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Published on 26 November 2024
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Zhang,Q. (2024). The Role of Artificial Intelligence in Modern Software Engineering. Applied and Computational Engineering,97,18-23.
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The Role of Artificial Intelligence in Modern Software Engineering

Qinbo Zhang *,1,
  • 1 Harbin Institute of Technology

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/97/20241339

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly influenced various industries, including software engineering. This paper explores the integration of AI into software engineering, focusing on its applications across different stages of the software development life cycle, including design, development, testing, project management, and maintenance. AI's ability to automate tasks, enhance efficiency, and improve code quality is revolutionizing how software is built and maintained. The paper also addresses the challenges and risks associated with AI-driven software engineering, such as dependency on AI tools, ethical concerns, and security vulnerabilities. Finally, the paper highlights future trends in AI-powered software engineering, including adaptive and self-healing systems, AI-enhanced collaboration, and full software automation. The role of AI in shaping the future of software engineering is both profound and transformative, making it a critical area of study.

Keywords

Artificial Intelligence (AI), Software Engineering, AI-driven Development, Automated Testing,Machine Learning.

[1]. Russell S and Norvig P 2020 Artificial Intelligence(A Modern Approach 4th ed Pearson)

[2]. Sommerville I 2019 Software Engineering(10th ed. Pearson)

[3]. Juristo N and Moreno A M 2019 Basics of Software Engineering Experimentation(Springer)

[4]. Amershi S et al 2019 "Guidelines for HumanAI Interaction." In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems.

[5]. Allamanis M et al 2018 "A Survey of Machine Learning for Big Code and Naturalness." ACM Computing Surveys, 51(4), 81.

[6]. DevOps Research and Assessment. 2019 State of DevOps Report.

[7]. Humble J and Farley D 2010 Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. AddisonWesley.

[8]. Ashmore R et al 2019 "Assuring the Safety of Machine Learning for Autonomous Systems." ACM Computing Surveys, 51(4), 79.

[9]. Nguyen A Yosinski J and Clune J 2015 "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

[10]. Varshney K R 2016 "Engineering Safety in Machine Learning." In Proceedings of the 2016 International Conference on Dependable Systems and Networks.

[11]. Xing Ying Overview of Software Engineering Technology Methods Based on Interpretable Artificial Intelligence [J]. Computer Science, 2023, 50 (05): 3-11

[12]. Zhou Yong Di et al Research on Artificial Intelligence Automation Testing Methods for Software Engineering [J]. Information Recording Materials, 2023, 24 (11): 115-119

[13]. Chen Li. Application of Artificial Intelligence in Computer Software Development [J]. Information and Computer (Theoretical Edition), 2023, 35 (12): 32-35

Cite this article

Zhang,Q. (2024). The Role of Artificial Intelligence in Modern Software Engineering. Applied and Computational Engineering,97,18-23.

Data availability

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

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

Conference website: https://2024.confmla.org/
ISBN:978-1-83558-673-0(Print) / 978-1-83558-674-7(Online)
Conference date: 21 November 2024
Editor:Mustafa ISTANBULLU
Series: Applied and Computational Engineering
Volume number: Vol.97
ISSN:2755-2721(Print) / 2755-273X(Online)

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