Comparative Analysis of Software Development Life Cycle Models (SDLC)

Akinsola, J. E. T.; Ogunbanwo, Afolakemi Simbo; Okesola, Olatunji; Odun-Ayo, Isaac; Ayegbusi, Florence D.; Adebiyi, A. A.

doi:10.1007/978-3-030-51965-0_27

Cited by 35 publications

(21 citation statements)

References 1 publication

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“…Standardization does not exist, and thus the choice of threat models is deterministic of the project needs like targeted risk area, allocated time, expertise, and stakeholder's involvement [68]. Furthermore, DOI: http://dx.doi.org/10.5772/intechopen.96534 it is advantageous to apply threat models at the requirement and design stage of the project life-cycle [65] and the use of a well-formulated model for Software Development Life Cycle (SDLC) [69] for efficient threat modeling is desirous. Though AI sheds a positive light on cyber security, it also presents alarming intrusion possibilities for cyber criminals.…”

Section: Predictive Analytics Of Threat Modelsmentioning

confidence: 99%

Application of Artificial Intelligence in User Interfaces Design for Cyber Security Threat Modeling

Akinsola¹,

Akinseinde²,

Olamide³

et al. 2022

Software Usability

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In recent years, Cyber Security threat modeling has been discovered to have the capacity of combatting and mitigating against online threats. In order to minimize the associated risk, these threats need to be modelled with appropriate Intelligent User Interface (IUI) design and consequently the development and evaluation of threat metrics. Artificial Intelligence (AI) has revolutionized every facet of our daily lives and building a responsive Cyber Security Threat Model requires an IUI. The current threat models lack IUI, hence they cannot deliver convenience and efficiency. However, as the User Interface (UI) functionalities and User Experience (UX) continue to increase and deliver more astonishing possibilities, the present threat models lack the predictability capacity thus Machine Learning paradigms must be incorporated. Meanwhile, this deficiency can only be handled through AI-enabled UI that utilizes baseline principles in the design of interfaces for effective Human-Machine Interaction (HMI) with lasting UX. IUI helps developers or designers enhance flexibility, usability, and the relevance of the interaction to improving communication between computer and human. Baseline principles must be applied for developing threat models that will ensure fascinating UI-UX. Application of AI in UI design for Cyber Security Threat Modeling brings about reduction in critical design time and ensures the development of better threat modeling applications and solutions.

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Section: Predictive Analytics Of Threat Modelsmentioning

confidence: 99%

Application of Artificial Intelligence in User Interfaces Design for Cyber Security Threat Modeling

Akinsola¹,

Akinseinde²,

Olamide³

et al. 2022

Software Usability

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“…This framework is based on traditional information systems development models like Waterfall, V-process, Rapid action development (RAD), and Spiral models. The models outline information systems development processes from planning and identifying requirements up to development and later maintenance [13] [23][24][25]. Each of the information systems development models has its own strengths and weaknesses with some models better fitting particular projects than others [24][25].…”

Section: Framework For Website Design and Developmentmentioning

confidence: 99%

“…The models outline information systems development processes from planning and identifying requirements up to development and later maintenance [13] [23][24][25]. Each of the information systems development models has its own strengths and weaknesses with some models better fitting particular projects than others [24][25]. The [13] framework was used to frame this study by identifying phases for website design and development and map them to guide preparation for data collection tools for this study.…”

Section: Framework For Website Design and Developmentmentioning

confidence: 99%

Website Development: The Case of Agricultural Extension Providers in Tanzania

Ngessa¹,

Michael²,

Mtei³

et al. 2022

IJASRE

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This study examined current practice in websites development among organizations in Tanzania that provide agricultural extension services to smallholder farmers. Google search was used to get a list of respondent organizations. A questionnaire, interviews, and a review of the websites owned by organizations under study were used to collect data. Other than websites, it was found that organizations use manual documents, physical visits, radios, SMSs, TVs, and social networks to disseminate information to smallholder farmers. Websites development was either done in-house or outsourced and the focus was to have websites that cater to the information needs of different stakeholders including smallholder farmers. Information on websites included contacts, announcements, specific information for farmers, with some of the information not directly relating to smallholder farmers. This led websites to contain lots of information which might affect the usability of those websites by smallholder farmers hence not benefiting from the information contained. This was the case for all websites. Also, the involvement of smallholder farmers during website development was low. Websites for agricultural extension providers were developed either in-house or outsourced. The government-owned agency (e-GA), commercial companies, and staff were among developers for the websites. Communication between stakeholders during the development process was easier for in-house development as compared to when websites were developed outside. Websites developed were either internally maintained or maintained outside organizations, with internal maintenance being cheaper compared to when maintenances were done outside organizations. To develop quality websites from smallholder farmers' point of view their involvement should be high and the websites and the contained information should be easier to access with minimal information. Also, websites should be professionally designed and developed to focus more on the provision of agricultural extension services.

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“…Software Development Lifecycles (SDLC): The various software development frameworks that are used to structure software development ( 8 ).…”

Section: Glossary Of Key Terms Used In This Manuscriptmentioning

confidence: 99%

An integration engineering framework for machine learning in healthcare

Assadi

Laussen

Goodwin

et al. 2022

Front. Digit. Health

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Background and ObjectivesMachine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare costs. Yet only a small fraction of all Machine Learning models for health care have been successfully integrated into the clinical space. There are no current guidelines for clinical model integration, leading to waste, unnecessary costs, patient harm, and decreases in efficiency when improperly implemented. Systems engineering is widely used in industry to achieve an integrated system of systems through an interprofessional collaborative approach to system design, development, and integration. We propose a framework based on systems engineering to guide the development and integration of Machine Learning models in healthcare.MethodsApplied systems engineering, software engineering and health care Machine Learning software development practices were reviewed and critically appraised to establish an understanding of limitations and challenges within these domains. Principles of systems engineering were used to develop solutions to address the identified problems. The framework was then harmonized with the Machine Learning software development process to create a systems engineering-based Machine Learning software development approach in the healthcare domain.ResultsWe present an integration framework for healthcare Artificial Intelligence that considers the entirety of this system of systems. Our proposed framework utilizes a combined software and integration engineering approach and consists of four phases: (1) Inception, (2) Preparation, (3) Development, and (4) Integration. During each phase, we present specific elements for consideration in each of the three domains of integration: The Human, The Technical System, and The Environment. There are also elements that are considered in the interactions between these domains.ConclusionClinical models are technical systems that need to be integrated into the existing system of systems in health care. A systems engineering approach to integration ensures appropriate elements are considered at each stage of model design to facilitate model integration. Our proposed framework is based on principles of systems engineering and can serve as a guide for model development, increasing the likelihood of successful Machine Learning translation and integration.

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Comparative Analysis of Software Development Life Cycle Models (SDLC)

Cited by 35 publications

References 1 publication

Application of Artificial Intelligence in User Interfaces Design for Cyber Security Threat Modeling

Application of Artificial Intelligence in User Interfaces Design for Cyber Security Threat Modeling

Website Development: The Case of Agricultural Extension Providers in Tanzania

An integration engineering framework for machine learning in healthcare

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