Sixty Years of Software Development Life Cycle Models

Kneuper, Ralf

doi:10.1109/mahc.2017.3481346

Cited by 15 publications

(22 citation statements)

References 23 publications

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“…Software development life cycle (SDLC) is one of the key issues in software engineering. It is because the structure of activities required to develop a software system weighs on the quality, usefulness and usability of the software developed [28,29]. Several approaches to software development exist.…”

Section: Delineating Traditional and Agile Software Development Methodsmentioning

confidence: 99%

Software Development Methodologies, HEIs, and the Digital Economy

Saeedi

Visvizi

2021

Education Sciences

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Progressing digitalization of business, economy, and the society places higher education institutions (HEIs) in the center of the debate on how to effectively respond to challenges and opportunities that are thus triggered. Several facets of this process and corresponding challenges exist, including the complex question of how to match students’ skills and competencies with the demands and expectations of the industry. From a different angle, considering the changing nature of work, HEIs are responsible for equipping future employees with skills necessary to work in virtual, distributed, culturally diverse, and frequently global, teams. In the domain of software development, i.e., the backbone of the digital world, the challenge HEIs need to face is paramount. For this reason, the way software development is taught at HEIs is crucial for the industry, for the economy, for the students, and for the HEIs. As there is a tendency in the industry to embrace the scrum method and seek employees equipped with skills necessary for the scrum methodology use, it is necessary to ensure that HEIs offer the students the opportunity to get exposed to scrum. By querying the challenges of switching to agile software development methodologies in senior capstone projects, this paper makes a case that software development and software development methodology form the thrust of a multi-stakeholder ecosystem that defines today’s digital economy and society. In this context, the added value of this paper rests in the elaboration of a method enabling HEIs to move toward scrum in senior projects.

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Section: Delineating Traditional and Agile Software Development Methodsmentioning

confidence: 99%

Software Development Methodologies, HEIs, and the Digital Economy

Saeedi

Visvizi

2021

Education Sciences

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“…A Software Development Life Cycle (SDLC) is a process consisting of a set of phases during which software is designed, planned, implemented and deployed [24]. An iterative SDLC model may complement agile methodologies since agile practices can be used within the iterative software development processes, e.g., agile planning and execution of development tasks may run through cycles of coding and testing.…”

Section: The Software Development Life Cyclementioning

confidence: 99%

“…Typically, an iterative SDLC may consist of an initial requirement phase followed by design-, development-, test-, deploy-and maintenance phases, respectively. These phases may differ in scope depending on what development methodologies are used (including tools for code versioning and continuous integration/deployment management), but in general, each phase should seamlessly deliver output to the subsequent phase [24].…”

Section: The Software Development Life Cyclementioning

confidence: 99%

Towards Agile Cybersecurity Risk Management for Autonomous Software Engineering Teams

Salin

Lundgren

2022

JCP

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In this study, a framework was developed, based on a literature review, to help managers incorporate cybersecurity risk management in agile development projects. The literature review used predefined codes that were developed by extending previously defined challenges in the literature—for developing secure software in agile projects—to include aspects of agile cybersecurity risk management. Five steps were identified based on the insights gained from how the reviewed literature has addressed each of the challenges: (1) risk collection; (2) risk refinement; (3) risk mitigation; (4) knowledge transfer; and (5) escalation. To assess the appropriateness of the identified steps, and to determine their inclusion or exclusion in the framework, a survey was submitted to 145 software developers using a four-point Likert scale to measure the attitudes towards each step. The resulting framework presented herein serves as a starting point to help managers and developers structure their agile projects in terms of cybersecurity risk management, supporting less overloaded agile processes, stakeholder insights on relevant risks, and increased security assurance.

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“…This approach also fails to recognize some of the unique implications of applied Machine Learning in practice. The purpose of SDLC has evolved over its 60-year history from ensuring an understanding of what needs to be done to focusing on structured development methods, to focusing on product delivery ( 26 ). To achieve this, there needs to be a balance between the structured and agile SDLC frameworks ( 26 ).…”

Section: Foundationsmentioning

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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Sixty Years of Software Development Life Cycle Models

Cited by 15 publications

References 23 publications

Software Development Methodologies, HEIs, and the Digital Economy

Software Development Methodologies, HEIs, and the Digital Economy

Towards Agile Cybersecurity Risk Management for Autonomous Software Engineering Teams

An integration engineering framework for machine learning in healthcare

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