Current applications and future prospects of artificial intelligence in software engineering

Zherong Ma

doi:10.54254/2977-3903/13/2024141

Research Article

Open access

Published on 19 November 2024

Download pdf

Ma,Z. (2024). Current applications and future prospects of artificial intelligence in software engineering. Advances in Engineering Innovation,13,71-75.

Export citation

Current applications and future prospects of artificial intelligence in software engineering

Zherong Ma *^,1,

¹ Guangzhou ULink International School

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2977-3903/13/2024141

Abstract

The advent of digital technology has spawned a revolution in software engineering, with artificial intelligence (AI) emerging as a key technology. The integration of advanced techniques, such as natural language processing (NLP) and deep learning has demonstrated AI’s remarkable capabilities throughout the software development lifecycle. Enhancements in areas such as code generation, code inspection, and software testing have significantly elevated both efficiency and quality. In addition, the potential of AI also provides new possibilities for automated software updates and maintenance in the future. However, despite the broad application prospects of AI in software engineering, it still faces some problems to be solved urgently. For example, inadequate adaptability and the challenges of personal data privacy protection limit its wider application. At the same time, the high research cost and immature model technology also bring obstacles to further development. By comprehensively analyzing the existing literature and related cases, this study deeply discusses the application status and limitations of AI in software engineering. The research results show that although AI can greatly improve the efficiency of software development, its shortcomings in data security and adaptability need attention. Future research should address these problems and seek more effective technical solutions to promote the sustainable development of AI in software engineering.

Keywords

artificial intelligence, software engineering, natural language processing, deep learning

View pdf

References

[1]. Wang, C.Y. (2013) Research on the Dartmouth Conference between the United States and the Soviet Union: 1960-1991. Shaanxi Normal University.

[2]. Wikipedia. (2024) History of artificial intelligence. https://en.wikipedia.org/wiki/History_of_artificial_intelligence

[3]. Nascimento, E.D., Nguyen-Duc, A., Sundbø, I. and Conte, T.U. (2020) Software engineering for artificial intelligence and machine learning software: A systematic literature review.

[4]. Zhou, F.S., Wang, L.Z. and Li, X.D. (2019) Automatic Defect Repair and Validation Approach for C/C++ Programs. Journal of Software, 30(5): 1243-1255.

[5]. Robert Feldt, Francisco G. de Oliveira Neto, and Richard Torkar. Ways of Applying Artificial Intelligence in Software Engineering (Ways of Applying Artificial Intelligence in Software Engineering (arxiv.org))

[6]. Feldt, R., de Oliveira Neto, F.G. and Torkar, R. (2018) Ways of Applying Artificial Intelligence in Software Engineering. International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, 35-41.

[7]. Lu, X.C, and Zhou, L.L. (2024) Program Code Defect Detection Based on Natural Language Processing. Information Recording Materials, 25 (08): 174-176.

[8]. Yang, Z.Z., Chen, S.R, Gao, C.Y., Li, Z.H., Li, G. and Lu, R.C. (2024) Deep Learning Based Code Generation Methods: Literature Review. Journal of Software, 35(2): 604-628.

[9]. Xie, T. (2018) Intelligent Software Engineering: Synergy Between AI and Software Engineering. In: Feng, X., Müller-Olm, M., Yang, Z. (eds) Dependable Software Engineering. Theories, Tools, and Applications. SETTA 2018. Lecture Notes in Computer Science, 10998.

[10]. Azeem, M.I., Palomba, F., Shi, L. and Wang, Q. (2019) Machine learning techniques for code smell detection: A systematic literature review and meta-analysis. Inf. Softw. Technol., 108: 115-138.

[11]. Gou, Q.W., Dong, Y.W. and Li, Y.M. (2024) Research progress of program synthesis based on deep learning. Journal of Computer Science, 1-36.

[12]. Wikipedia. (2024) Test case. https://en.wikipedia.org/wiki/Test_case

[13]. Zhang, C., Xu, H.C. and Luo, T.J. (2024) Research on the application of artificial intelligence technology in the field of software testing. Shanxi Science and Technology News.

[14]. Deng, X., Ye, W., Xie, R. and Zhang, S.K. (2023) Survey of Source Code Bug Detection Based on Deep Learning. Journal of Software, 34(2): 625-654.

[15]. Shen, K., Huang, K..F., Chen, B.H, et al. (2024) Python third-party library API compatibility problem detection method based on static analysis. Journal of Software, 1-26.

[16]. Meziane, F. and Vadera, S. (2009) Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects.

Cite this article

Ma,Z. (2024). Current applications and future prospects of artificial intelligence in software engineering. Advances in Engineering Innovation,13,71-75.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Journal：Advances in Engineering Innovation

Volume number: Vol.13

ISSN：2977-3903(Print) / 2977-3911(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).