Enhancing integration testing efficiency through AI-driven combined structural and textual class coupling metric

Iyad Alazzam 1 * , Anas Mohammad Ramadan AlSobeh 1 * , Basil Bani Melhem 1
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1 Department of Information Systems, Faculty of Information Technology and Computer Science, Yarmouk University, Irbid, JORDAN
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 14, Issue 4, Article No: e202460. https://doi.org/10.30935/ojcmt/15524
OPEN ACCESS   190 Views   116 Downloads   Published online: 28 Oct 2024
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ABSTRACT

Integration testing, a critical and resource-intensive phase in the software development lifecycle, can account for up to a high percentage of the total testing cost. Identifying classes with high coupling is crucial for efficient integration testing, as these classes are more susceptible to the impact of maintenance-related changes. This research introduces a novel metric called combined structural and textual class coupling (CSTCC), which harnesses the power of artificial intelligence (AI) techniques to predict and rank the most critical classes in an object-oriented software system. CSTCC integrates structural coupling metrics with latent semantic indexing (LSI)-based textual coupling, providing a comprehensive measure of class coupling. LSI, an information retrieval technique, analyses the semantic relationships between classes based on their textual content, enabling CSTCC to capture both structural and conceptual dependencies, resulting in a more accurate identification of high-risk classes. The effectiveness of the proposed approach is rigorously evaluated using mutation testing on four Java open-source projects, and the results demonstrate that test cases developed based on CSTCC achieve high mutation scores, indicating their ability to detect a significant percentage of integration faults. By focusing testing efforts on high-coupling classes identified by CSTCC, developers can potentially save time and cost during integration testing. The results demonstrate that test cases developed based on CSTCC achieve high mutation scores, ranging from 98% to 100%, indicating their ability to detect a significant percentage of integration faults. Additionally, the approach results in substantial efficiency gains, with a notable reduction in the number of test cases needed, saving up to 33.3% of the testing effort in some cases. By focusing testing efforts on high-coupling classes identified by CSTCC, developers can potentially save time and cost during integration testing. The CSTCC metric provides a novel and effective approach to prioritize testing resources and improve the efficiency of integration testing in object-oriented software systems.
Keywords: integration testing optimization, mutation testing, test cases, coupling, AI-driven software testing, latent semantic indexing, combined coupling metrics

CITATION

Alazzam, I., AlSobeh, A. M. R., & Bani Melhem, B. (2024). Enhancing integration testing efficiency through AI-driven combined structural and textual class coupling metric. Online Journal of Communication and Media Technologies, 14(4), e202460. https://doi.org/10.30935/ojcmt/15524

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