Управління розвитком (Nov 2023)
Hybrid approaches to machine learning in software development: Applying artificial intelligence to automate and improve processes
Abstract
The study on hybrid machine learning approaches is relevant because these approaches have great potential to improve predictive accuracy and software automation, and their use is becoming more widespread. The purpose of this study was to provide recommendations for the use of hybrid machine learning methods and analyse the areas of application of artificial intelligence, which is used to automate and improve processes. Problems related to hybrid approaches to machine learning were identified using the analytical method. The use of the statistical method allowed assessing the development of stability and performance of hybrid machine learning approaches. Features and differences of machine learning in the field of software development are noted. Errors and reasons that are made when improving development processes are analysed. It is established that a comprehensive analysis of the functioning of artificial intelligence is important to assess its effectiveness, development, and complexity of work in automation and improvement of development. The issues of evaluating the work of this type of approach, the expediency of their use, limitations in the process, and the impact of restrictions on the result are considered. It is determined that the use of artificial intelligence in the process of automation and improvement of development processes will improve the quality of resource optimisation. The study offers recommendations that will contribute to the effective regulation of this issue. The practical value of the study lies in the possibility of applying the results obtained to eliminate errors in the development and improvement of hybrid approaches, investigating the reliability of using artificial intelligence, considering various factors that serve as the basis for recommendations on appropriate use
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