Category of Work
Conference
Title of Conference/Lecture Series
The 22nd International Conference on Information Technology - New Generations (ITNG 2025)
Abstract
Software testing is a critical part of software development, it is essential for preventing failures and enhancing software quality attributes. However, the testing process can be costly and time-consuming, often involving a large number of test cases. Over time, the accumulation of redundant and overlapping test cases can complicate and lengthen the testing time. To address these challenges, this paper utilizes graph similarity and deep learning techniques to optimize test suites. It uses call graphs from test cases to identify redundant and similar test cases. A machine learning model is used to calculate and predict the similarity scores between these call graphs, helping to classify and prioritize the test cases. This helps rank test cases based on their similarity scores, with lower scores indicating higher priority due to their unique code coverage. This approach allows test engineers to focus on the most diverse set of test cases, ensuring comprehensive code coverage and efficient testing. By reducing the number of redundant test cases, this method aims to streamline the testing process, reduce costs, and maintain high software quality standards. Ultimately, this paper seeks to provide a systematic framework for test engineers to determine the optimal amount of testing needed to effectively meet the software quality objectives.
First Page
517
Last Page
527
DOI
https://doi.org/10.1007/978-3-031-89063-5_45
Presentation Date
4-2025
Recommended Citation
Al-Sharif, Ziad A.; Chintala, Hemanth G.; and Omari, Safwan, "The Use of Call Graphs and Deep Learning to Improve Software Testing" (2025). Engineering, Computing and Mathematical Sciences Faculty Conferences. 5.
https://digitalcommons.lewisu.edu/ecms_faccons/5
