUTOMATION - A SYSTEMATIC LITERATURE REVIEW
    (The Library, University of Kelaniya, Sri Lanka., 2024) Ranapana, R. M. S.; Wijayanayake, W. M. J. I.
    Artificial Intelligence (AI) has emerged as a transformative force in software test automation, enhancing the efficiency, accuracy, and reliability of testing processes. This systematic literature review investigates the role of AI in software test automation, focusing on key methodologies, applications, and challenges faced in its implementation. The review aims to identify and analyze the various AI-driven techniques such as Machine Learning (ML), Neural Networks, and Genetic Algorithms that are being utilized to optimize testing activities, including test case generation, defect detection, and test execution. The findings reveal that AI can significantly improve the software testing lifecycle by automating repetitive tasks, reducing human error, and increasing test coverage. By leveraging AI algorithms, organizations can achieve faster turnaround times and enhance the overall quality of software products. Moreover, AI facilitates predictive analytics, allowing teams to identify potential defects early in the development process, thus minimizing costs and time associated with late-stage bug fixes. However, the review also highlights several challenges that hinder the widespread adoption of AI in software testing. Issues such as data quality, model overfitting, and the complexity of integrating AI solutions with existing testing frameworks present significant barriers. Additionally, many AI applications remain largely theoretical or are limited to academic research, lacking real-world implementation. The insights gained from this review are invaluable for both researchers and practitioners seeking to harness the capabilities of AI to revolutionize software testing practices. By addressing the identified challenges and fostering collaboration between academia and industry, stakeholders can develop more robust frameworks and models that leverage AI's potential to create a more efficient and effective software testing environment.
  • No Thumbnail Available
    Item
    LEVERAGING LARGE LANGUAGE MODELS IN CYBERSECURITY: A SYSTEMATIC REVIEW OF EMERGING METHODS AND TECHNIQUES
    (The Library, University of Kelaniya, Sri Lanka., 2024) Sandaruwan, T.; Wijayanayake, J.; Senanayake, J.
    This systematic literature review examined how Large Language Models (LLMs) can be incorporated with vulnerability scanning and other cybersecurity tools and explored and assessed ways to improve cybersecurity practices. The PRISMA model was used, and the search was conducted using specific search terms in the leading databases such as the ACM Digital Library, IEEE Xplore Digital Library, and ScienceDirect from 2018 to July 2024. Initially, 313 records were gathered and reduced the count was reduced to 48 articles after applying the inclusion criteria. The findings were structured to answer the research questions regarding the approaches applied to incorporate LLMs with cybersecurity tools and the strengths and limitations of these tools based on the identified methodologies. The methods were reviewed and classified into Training and Adaptation Methods, Integration and Deployment Methods, and Inference and Utilization Techniques. After that, the accuracies of these methods were presented. The results show that fine-tuning and domain adaptation improves LLMs’ performance in cybersecurity tasks. In addition, fine-tuning, prompt engineering, and few-shot learning enhance models for specific tasks, making them more efficient in practical applications.
  • No Thumbnail Available
    Item
    REVIEW OF EFFECTIVE CANDIDATE EVALUATION USING KSA PARAMETERS
    (The Library, University of Kelaniya, Sri Lanka., 2024) Asanka, P. P. G. D.; Dilshan, B. A. T.
    This literature study has the goal of reviewing the significance of Knowledge, Skills, and Abilities in resume analysis in the case of software engineering applicants. The period of study is from 2015 to 2024, and the emphasis is on the use of Natural Language Processing (NLP) and Machine Learning (ML) in the automation of the recruitment process. The purpose of the study is to assess KSA(Knowledge, Skills, Abilities) factors in their relationship to resume analysis and evaluate successful approaches in the application of NLP and ML. Research data was obtained through academic databases. Inclusion criteria included information on KSA, peer-reviewed studies, and data on the NLP and ML application in resume analysis. The result is that 58 records were selected and submitted to risk of bias evaluation. The findings state that the employment of the combined NLP and ML significantly assists in the process of KSA evaluation of submitted resumes. Recommendations include further studies of the analysis and information extraction skills of the two technologies. The implications of KSA factors are that they significantly improve the resume analysis and candidate assessment. The results present important stakeholders, most influential researchers and authors, most reliable journals, and major trends in the field of resume evaluation. This study constitutes a new basis for the following research and applications. The emphasis can be made on the utilization of standardized concepts for KSA evaluation and further innovation in this sphere.
  • No Thumbnail Available
    Item
    OUTLIER DETECTION IN DATA WAREHOUSES TO IMPROVE DESCRIPTIVE AND DIAGNOSTIC ANALYTICS
    (The Library, University of Kelaniya, Sri Lanka., 2024) Fernando, W. W. A. D. R.; Asanka, P. P. G. D.
