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An Efficient DAGbM-KSJS Algorithm for Agile Software Testing


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DOI: https://doi.org/10.15866/irecos.v11i10.10426

Abstract


Agile software testing is a software testing practice that follows the principles of agile software development. In this paper, an optimal Agile software testing is performed using Directed Acyclic Graph-based Model (DAGbM). Initially, the suggested model pre-processes the test case dataset, then by deploying the Dependency Assessment for Use Case (DAUC) algorithm, the dependency between the uses of cases are determined. The K-Shingling based Jaccard Similarity (KSJS) algorithm estimates the similarity among every test cases and prioritizes the clustered test cases using Span Clustering based Prioritization (SCP) algorithm. After prioritizing the clustered test cases, the use cases are prioritized based on dependency. Finally, the minimum distance value is exploited for prioritizing the individual test cases. The performance of the suggested method is validated using parameters such as code average, failure rate, prioritization time, and percentage of defects detected. The validation results prove that when compared to the existing methods, the suggested method provides optimal results for all the parameters.
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Keywords


Agile Software Testing; Directed Acyclic Graph-Based Model (Dagbm); Dependency Assessment for Use Case (DAUC) Algorithm; K-Shingling Based Jaccard Similarity (KSJS) Algorithm; Span Clustering Based Prioritization (SCP) Algorithm

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