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Software Maintainability Estimation in Agile Software Development

Software Maintainability Estimation in Agile Software Development

Parita Jain, Arun Sharma, Laxmi Ahuja
Copyright: © 2022 |Pages: 16
ISBN13: 9781668437025|ISBN10: 1668437023|EISBN13: 9781668437032
DOI: 10.4018/978-1-6684-3702-5.ch050
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MLA

Jain, Parita, et al. "Software Maintainability Estimation in Agile Software Development." Research Anthology on Agile Software, Software Development, and Testing, edited by Information Resources Management Association, IGI Global, 2022, pp. 1002-1017. https://doi.org/10.4018/978-1-6684-3702-5.ch050

APA

Jain, P., Sharma, A., & Ahuja, L. (2022). Software Maintainability Estimation in Agile Software Development. In I. Management Association (Ed.), Research Anthology on Agile Software, Software Development, and Testing (pp. 1002-1017). IGI Global. https://doi.org/10.4018/978-1-6684-3702-5.ch050

Chicago

Jain, Parita, Arun Sharma, and Laxmi Ahuja. "Software Maintainability Estimation in Agile Software Development." In Research Anthology on Agile Software, Software Development, and Testing, edited by Information Resources Management Association, 1002-1017. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-3702-5.ch050

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Abstract

Agile methodologies have gained wide acceptance for developing high-quality products with a quick and flexible approach. However, until now, the quality of the agile process has not been validated quantitatively. Quality being important for the software system, there is a need for measurement. Estimating different quality factors will lead to a quality product. Also, agile software development does not provide any precise models to evaluate maintainability. Therefore, there is a need for an algorithmic approach that can serve as the basis for estimation of maintainability. The article proposes an adaptive neuro-fuzzy inference system (ANFIS) model for estimating agile maintainability. Maintainability is one of the prominent quality factors in the case of agile development. The proposed model has been verified and found to be effective for assessing the maintainability of agile software.

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