Review of an Agile Software Development Methodology with SCRUM &amp; Extreme Programming

Sankhe, Purvi; Mathur, Shruti; Rehman, Tasneem Bano; Dixit, Mukesh

doi:10.1109/ccet56606.2022.10080640

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Each iteration of agile development entails communication, plan, design, development, testing, and releasing a tiny, working piece of software (10) . This approach is iterative and incremental.…”

Section: Fig 2 Agile Software Development Approachmentioning

confidence: 99%

Analysis of Traditional and Agile Software Development Process for Developing Recommender Model using Machine Learning

Sankhe,

Dixit

2024

IJST

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Objective: To create an AI-powered recommendation system that is designed for IT professionals to help them choose the best software development approaches. Through the use of specified data parameters. Methods: The recommendation system will make use of machine learning algorithms and data analysis methods to examine team dynamics, project needs, and other variables. The technology will enable developers to improve the quality of products and speed up the development process by recommending suitable development methodologies. Data parameters considered for the development of the recommendation model fall into four categories: requirements, user involvement, development team, type of project, and risk associated with it. Findings: Existing recommendation systems developed by different researchers are applicable for only requirement elicitation and to recommend different phases of the development process, whereas systems that will help select development methodology are not available in the existing systems. Among the five machine learning algorithms applied in the recommender system building process, the DecisionTree Classifier and RandomForest Classifier exhibit superior performance, achieving 100% accuracy, while the Kneighbors Classifier indicates 94.74% accuracy. Novelty: This study of systems introduces a novel approach to software development methodology, a recommender system, which helps IT developers select the best appropriate development approach for the development of a software product or project based on the type of project to be built and other data parameters. Keywords: Agile, Development, Requirements, Methodology, User, Customer

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Section: Fig 2 Agile Software Development Approachmentioning

confidence: 99%

Analysis of Traditional and Agile Software Development Process for Developing Recommender Model using Machine Learning

Sankhe,

Dixit

2024

IJST

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“…During these development sprints, there were a range of between meeting interactions along with the regular meetings. The use of agile methodology, where the intention is on 'integrate and test' (Sankhe et al, 2022), had begun to influence the willingness of the student development team to trial the industry partner's requirements, freeing them from the need to perfect or simply responsive. Deliberate interactions from the industry partner team encouraged the student team to own their technological expertise, while also building trust and safety in the working relationship.…”

Section: Sprint 1: Designmentioning

confidence: 99%

University learning partnerships

Samson,

Oliveira

2023

APUBS

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Universities hold a significant advantage in the development of enterprising learning partnerships where internal and intentional collaboration can assist in addressing complex problems. Building a collaborative learning partnership between faculties enhances learning, removes blocks to enterprise and addresses industry problems. The learning partnership developed in this collaboration included academic staff from two different faculties, a team of students as well as input from external stakeholders including schools and pre-service teacher candidates. The approach included digital expertise and developmental pedagogies; with a shared outcome designed to meet the enterprise goals of the industry’s partner. The student team developed confidence and capacity in their ability to communicate with the industry partner as they were encouraged to be creative, equal participants with the agency to take risks and problem solve. The tangible outcome was the delivery of a minimum viable product that has potential to address issues around teacher shortage and other limits to school resourcing. Learning partnerships within universities facilitate authentic learning and encouraged enterprise.

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Integrating Generative AI and IoT for Sustainable Smart Tourism Destinations

Suanpang,

Pothipassa

2024

Sustainability

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This paper aims to develop a groundbreaking approach to fostering inclusive smart tourism destinations by integrating generative artificial intelligence (Gen AI) with natural language processing (NLP) and the Internet of Things (IoT) into an intelligent platform that supports tourism decision making and travel planning in smart tourism destinations. The acquisition of this new technology was conducted using Agile methodology through requirements analysis, system architecture analysis and design, implementation, and user evaluation. The results revealed that the synergistic combination of these technologies was organized into three tiers. The system provides information, including place names, images, descriptive text, and an audio option for users to listen to the information, supporting tourists with disabilities. Employing advanced AI algorithms alongside NLP, developed systems capable of generating predictive analytics, personalized recommendations, and conducting real-time, multilingual communication with tourists. This system was implemented and evaluated in Suphan Buri and Ayutthaya, UNESCO World Heritage sites in Thailand, with 416 users participating. The results showed that system satisfaction was influenced by (1) the tourism experience, (2) tourism planning and during-trip factors (attention, interest, and usage), and (3) emotion. The relative Chi-square (χ2/df) of 1.154 indicated that the model was suitable. The Comparative Fit Index (CFI) was 0.990, the Goodness-of-Fit Index (GFI) was 0.965, and the model based on the research hypothesis was consistent with the empirical data. This paper contributions significant advancements in the field of smart tourism by demonstrating the integration of Gen AI, NLP, and the IoT and offering practical solutions and theoretical insights that enhance accessibility, personalization, and environmental sustainability in tourism.

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Review of an Agile Software Development Methodology with SCRUM & Extreme Programming

Cited by 3 publications

References 11 publications

Analysis of Traditional and Agile Software Development Process for Developing Recommender Model using Machine Learning

Analysis of Traditional and Agile Software Development Process for Developing Recommender Model using Machine Learning

University learning partnerships

Integrating Generative AI and IoT for Sustainable Smart Tourism Destinations

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