Citation:

Satyadhar Kumar Chintagunta , 2024. Generative AI Approaches to Automated Unit Test Case Generation in Large-Scale Software Projects , ESP Journal of Engineering & Technology Advancements 4(1): 150-157.

Abstract:

Automated test case generation is also supposed to complement software testing by reducing the level of manual effort required in test case generation. The SDLCs need experienced professionals who have knowledge of the domain at each level. Skill determines the quality of output and efficiency of every phase. Software testing is a process that belongs to the software development life cycle (SDLC) and has a role of testing the accuracy, reliability, and performance of the product. Traditional forms of testing may be tedious, resource-consuming, and give results only of a section of the system which is especially problematic with multifaceted and dynamic systems. Generative AI (GenAI) and artificial intelligence (AI) offer solutions that are game changers in software testing by automation of the process of test case creation, the discovery of problems, and the coverage of edge cases. GenAI models, including autoencoders, VAEs, GANs, and LSTM-based networks, allow intelligent and adaptable testing and scalable, as well as reduced human error and bias. The paper reviews AI-based testing techniques, the use of GenAI to enhance the quality of the code, and discusses the problems, limitations and opportunities of applying AI-based testing in the modern software development.

References:

[1] M. Tufano, D. Drain, A. Svyatkovskiy, S. K. Deng, and N. Sundaresan, “Unit Test Case Generation with Transformers and Focal Context,” May 2021, doi: 10.48550/arXiv.2009.05617.

[2] X. Guo, H. Okamura, and T. Dohi, “Automated Software Test Data Generation With Generative Adversarial Networks,” IEEE Access, vol. 10, pp. 20690–20700, 2022, doi: 10.1109/ACCESS.2022.3153347.

[3] A. Fontes, G. Gay, F. G. de O. Neto, and R. Feldt, “Automated Support for Unit Test Generation: A Tutorial Book Chapter,” pp. 1–29, Oct. 2021, doi: 10.48550/arXiv.2110.13575.

[4] V. S. Thokala, “Utilizing Docker Containers for Reproducible Builds and Scalable Web Application Deployments,” Int. J. Curr. Eng. Technol., vol. 11, no. 6, pp. 661–668, 2021, doi: 10.14741/ijcet/v.11.6.10.

[5] A. Mustafa et al., “Automated Test Case Generation from Requirements: A Systematic Literature Review,” Comput. Mater. Contin., vol. 67, no. 2, pp. 1819–1833, 2021, doi: 10.32604/cmc.2021.014391.

[6] D. Thakur, A. Mehra, R. Choudhary, and M. Sarker, “Generative AI in Software Engineering: Revolutionizing Test Case Generation and Validation Techniques,” Iconic Res. Eng. Journals, vol. 7, no. 5, pp. 281–293, 2023.

[7] S. Wang, N. Shrestha, A. K. Subburaman, J. Wang, M. Wei, and N. Nagappan, “Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), IEEE, May 2021, pp. 1548–1560. doi: 10.1109/ICSE43902.2021.00138.

[8] H. P. Kapadia and K. B. Thakkar, “Generative AI for Real-Time Customer Support Content Creation,” J. Emerg. Technol. Innov. Res., vol. 10, no. 12, pp. i36–i43, 2023.

[9] M. Islam, F. Khan, S. Alam, and M. Hasan, “Artificial Intelligence in Software Testing: A Systematic Review,” in TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON), IEEE, Oct. 2023, pp. 524–529. doi: 10.1109/TENCON58879.2023.10322349.

[10] G. Modalavalasa, “The Role of DevOps in Streamlining Software Delivery: Key Practices for Seamless CI/CD,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 1, no. 12, pp. 258–267, Jan. 2021, doi: 10.48175/IJARSCT-8978C.

[11] J. Gao, C. Tao, D. Jie, and S. Lu, “Invited Paper: What is AI Software Testing? and Why,” in 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), IEEE, Apr. 2019, pp. 27–2709. doi: 10.1109/SOSE.2019.00015.

[12] Z. Khaliq, S. U. Farooq, and D. Khan, “Artificial Intelligence in Software Testing : Impact, Problems, Challenges and Prospect,” arXiv, 2022, doi: 10.48550/arXiv.2201.05371.

[13] A. S. Verma, A. Choudhary, and S. Tiwari, “Software Test Case Generation Tools and Techniques: A Review,” Int. J. Math. Eng. Manag. Sci., vol. 8, no. 2, pp. 293–315, Apr. 2023, doi: 10.33889/IJMEMS.2023.8.2.018.

