Evaluating the Effectiveness of Requirement Patterns for Data Intensive Application Development
Received: 12 April 2024 | Revised: 5 May 2024 | Accepted: 14 May 2024 | Online: 23 May 2024
Corresponding author: Margret Anouncia Savarimuthu
Abstract
In the rapidly evolving landscape of data-intensive applications, the precision and clarity of software requirements play a pivotal role in these applications’ development process. This research focuses on the validation of a specifically designed Transformation Requirement Pattern (TFReqPat) for data-intensive applications, such as banking, e-commerce, and healthcare. The main objective is to ascertain the completeness, correctness, and unambiguity of requirements captured using this novel pattern. Traditional approaches to requirement documentation often suffer from inconsistencies leading to the exploration of structured patterns that promise enhanced clarity and reuse. This article focuses on acceptable verification and validation procedures deployed as concrete methods to validate the adequacy of the captured requirements. Through the generation of stringent acceptance criteria, it is ensured that the documented requirements were adherent to developmental standards with fewer ambiguities. As per the proposed validation process, the correctness and completeness of the specified requirements were determined through the acceptance criteria that check for the presence of process, domain dependency, behavior, and storage details in the specifications. Accordingly, the obtained evaluation score was 93.1%, 88.5%, and 75.2% for correctness and 94.8%, 92.9%, and 76.4%, respectively, for completeness. These findings significantly underscore the suitability of the proposed pattern for data-intensive applications, marking it as a more efficient and effective methodology compared to ad-hoc reuse approaches. This article not only contributes a validated requirement pattern to the field but also highlights the importance of structured requirement documentation in enhancing software development outcomes for data-intensive domains.
Keywords:
transformation requirement pattern, requirement validation, acceptance criteria, correctness, completenessDownloads
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