Browsing by Author "Elisa, N."

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    Nexus of Big Data and Big Data Analytics for Managerial Business Decision-Making: A Systematic Review Analysis
    (University of Dar es Salaam, 2024) Didas, M; Chali, F; Elisa, N.
    The growing usage of big data and big data analytics in business has prompted academics and professionals to widen their examination of their implications in business decision-making procedures.  Until now, academics and business leaders have concentrated solely on the technical components of big data and analytics, ignoring the impact they have on the effectiveness of commercial decision-making systems. To begin, this paper intends to review the literature on the study of the relationship between the use of big data and big data analytics for its effectiveness in business industrial decision-making systems. Second, it gives important facts to assess whether big data and big data analytics catalyze the deployment of sophisticated business intelligence and informed decision-making representations. In this regard, the paper identifies the essential concerns that underpin the business-driven decision-making processes such as efficiency, and preciseness among others. Fundamentally, the current work contributes to the literature on big data and big data analytics for business-driven decision-making in both a theoretical fashion and provides a shot for future agenda possibilities to develop knowledge in this area.
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    On the Impact of Refactorings on Software Attack Surface
    (Institute of Electrical and Electronics Engineers (IEEE), 2024) Edward, E.; Nyamawe, A. S.; Elisa, N.
    Refactoring is one of the techniques mostly employed by software developers to improve thequality attributes of their systems. However, little has been done to investigate how refactoring operations specifically aimed at improving the internal structure of software can impact its security. Refactoring usually entails different code change operations including the decomposition of classes, methods, and the reallocation of code elements. While this refinement aims to improve the internal design of a system, it might inadvertently disperse security-critical code elements throughout the codebase. Consequently, such dispersion could affect the software attack surface. To this end, this paper presents an empirical study of 30 open-source software systems developed in Python, C, and Javascript. The study scrutinized two subsequent versions of each subject application to uncover the refactoring operations applied and the trend of the software attack surface. Specifically, the study focused on the injection or removal of bugs, code smells and other vulnerabilities aiming to discern the impact of refactorings on the software attack surface. Data was collected using wellknown tools, namely SonarQube, RefDiff, and PyReff. The findings suggest that refactorings can have multiple impacts (i.e., positive, negative, or neutral) on bugs, code smells, and vulnerabilities. The findings further confirm that developers must be aware of the combination or sequence of refactoring operations that can improve software quality without compromising its security.