[14] E. Daka and G. Fraser, “A Survey on Unit Testing Practices and Problems,” in 2014 IEEE 25th International Symposium on Software Reliability Engineering, IEEE, Nov. 2014, pp. 201–211. doi: 10.1109/ISSRE.2014.11.

[15] V. S. Thokala, “A Comparative Study of Data Integrity and Redundancy in Distributed Databases for Web Applications,” Int. J. Res. Anal. Rev., vol. 8, no. 4, pp. 383–389, 2021.

[16] D. S. N, S. D. S, D. Vijayasree, N. S. Roopa, and A. Arun, “A Review on the Process of Automated Software Testing,” no. September, 2022, doi: 10.48550/arXiv.2209.03069.

[17] V. Maheshwari and M. Prasanna, “Generation of Test Case using Automation in Software Systems – A Review,” Indian J. Sci. Technol., vol. 8, no. 35, Dec. 2015, doi: 10.17485/ijst/2015/v8i35/72881.

[18] H. S. Chandu, “Advancements in Asynchronous FIFO Design: A Review of Recent Innovations and Trends,” Int. J. Res. Anal. Rev., vol. 10, no. 02, pp. 573–580, 2023.

[19] L. Barrio, “Leveraging Generative AI for Scalable Automated Test Case Generation and Defect Detection.” 2023. doi: 10.13140/RG.2.2.22882.54726.

[20] C. Ebert and P. Louridas, “Generative AI for Software Practitioners,” IEEE Softw., vol. 40, no. 4, pp. 30–38, Jul. 2023, doi: 10.1109/MS.2023.3265877.

[21] A. Goyal, “Driving Continuous Improvement in Engineering Projects with AI-Enhanced Agile Testing and Machine Learning,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 3, pp. 1320–1331, Jul. 2023, doi: 10.48175/IJARSCT-14000T.

[22] A. Bandi, P. V. S. R. Adapa, and Y. E. V. P. K. Kuchi, “The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges,” Futur. Internet, vol. 15, no. 8, Jul. 2023, doi: 10.3390/fi15080260.

[23] A. R. Chan, “Generative AI : Automating Code and Unit Testing for Faster , High-Quality Development,” ICONIC Res. Eng. JOURNALS, vol. 6, no. 7, pp. 527–537, 2023.

[24] H. P. Kapadia, “Generative AI for Real Time Conversational Agents,” Int. J. Curr. Sci., vol. 13, no. 3, pp. 201–208, 2023.

[25] A. Aleti, “Software Testing of Generative AI Systems: Challenges and Opportunities,” in 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE), IEEE, May 2023, pp. 4–14. doi: 10.1109/ICSE-FoSE59343.2023.00009.

[26] S. Missaoui, S. Gerasimou, and N. Matragkas, “Semantic Data Augmentation for Deep Learning Testing Using Generative AI,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), IEEE, Sep. 2023, pp. 1694–1698. doi: 10.1109/ASE56229.2023.00194.

[27] A. Fan et al., “Large Language Models for Software Engineering: Survey and Open Problems,” in 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE), IEEE, May 2023, pp. 31–53. doi: 10.1109/ICSE-FoSE59343.2023.00008.

[28] V. A. de S. Junior, “A method and experiment to evaluate deep neural networks as test oracles for scientific software,” in Proceedings of the 3rd ACM/IEEE International Conference on Automation of Software Test, New York, NY, USA, NY, USA: ACM, May 2022, pp. 40–51. doi: 10.1145/3524481.3527232.

[29] N. Nigar, M. K. Shahzad, S. Islam, S. Kumar, and A. Jaleel, “Modeling Human Resource Experience Evolution for Multiobjective Project Scheduling in Large Scale Software Projects,” IEEE Access, vol. 10, pp. 44677–44690, 2022, doi: 10.1109/ACCESS.2022.3169596.

[30] H. Ebadi, M. H. Moghadam, M. Borg, G. Gay, A. Fontes, and K. Socha, “Efficient and Effective Generation of Test Cases for Pedestrian Detection - Search-based Software Testing of Baidu Apollo in SVL,” in 2021 IEEE International Conference on Artificial Intelligence Testing (AITest), IEEE, Aug. 2021, pp. 103–110. doi: 10.1109/AITEST52744.2021.00030.

Keywords:

Software Testing, Artificial Intelligence (AI), Generative AI (GenAI), Test Automation, Software Quality Assurance.