Browsing by Author "Asanka, P.P.G.D."

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    A Comprehensive Approach to Evaluating Software Code Quality Through a Flexible Quality Model
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Shyamal, D.K.K.; Asanka, P.P.G.D.; Wickramaarachchi, D.
    The rapid growth of the software engineering sector has led to a detrimental effect on the quality of software being developed. Code quality is crucial in determining the overall quality of software however, it is often observed that quality management programs primarily focus on internal processes within organizations, while the importance of code quality lacks proper attention despite the existence of quality standards for software products and processes. Due to its dynamic nature, the concept of quality poses a challenge in terms of precise definition, however, this paper addresses this issue by providing a comprehensive definition for code quality that considers all its dimensions, thus laying the foundation for conducting research related to quality. Code quality encompasses factors such as readability, scalability, performance, and adherence to industry standards. High-quality code is easy to understand, modify, and test, making it more reliable and less prone to bugs. By considering the multitude of challenges that currently exist and acknowledging the criticality of code quality, this study proposes an approach for assessing code quality, and a comprehensive quality model that considers the most critical code quality attributes and their relevant metrics along with corresponding threshold values specifically use in the contemporary software industry.
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    Linguistics Analytics in Data Warehouses Using Fuzzy Techniques
    (IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Asanka, P.P.G.D.; Perera, A.S.
    A data warehouse is used intensively in many industry domains to gain competitive advantage over its competitors. In modern data warehouses, linguistic analytics is an important aspect, so that it has the ability to take more precious decisions. In most of the data warehouse implementations, it is designed for crisp analysis. Crisp analysis has its own limitations and boundaries with the major assumptions that every situation belongs to one state and denial to other states. Hence, crisp data warehouse does not allow to carry out linguistic analytics. When a fuzzy data warehouse is implemented, because of the fuzzy nature of the data warehouse, linguistic analytics can be done to a certain extent. In this research, non-functional requirements such as performance and configuration are also covered so that this method can be implemented in the real world