Application directions of artificial intelligence in software development technologies
DOI:
https://doi.org/10.20535/2411-1031.2024.12.2.315741Keywords:
artificial intelligence, software development technologies, software engineering, IT project management, artificial intelligence application modelAbstract
The article presents the results of a systematic analysis of the current state of application of artificial intelligence (AI) in software engineering (SW) based on the analysis of publications, assessment of AI capabilities, experience in its application, and conducted experiments. The conceptual foundations of the research were formed, which determine: perception of AI as a tool, not an individual of work; the main directions of its application are engineering and management; the subject of AI application is the processing of artifacts (synthesis and analysis) and obtaining consultations; the need to assess the quality of AI-derived products and analyze the risks of its use is emphasized. Directions of application of AI in management: agreement processes (development of product concept and contract), organizational processes (project group formation and selection of technologies) and project management (planning, risk management, control and analysis of project implementation) Directions of application of AI in engineering: requirements management, design, construction, testing and documenting. To systematize the analysis of AI application directions, a conceptual model was developed, which includes: the direction, subject, and mode of application of AI. The mode of application of AI: the format of the prompt (problem statement and set of input data), the required product and its type (finished product, prototype, template, solution options, information support), the role of AI (executor, co-author, consultant), form of AI interaction (external service, integration via API, integrated system or local autonomous system). A structure of derivative models was formed for the analysis of the application of AI in specific directions with an overview of the capabilities of the most effective AI tools. As conclusions, it was determined that in management, the most rational model of using AI is to receive consultations and prototypes of documentation when contacting external AI services, in engineering – creating prototypes of project solutions and documentation based on external services, using integrated AI systems for design and testing in co-authorship mode. The risks of using AI include the possibility of obtaining insufficiently detailed documentation, complex and confusing software artifacts, and errors in the software code. To reduce risks and increase the effectiveness of AI application, it is determined that constant quality control of its products and training based on corporate requirements and standards is required.
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