    This paper reviews the literature on outlier detection(OD) technologies to improve descriptive and diagnostic analytics in data warehouses. This will ensure higher-quality and more reliable data, increasing decision-making and operational efficiency. The major research objectives are a systematic review of existing OD techniques; identification and discussion of the key challenges and limitations in applying the OD methods to data warehouse environments and synthesis of methodologies for integrating OD with descriptive and diagnostic analytics. The study collects data from both traditional and AI-based literature review tools. Traditional review tools are Google Scholar, Research Gate, and IEEE Xplore. AI-based review tools are Semantic Scholar, Research Rabbit, and SciSpace. To present the insights, the study objectively selected 57 papers that were published between 2010 and 2024 were considered. The literature review here elaborates on the OD in a data warehouse which uses different data warehousing techniques and data analytics to enhance the quality and reliability of data to correct. This systematic review goes from the evaluation of statistical-based to distance-based, density-based, clustering-based, learning-based, and ensemble-based. The OD methods of unsupervised learning have been found to outperform those of supervised learning in special settings that are massive and heterogeneous in information like data warehouses. Isolation Forest, Local Outlier Factor (LOF), One-Class SVM, and Autoencoders are identified as highly accurate and efficient at detecting anomalies. Moreover, hybrid models combining several OD methods have been demonstrated to perform better than individual techniques. The results may be useful in offering important new insights and practical guidelines for developing more effective.
  • No Thumbnail Available
    Item
    FORMATIVE AND SUMMATIVE ASSESSMENT AND ITS IMPACT ON COURSE UNIT PERFORMANCE: EVIDENCE FROM MANAGEMENT UNDERGRADUATES OF UNIVERSITY
    (The Library, University of Kelaniya, Sri Lanka., 2024) Weligamage, S. S.; Karunarathne, W. V. A. D.; Karunarathne, R. A. I. C.
    Assessment plays a fundamental role in education. The primary purpose of this study is to examine the effect of formative and summative assessment on students’ performance in the course units. To achieve this objective, we designed this study as a multi-stage study, and this paper presented the data collected in stage one of the study. First, we examined the existing practices of formative and summative assessment in evaluating students’ performance; then, we examined the relationship between formative and summative assessment and the students’ performance of different course units. Furthermore, we examined the impact of formative and summative assessments on the performance of different course units. We collected secondary data from two selected departments of the Faculty of Commerce and Management Studies, University of Kelaniya, which aimed to see the effect of changing the composition of each assessment method on the final evaluation. We choose the course units which represent the main course units offered by the faculty. This study's findings revealed that the student's final performance of the course unit is highly related to the summative assessment, and the formative assessment score varies according to the type of assessment, i.e., individual vs group. Individual assessments have a more impact on final course unit performance. The outcomes of this study, theoretically as well as practically is very vital and important for policy design in higher education.
  • No Thumbnail Available
    Item
    POTENTIAL CRITICAL FACTORS INFLUENCING THE MATURITY OF BUSINESS ANALYTICS IN SRI LANKA – A SYSTEMATIC LITERATURE REVIEW
    (The Library, University of Kelaniya, Sri Lanka., 2024) De Silva, T. N.; Jayasinghe, S.; Wijayanayake., W. M. J. I.
    In the rapidly evolving, data-driven business landscape, organizations leverage vast amounts of data from various sources to enhance efficiency, decision-making, and financial performance. Business Analytics (BA), defined as the use of data, IT, statistical analysis, quantitative methods, and computer-based models, plays a crucial role in this context. The maturity of business analytics, which measures an organization's analytics competency, is pivotal for making informed, data-driven decisions. This systematic literature review investigates the potential critical factors influencing business analytics maturity in Sri Lanka's apparel and software industries. This review utilized the PRISMA framework to identify and screen relevant literature, resulting in the selection of 43 papers that met the inclusion criteria, focusing on business analytics maturity and its influencing factors, using the Technology-Organization-Environment (TOE) framework. The review identifies and categorizes key factors such as compatibility, data management, data infrastructure, technology-supporting infrastructure, trust-in-technology, top management support, organizational culture, organizational readiness, and environmental factors including regulations and competition pressure as the potential factors that could affect the BA maturity of Sri Lankan apparel and software industry. The analysis reveals that top management support, organizational culture, data management, and robust technology infrastructure are the most significant determinants of BA maturity. The findings suggest that while these factors are widely recognized in broader contexts, their applicability and impact within Sri Lanka's unique business environment require empirical validation. Consequently, this review highlights the necessity for future research to test these factors specifically in Sri Lanka's software and apparel industries.
  • No Thumbnail Available
    Item
    IMPACT OF CLOUD ENTERPRISE SYSTEMS ON BUSINESS SECURITY AND BUSINESS CONTINUITY IN SMES IN SRI LANKA: A SYSTEMATIC LITERATURE REVIEW
    (The Library, University of Kelaniya, Sri Lanka., 2024) Malshan, W. P. P.; Wijayanayake, W. M. J. I.
