Scientific Online Resource System

Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series

Using artificial intelligence in software development

Mariya Armyanova, Yanka Aleksandrova

Abstract

The AI use is also heavily influencing software development. The demands on software are many, including its continuous update and ever-shorter development cycles. There is a growing need for shorter development cycles, flexibility and meeting requirements without compromising software quality. AI can help meet these requirements in software development. AI supports these goals. AI tools aid software development by supporting almost all development activities. It aids testing by making it faster and more efficient. The main possibilities to support activities in the different phases of software production are presented, based on tools that are available on the market and prototypes exploring the possible AI applications. The research purpose is to study the modern capabilities of AI in the domain of software production, its application limitations and to determine its role and place in software production.


Keywords

artificial intelligence, software development, software engineering, code generation, requirements gathering, quality control, feedback management

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References

Aleksandrova, Y. (2021) Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending. TEM Journal. vol. 10, is. 1, pp. 133‐143.

Becker, K., Gottschlich, J. (2021) AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms. GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion. 08 July 2021. pp.1513–1521.

Bruyn, А., (2020). Artificial Intelligence and Marketing: Pitfalls and Opportunities, Journal of Interactive Marketing. vol. 51. August 2020. pp. 91-105.

Filipova, N., Parusheva, S., Aleksandrova, Y. (2017) Fundamentals of information systems.

Science and Economics. Varna.

Grewal, D., et al., (2020). Frontline cyborgs at your service: How human enhancement technologies affect customer experiences in retail, sales, and service settings, Journal of Interactive Marketing. vol. 51, pp. 9–25.

IBM. (2023a) IBM Plans to Make Llama 2 Available within its Watsonx AI and Data Platform. [Online] Available from: https://newsroom.ibm.com/2023-08-09-IBM-Plans-to-Make-Llama-2- Available-within-its-Watsonx-AI-and-Data-PlatformAccessed 27/11/2023].

IBM. (2023b) IBM Unveils watsonx Generative AI Capabilities to Accelerate Mainframe Application Modernization. [Online] Available from: https://newsroom.ibm.com/2023-08-22- IBM-Unveils-watsonx-Generative-AI-Capabilities-to-Accelerate-Mainframe-Application- Modernization [Accessed 27/11/2023].

Iftikhar, H. (2023) The Role of AI in Software Engineering in 2023: Where is the Tech Industry Heading? [Online] Available from: https://medium.com/tech-lead-hub/the-role-of-ai-in- software-engineering-in-2023-where-is-the-tech-industry-heading-87185022dffa [Accessed 27/11/2023].

Intellectsoft. (2023) Benefits and Perspectives of Artificial Intelligence in Software Development. [Online] Available from: https://www.intellectsoft.net/blog/benefits-and- perspectives-of-artificial-intelligence-in-software-development/ [Accessed 27/11/2023].

J.P. Morgan. (2023) J.P. Morgan AI Research Awards. [Online] Available from: https://www.jpmorgan.com/technology/artificial-intelligence/research-awards [Accessed 27/11/2023].

Kniahynyckyj, R. (2021) The pros and cons of AI in marketing. [Online]. Available from: https://business.twitter.com/en/blog/the-pros-cons-ai-in-marketing.html [Accessed 12/11/2021].

Lewowski, T., Madeyski, L. (2022) Code smells detection using artificial intelligence techniques: A business-driven systematic review. Developments in Information I& Knowledge Management for Business Applications. pp.285–319.

Li, Z., Zou, D., Tang, J., Zhang, Z., Sun, M. Jin, H. (2019) A Comparative Study of Deep Learning-Based Vulnerability Detection System. IEEE Access 7, 2019. pp.103184–103197.

Nacheva, R., Sulova, S., Penchev, B. (2021) Where Security Meets Accessibility: Mobile Research Ecosystem. International Conference on Electronic Governance and Open Society: Challenges in Eurasia. Springer International Publishing. pp.216-231.

Pandey, S., Mishra, R, Tripathi, A. (2021) Machine learning based methods for software fault prediction: A survey, Expert Systems with Applications 172. p.114595.

Petrov, P., Sulova, S., Radev, M., Aleksandrova, Y., Mileva, L., Yankov, P. (2020) Digitization of business processes in construction and logistics. Knowledge and business. book 8.

Statista. (2023) Artificial Intelligence – Worldwide. [Online]. Available from: https://www.statista.com/outlook/tmo/artificial-intelligence/worldwide [Accessed 27/11/2023].

Stevenson, C., Smal, I., Baas, M., Grasman, R., van der Maas, H. (2022) Putting GPT3’s Creativity to the (Alternative Uses) Test. In International Conference on Computational Creativity (ICCC) 2022.

Thormundsson, B. (2022) Artificial intelligence software market revenue worldwide 2018-2025. [Online] Available from: https://www.statista.com/statistics/607716/worldwide-artificial- intelligence-market-revenues/ [Accessed 27/11/2023].

Varma, К. (2023) How AI is Automating Requirement Gathering and Documentation: A Perspective from an AI Tool. Available from: https://www.linkedin.com/pulse/how-ai- automating-requirement-gathering-documentation-kiron-varma/ [Accessed 27/11/2023].

Zhang, Y. (2021) Artificial intelligence in marketing: History, definition, types, examples and benefits. [Online]. Available from: https://hapticmedia.com/blog/artificial-intelligence-in- marketing-definition-example-benefit/ [Accessed 02/11/2021].


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