A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Shafiq, Saad; Mashkoor, Atif; Mayr-Dorn, Christoph; Egyed, Alexander

doi:10.1109/access.2021.3119746

Cited by 35 publications

(16 citation statements)

References 257 publications

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“…AI and SE can interact across iterative and agile software process lifecycles. For example, Shafiq et al 10 provide an overview of how AI is disrupting various lifecycle stages of software development. Another view of the connection of AI and SE adapted from Carleton et al 3 is presented in Figure 1.…”

Section: A Rtificial Intelligence (Ai)mentioning

confidence: 99%

Artificial Intelligence and Software Engineering: Are We Ready?

2022

Self Cite

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Section: A Rtificial Intelligence (Ai)mentioning

confidence: 99%

Artificial Intelligence and Software Engineering: Are We Ready?

2022

Self Cite

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“…Herein, the author reports the results of a study on the application of machine learning at various stages of the software development life cycle. Overall, [ 3 ] investigated the relationship between software development life cycle stages and machine learning tools, techniques, or types, which is a broad goal. In an attempt to answer the question of whether machine learning favors specific stages or methodologies, we conduct a comprehensive analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Each of these tasks necessitates searching for and working with a wide range of information and resources, as well as planning and preparing for the upcoming one [ 2 ]. A developer may investigate other code repositories for prospective solutions, explore online sites with relevant programming material, or contact coworkers for information before programming a possible solution to the problem at hand and testing the answer [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Trends in Intelligent and AI-Based Software Engineering Processes: A Deep Learning-Based Software Process Model Recommendation Method

Alshammari

2022

Computational Intelligence and Neuroscience

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In recent years, numerous studies have successfully implemented machine learning strategies in a wide range of application areas. Therefore, several different deep learning models exist, each one tailored to a certain software task. Using deep learning models provides numerous advantages for the software development industry. Testing and maintaining software is a critical concern today. Software engineers have many responsibilities while developing a software system, including coding, testing, and delivering the software to users via the cloud. From this list, it is easy to see that each task calls for extensive organization and preparation, as well as access to a variety of resources. A developer may consult other code repositories, websites with related programming content, and even colleagues for information before attempting to build and test a solution to the problem at hand. In this investigation, we aim to identify the factors that led to developing the recommender. This system analyzes the recommender’s performance and provides suggestions for improving the software based on users’ opinions.

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“…The evolution of these different life cycles has been in response to the increasing complexity of software, the systems for which new software are being designed, advancements in hardware, and the widespread use of software in society. Despite successful use of software globally, a strong emphasis on time to market has led to the incomplete application of many well-known SDLC recommendations, particularly those of requirement gathering, planning, specifications, architecture, design, and documentation ( 27 ). There are also inherent limitations to each of the SDLC models that can further contribute to poor software design ( Table 2 ).…”

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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A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Cited by 35 publications

References 257 publications

Artificial Intelligence and Software Engineering: Are We Ready?

Artificial Intelligence and Software Engineering: Are We Ready?

Trends in Intelligent and AI-Based Software Engineering Processes: A Deep Learning-Based Software Process Model Recommendation Method

An integration engineering framework for machine learning in healthcare

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