    The adoption of cloud enterprise systems presents a transformative opportunity for small and medium enterprises (SMEs) in Sri Lanka, particularly in enhancing business security and continuity. This research investigates how these systems can effectively address critical challenges faced by SMEs, including cybersecurity threats, regulatory compliance, and infrastructural constraints. Utilizing a mixed-methods approach, the study integrates a systematic literature review with qualitative data from interviews and focus groups, as well as quantitative data from structured surveys. The findings reveal that while cloud technologies provide significant benefits in terms of data security, operational efficiency, and disaster recovery, the adoption process is hindered by unique local challenges. Key barriers identified include inadequate technological infrastructure, limited awareness among stakeholders, and regulatory hurdles that complicate implementation. To tackle these issues, the research proposes a tailored cybersecurity framework designed to offer practical guidelines for SMEs, enhancing their security posture. Moreover, strategic recommendations are presented to assist policymakers and technology providers in creating a supportive environment for cloud adoption. This research contributes to a deeper understanding of the role of cloud enterprise systems in improving business security and continuity for SMEs in Sri Lanka. It offers actionable insights for stakeholders aiming to cultivate a resilient and secure business ecosystem. By addressing the specific needs and challenges of SMEs, the study aims to promote a more robust adoption of cloud technologies, ultimately supporting the growth and sustainability of these enterprises in the evolving digital landscape. Overall, this research underscores the importance of cloud solutions in fostering a secure and efficient operational framework for SMEs in Sri Lanka.
  • No Thumbnail Available
    Item
    ASSESS THE ROLE OF ARTIFICIAL INTELLIGENCE IN SUPPORTING AND ENHANCING DECISION-MAKING PROCESSES WITHIN ORGANIZATIONS: A SYSTEMATIC LITERATURE REVIEW
    (The Library, University of Kelaniya, Sri Lanka., 2024) Perera, K. A. V. U
    This study aims to assess the role of Artificial Intelligence (AI) in supporting and enhancing decision-making processes within organizations. The research objectives include understanding how AI influences decision-making, identifying AI tools and applications used in this context, and exploring the challenges associated with AI-driven decision-making. A systematic literature review was employed while reviewing articles from Google Scholar database. Key findings indicate that AI significantly enhances decision-making by providing data-driven insights, automating routine tasks, and enabling predictive analytics. AI tools such as machine learning algorithms, natural language processing, and expert systems were identified as critical enablers of improved decision-making processes. However, the study also highlights several challenges, including data quality issues, algorithmic bias, lack of transparency, ethical considerations, and the need for robust integration with existing organizational processes. The conclusion emphasizes that while AI holds considerable potential for transforming decision-making in organizations, addressing these challenges is crucial for its effective implementation. Recommendations include establishing robust data governance frameworks, investing in explainable AI techniques, implementing ethical guidelines, and fostering a culture of continuous learning and innovation. By navigating these challenges, organizations can fully leverage AI to make more informed, ethical, and effective decisions. The findings contribute to the existing body of knowledge on AI in organizational decision-making and provide practical insights for practitioners aiming to integrate AI into their decision-making processes.
  • No Thumbnail Available
    Item
    BARRIERS FOR MANUFACTURING SMES: A SYSTEMATIC EMPIRICAL RE-VIEW AND FUTURE RESEARCH AGENDA
    (The Library, University of Kelaniya, Sri Lanka., 2024) Hettiarachchi, H. N.; Abeysekera, R.; Divakara, S.
    Small and Medium-sector enterprises play an important role in creating employment opportunities, poverty reduction, GDP growth and economic improvement in any country. Thus it is considered as the backbone of the economy. The objective of this paper is to examine the entrepreneurship literature that discusses the barriers for small and medium-sector manufacturing organizations and set future research directions through a systematic literature review. To do this systematic literature review, a number of researches that investigated the barriers for manufacturing SMEs have been taken into consideration. To conduct the review, the PRISMA Method (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) has been followed. This study is based on journal articles published in databases such as Emerald Insight, Sage, Research Gate Net, Taylor & Francis, Science Direct and Google Scholar. After setting inclusion & exclusion criteria, 60 journal articles were selected. Economic issues, financial barriers, political barriers, lean manufacturing barriers, internationalization barriers, e-commerce adoption barriers, lack of resources and management issues have been identified as the prominent barriers for manufacturing SMEs. However, there is no adequate evidence to support that institutions and human capital are strong determinants for SME development. In line with the systematic literature review, most of the research to investigate SME barriers had been done in developing countries. However, research is scarce in South Asian countries like Sri Lanka, Nepal, Maldives and Bhutan. At the same time, the majority of the studies utilized the quantitative methodology to investigate the SME barriers. Qualitative researches are comparatively less. Therefore, more attention is needed to fill these gaps in existing literature to get a full understanding of the matter. Thus, this literature-based study calls for a new research agenda to investigate such issues as it directly and indirectly results in to improvement of the entire SME sector.