Browsing by Author "Tsinale, Harriet Loice"

Now showing 1 - 5 of 5

Application lifecycle management activities for quality assurance in software development
(International Journal of Trend in Research and Development, 2017) Otibine, Tobias Okumu; Mbuguah, Samuel; Kilwake, Juma; Tsinale, Harriet Loice
Lifecycle Management approaches promise more systematic and efficient ways to support the development and management of complex products. The concept of Application Lifecycle Management (ALM) indicates the coordination of activities and the management of artifacts during the software product‟s lifecycle. Most of the currently available unified ALM solutions are either based on basic version control and other 'low level' point-to-point integrations, or advocate the adoption of a new and expensive all-in-one solution from a single vendor. The problem with these current solutions is that the first does not go far enough to really provide the previously described benefits of applying an ALM solution, while the second one is often associated with high costs in tools, infrastructure and personnel which in turn affect the quality of software developed for SME‟s and middle level organizations. This paper discusses the effect of application lifecycle management activities on quality assurance in software development. The research employed multiple case study design. The data collection tools included Questionnaire, Observation and Interviews. ALM activities were found to be a direct predictor of Software quality assurance in software development. The research found out that ALM elements and their relations together with good documentation were very key in coming up with an efficient ALM solution and with improved process support and better knowledge and experience on ALM, application lifecycle management activities greatly affected the quality of software's developed thus improving on quality assurance in software development.
Application Lifecycle Management Activities For Quality Assurance In Software Development
(International Journal of Trend in Research and Development, 2017-04-26) Otibine, Tobias Okumu; Mbuguah, Samuel Mungai; Kilwake, Juma; Tsinale, Harriet Loice
Lifecycle Management approaches promise more systematic and efficient ways to support the development and management of complex products. The concept of Application Lifecycle Management (ALM) indicates the coordination of activities and the management of artifacts during the software product‟s lifecycle. Most of the currently available unified ALM solutions are either based on basic version control and other 'low level' point-to-point integrations, or advocate the adoption of a new and expensive all-in-one solution from a single vendor. The problem with these current solutions is that the first does not go far enough to really provide the previously described benefits of applying an ALM solution, while the second one is often associated with high costs in tools, infrastructure and personnel which in turn affect the quality of software developed for SME‟s and middle level organizations. This paper discusses the effect of application lifecycle management activities on quality assurance in software development. The research employed multiple case study design. The data collection tools included Questionnaire, Observation and Interviews. ALM activities were found to be a direct predictor of Software quality assurance in software development. The research found out that ALM elements and their relations together with good documentation were very key in coming up with an efficient ALM solution and with improved process support and better knowledge and experience on ALM, application lifecycle management activities greatly affected the quality of software's developed thus improving on quality assurance in software development.
Architectural Health Data Standards and Semantic Interoperability: A Comprehensive Review in the Context of Integrating Medical Data into Big Data Analytics
(International Journal of Engineering Applied Sciences and Technology, 2023-03-26) Tsinale, Harriet Loice; Mbugua, Samuel Mungai; Luvanda, Anthony
The integration of medical data into Big Data analytics holds significant potential for advancing healthcare practices and research. However, achieving semantics interoperability, wherein data is exchanged and interpreted accurately among diverse systems, is a critical challenge. This study explores the impact of existing architectures on semantics interoperability in the context of integrating medical data into Big Data analytics. The study highlights the complexities involved in integrating medical data from various sources, each using different formats, data models, and vocabularies. Without a strong emphasis on semantic interoperability, data integration efforts can result in misinterpretations, inconsistencies, and errors, adversely affecting patient care and research outcomes. The significance of data standards and ontologies in establishing a common vocabulary and structure for medical data integration is underscored. Additionally, the importance of data mapping and transformation is discussed, as data discrepancies can lead to data loss and incorrect analysis results. The success of integrating medical data into Big Data analytics is heavily reliant on existing architectures that prioritize semantics interoperability. A well- designed architecture addresses data heterogeneity, promotes semantic consistency, and supports data standardization, unlocking the transformative capabilities of medical data analysis for improved healthcare outcomes.
Effective approaches for enhancing data management and semantic interoperability within the healthcare sector
(International Journal of Applied Research, 2023-08-04) Tsinale, Harriet Loice; Mbugua, Samuel Mungai; Luvanda, Anthony
In a variety of contexts, Big Data is widely used. In healthcare Big Data has its own characteristics, including heterogeneity, incompleteness, timeliness and durability, privacy, and ownership. To enhance health-related science, these features present a number of challenges for data storage, mining, and sharing. Big Data helps to understand electronic health records, gather demographic and medical data such as clinical data, medical conditions and diagnosis, allow doctors to provide a wide variety of patients with quality health care and also to make informative decisions. The large volume of data also provides researchers in the fields of medicine and healthcare with the ability to use tools and techniques to unlock hidden solutions. This study sought to investigate the effects that the existing approaches have on enhancing data management and semantic interoperability in healthcare sector. 180 respondents who took part of the survey were chosen from the Kenyatta University Teaching, Referral & Research Hospital. It was discovered that implementing health standards and tools can help healthcare companies in a number of ways by eliminating compatibility concerns and assuring accurate data representation. Consequently, encouraging successful adoption boosts decision-making processes, encourages sustainability, improves data quality, allows for interoperability, and makes it easier to comply with regulations. These implications help improve patient care, healthcare systems, and overall health outcomes
Performance expectancy as a determinant for cloud computing acceptance in Kenyan Public Universities
(International Journal of Applied Research, 2016) Tsinale, Harriet Loice; Wabwoba, Franklin; Ikoha, Anselemo P; Otibine, Tobias Okumu
Cloud Computing is interrelated with a new perception for the provision of computing infrastructure and other services. It provides an infrastructure that is scalable, usable, virtually accessible and adjustable Information Technology resources that need not be owned by an individual but can be delivered as a service over the Internet. This paper discusses the effect of Performance Expectancy on the acceptance to use Cloud Computing within public universities in Kenya. Mixed method research was used with Purposive and Stratified sampling being used to select the sample population and the sample size was 181 respondents from four public universities which were arrived at by using proportional allocation method. Questionnaires and interviews were used to capture data. The study established factors such as Performance Expectance to be a direct predictor of acceptability of cloud computing, migration to the cloud computing should be structured and incremental and more people would use Cloud Computing if given access to reliable internet and computers.