Open Access

AI-Enabled Test Case Generation and Optimization for Modern Software Development

https://doi.org/10.55640/ijnget-v03i08-08

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Dinesh Perera ¹ , Nethmi Fernando ¹ ,

⁴ Department of Artificial Intelligence, Institute of Computing Sciences, Colombo, Sri Lanka

⁴ Department of Computer Science, Center for Intelligent Technology Research, Kandy, Sri Lanka

Abstract

Modern software systems are characterized by continuous integration, frequent releases, heterogeneous architectures, and increasingly complex interaction patterns. These conditions place substantial pressure on conventional test-case design, particularly where manually authored tests struggle to achieve adequate coverage within constrained development cycles. This research examines an AI-enabled approach to test-case generation and optimization for modern software development by synthesizing evidence from studies concerning augmented reality, simulation-based learning, computational visualization, embedded-system monitoring, and AI-driven software quality engineering. The proposed methodology conceptualizes test generation as a pipeline comprising requirement interpretation, test-objective identification, candidate test generation, execution-oriented prioritization, redundancy reduction, and continuous optimization. Particular emphasis is placed on the relationship between intelligent automation and software quality engineering, where AI-driven frameworks can transform testing from a predominantly scripted activity into an adaptive quality-assurance process (Ramamurthy, 2023). The analysis indicates that AI can provide substantial benefits in generating diverse test scenarios, prioritizing high-value cases, and adapting test suites to changing software conditions. However, optimization effectiveness depends on the quality of requirements, training or heuristic signals, system observability, and validation mechanisms. The research therefore positions AI-enabled testing not as a replacement for engineering judgment but as an augmentation mechanism that improves scalability, coverage, and prioritization while retaining human oversight for critical decisions.

Keywords

Artificial Intelligence, Test Case Generation, Test Optimization, Software Testing

References

Aydoğdu, F., & Kelpšiene, M. (2021). Uses of augmented reality inpreschool education.International Technology and EducationJournal,5,11–20.https://files.eric.ed.gov/fulltext/EJ1312893.pdf

Bogusevschi, D., Muntean, C. H., & Muntean, G. M. (2020).Teaching and learning physics using 3D virtual learningenvironment: A case study of combined virtual reality andvirtual laboratory in secondary school.Journal of Computers inMathematics and Science Teaching,39,5–18.http://www.newtonproject.eu/wp-content/uploads/2019/07/SITE2019_DBogusevschi_CameraReady.pdf

Cai, S., Liu, C., Wang, T., Liu, E., & Liang, J. (2021). Effects oflearning physics using Augmented Reality on students’self-efficacy and conceptions of learning.British Journal ofEducational Technology,52, 235–251.https://doi.org/10.1111/bjet.13020

COMSOL Multiphysics 5.6. 2020. Available at:https://www.comsol.com/model/thermoelectric-cooler-30611

Cui, Y., Zhao, H., & Zhao, L. (2020). Design of greenhouse shuttercontroller based on STM8.Agricultural Technology andEquipment, 01, 21–23.

Garon, M., Boulet, P., Doironz, J., Beaulieu, L., & Lalonde, J.(2016). Real-time high resolution 3D data on the HoloLens.In2016 IEEE International Symposium on Mixed andAugmented Reality (ISMAR-Adjunct)(pp. 189–191).

Harun, Tuli, N., & Mantri, A. (2020). Experience Fleming’s rule inelectromagnetism using augmented reality: Analyzing impacton students learning.Procedia Computer Science,172,660–668.https://doi.org/10.1016/j.procs.2020.05.086

Hedenqvist, C., Romero, M., & Vinuesa, R. (2021). Improving thelearning of mechanics through augmented reality.TechnologyKnowledge Learning,https://doi.org/10.1007/s10758-021-09542-1

Huang, J. M., Ong, S.K., & Nee, A. Y. C. (2017). Visualization andinteraction of finite element analysis in augmented reality.Computer-Aided Design,84,1–14.https://doi.org/10.1016/j.cad.2016.10.004

Kim, Y., Kim, H., Lee, S., & Kim, W. (2021). Trustworthy buildingfire detection framework with simulation-based learning.IEEEAccess.https://doi.org/10.1109/ACCESS.2021.3071552

Liu, W. (2021). Design of wireless low-power temperature and humiditymonitoring terminal based on STM8.Electrotechnics,18,42–44.https://doi.org/10.19.768/j.cnki.dgjs.2021.18.015

Long,X.,Chen,Y.,&Zhou,J.(2021).Augmentedrealityexperimentonmechanical stress of block on arch with simulation-based asset. In2021 4th International Conference on Information Systems andComputer Aided Education, September 24–26, 2021, Dalian,China. ACM, New York, NY, USA, 5 pages.

Lu, Y., Chen, J., Li, J., & Xu, W. (2022). A study on theelectromagnetic–thermal coupling effect of cross-slotfrequency selective surface. Materials, 15, 640.https://doi.org/10.3390/ma15020640

Mota, J. M., Rube, I. R., Dodero, J. M., & Sánchez, I. A. (2018).Augmented reality mobile app development for all.Computers and Electrical Engineering,65, 250–260.https://doi.org/10.1016/j.compeleceng.2017.08.025

Ramamurthy, K. (2023). AI-Driven Test Automation Frameworks for the Modern Software Quality Engineering. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 257-269.

Philip, P. G. (2024). Artificial Intelligence-Driven Project Risk Prediction Models: Enhancing Decision-Making Accuracy in Large-Scale Infrastructure Projects . American Journal of Technology, 3(1), 52–69. https://doi.org/10.58425/ajt.v3i1.571

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