DRC 2024

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

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Subject: search.filters.subject.Machine Learning

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    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.
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    IMPROVING CARDIOVASCULAR RISK PREDICTION OF SRI LANKANS USING ARTIFICIAL INTELLIGENCE
    (The Library, University of Kelaniya, Sri Lanka., 2024) Mettananda, C.; Solangaarachchige, M. B.; Haddela, P. S.; Dassanayake, A. S.; Kasturiratne, A.; Wickramasinghe, A. R.; Kato, N.; De Silva, H. J.
    There are no CV risk prediction models derived from Sri Lankan cohorts. Therefore, the World Health Organization(WHO) risk charts developed for the Southeast Asia Region are being used to risk stratify Sri Lankans. However, Sri Lankans are quite different to some Southeast Asian countries and may not agree with Sri Lankans. Therefore, we aimed to develop a CV risk prediction model specific to a cohort of Sri Lankans. Using supervised machine learning of 10-year follow-up data of a randomly selected, population-based cohort of Sri Lankans, we developed a model to predict the 10-year risk of developing a cardiovascular event. We compared predictions of the new model at baseline in 2007 with the observed events in 2017 following a 10-year follow-up using receiver operating characteristic curves(ROC) to find the predictive performance. We compared the predictions of the new model and the currently used WHO risk charts. We selected 2596 Sri Lankans between 40 and 65 years old with no history of previous CV diseases (CVD) at recruitment and who had completed 10-year follow-ups. There were 179 hard CVDs recorded over the ten years. CVD included all cardiovascular deaths confirmed or presumed cases as mentioned in death certificates, non-fatal strokes, and physician-diagnosed non-fatal acute coronary syndromes, including elective percutaneous coronary interventions and coronary artery bypass grafts done on patients with symptomatic unstable angina. Any cardiac presentation except those mentioned here was excluded. Of 179 events, the ML-based model predicted 124; only 33 were predicted by the new model, while only 33 were predicted by 2019 WHO risk charts. The new ML-based model had 0.93 accuracy with an AUC-ROC of 0.74 ± 0.06. Machine learning of individual data of a Sri Lankan cohort improved CV risk prediction of Sri Lankans than using risk charts developed for an epidemiological region using a modelling approach.