Artificial Intelligence-Based Tools in Software Development Processes: Application of ChatGPT

Year 2023, , 229 - 240, 31.12.2023

Zeynep Özpolat , Özal Yıldırım , Murat Karabatak

https://doi.org/10.36222/ejt.1330631

Abstract

Software development processes are continuously evolving and rapidly transforming alongside the rapid changes in technology. Recently, innovations in the field of Artificial Intelligence (AI) have led to significant changes in software development practices. AI tools can greatly enhance traditional software development processes by offering developers the ability to create projects more intelligently, swiftly, and effectively. These tools can be employed in various tasks, such as code generation, test automation, error analysis, and performance improvements. Particularly, ChatGPT, an AI-based language model that has had a profound impact on almost every domain, can assist software developers in writing code faster and in a more natural language manner. In this research article, essential information about the usage of ChatGPT in the software development process is presented. To evaluate some capabilities of ChatGPT in the software development context, applications were performed on a software project. For this purpose, a software development process was constructed based on the responses provided by ChatGPT. Various questions related to software development processes were formulated, and the responses generated by GPT were evaluated. The obtained results indicated that ChatGPT exhibited excellent performance in the software development process. Based on these findings, it was observed that AI-based models like ChatGPT could be effectively utilized as assisting tools in software development processes, accelerating traditional workflows. Furthermore, AI-based tools can automate testing processes, enhancing software quality while saving time and effort.

Keywords

Artificial Intelligence, Software Development, ChatGPT-4, AI tools

References

• [1] I. Sommerville, Engineering Software Products: An Introduction to Modern Software Engineering. Pearson, 2019.
• [2] B. W. Boehm, “A spiral model of software development and enhancement,” Computer, vol. 21, no. 5, pp. 61–72, May 1988, doi: 10.1109/2.59.
• [3] M. U. Cheema and Q. Zearlish, “The Choice of Project Management Software by Project Managers; with the Moderating Impact of Top Management Support,” vol. 2, no. 1.
• [4] E. Nascimento, A. Nguyen-Duc, I. Sundbø, and T. Conte, “Software engineering for artificial intelligence and machine learning software: A systematic literature review”.
• [5] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language Models are Unsupervised Multitask Learners”.
• [6] A. Ahmad, M. Waseem, P. Liang, M. Fahmideh, M. S. Aktar, and T. Mikkonen, “Towards Human-Bot Collaborative Software Architecting with ChatGPT,” in Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, Oulu Finland: ACM, Jun. 2023, pp. 279–285. doi: 10.1145/3593434.3593468.
• [7] O. Katar, D. Özkan, G. -3, Ö. Yildirim, and U. R. Acharya, “Evaluation of GPT-3 AI Language Model in Research Paper Writing,” Turkish Journal of Science and Technology, Jun. 2023, doi: 10.55525/tjst.1272369.
• [8] M. A. Akbar, A. A. Khan, and P. Liang, “Ethical Aspects of ChatGPT in Software Engineering Research.” arXiv, Jun. 13, 2023. Accessed: Jul. 14, 2023. [Online]. Available: http://arxiv.org/abs/2306.07557
• [9] M. Fraiwan and N. Khasawneh, “A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions”.
• [10] R. A. Carter, A. I. Anton, A. Dagnino, and L. Williams, “Evolving beyond requirements creep: a risk-based evolutionary prototyping model,” in Proceedings Fifth IEEE International Symposium on Requirements Engineering, Aug. 2001, pp. 94–101. doi: 10.1109/ISRE.2001.948548.
• [11] A. C. Nelson and J. T. C. Teng, “Do systems development methodologies and CASE tools decrease stress among systems analysts?,” Behaviour & Information Technology, vol. 19, no. 4, pp. 307–313, Jan. 2000, doi: 10.1080/01449290050086417.
• [12] W. Model, “Waterfall model,” Luettavissa: http://www. waterfall-model. com/. Luettu, vol. 3, 2015.
• [13] K. Rasheed, M. Imran, M. Noman, and M. Iqbal, “A Study On Traditional And Evolutionary Software Development Models,” vol. 6, no. 07, 2017.
• [14] G. Kumar and P. K. Bhatia, “Comparative Analysis of Software Engineering Models from Traditional to Modern Methodologies,” in 2014 Fourth International Conference on Advanced Computing & Communication Technologies, Feb. 2014, pp. 189–196. doi: 10.1109/ACCT.2014.73.
• [15] G. Regulwar, P. Jawandhiya, V. Gulhane, and R. Tugnayat, “Variations in V Model for Software Development,” Jan. 2021.
• [16] A. K. M. Z. Islam and Dr. A. Ferworn, “A Comparison between Agile and Traditional Software Development Methodologies,” GJCST, pp. 7–42, Dec. 2020, doi: 10.34257/GJCSTCVOL20IS2PG7.
• [17] T. Clement, N. Kemmerzell, M. Abdelaal, and M. Amberg, “XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process,” Machine Learning and Knowledge Extraction, vol. 5, no. 1, Art. no. 1, Mar. 2023, doi: 10.3390/make5010006.
• [18] S. Russell, Artificial Intelligence: A Modern Approach, eBook, Global Edition. Pearson Education, Limited, 2016.
• [19] A. Holzinger, C. Biemann, C. S. Pattichis, and D. B. Kell, “What do we need to build explainable AI systems for the medical domain?” arXiv, Dec. 28, 2017. doi: 10.48550/arXiv.1712.09923.
• [20] F. A. Batarseh, R. Mohod, A. Kumar, and J. Bui, “10 - The application of artificial intelligence in software engineering: a review challenging conventional wisdom,” in Data Democracy, F. A. Batarseh and R. Yang, Eds., Academic Press, 2020, pp. 179–232. doi: 10.1016/B978-0-12-818366-3.00010-1.
• [21] S. Vemuri, S. Chala, and M. Fathi, “Automated use case diagram generation from textual user requirement documents,” in 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Apr. 2017, pp. 1–4. doi: 10.1109/CCECE.2017.7946792.
• [22] M. Latinovic and V. Pammer-Schindler, “Automation and Artificial Intelligence in Software Engineering: Experiences, Challenges, and Opportunities: The 54th Hawaii International Conference on System Sciences,” Proceedings of the 54th Annual Hawaii International Conference on System Sciences, HICSS 2021, pp. 146–155, 2021, doi: 10.24251/HICSS.2021.017.
• [23] A. Rafael Lenz, A. Pozo, and S. Regina Vergilio, “Linking software testing results with a machine learning approach,” Engineering Applications of Artificial Intelligence, vol. 26, no. 5, pp. 1631–1640, May 2013, doi: 10.1016/j.engappai.2013.01.008.
• [24] https://facebook.com/saiftheboss7, “The Future of AI in Software Development in 2023 and Beyond - Appsero,” Mar. 06, 2023. https://appsero.com/user-guide/ai-in-software-development/ (accessed Jul. 13, 2023).
• [25] H. Klaus, M. Rosemann, and G. G. Gable, “What is ERP?,” Information Systems Frontiers, vol. 2, no. 2, pp. 141–162, Aug. 2000, doi: 10.1023/A:1026543906354.