Year: 2025
Type: dissertation
Study design: Observational Study
Abstract: The introduction of artificial intelligence (AI) technologies into software development has transformed pro gramming activities. AI-based coding assistants, such as GitHub Copilot, Tabnine, and Codeium, support de velopers by generating code and providing contextual recommendations in real time. This research anal yses how these tools impact productivity for developers with different levels of skills and varying degrees of task complexity. A mixed-methods design was adopted wherein quantitative data was collected from 100 developers through structured surveys, and qualitative data was gathered through 10 semi-structured interviews. The thesis studied the effect of four independent variables on the dependent variable of developer produc tivity: the quality of code suggestions, task complexity, tool user-friendliness, and the user’s level of exper tise. The mean scores of all constructs suggested strong, positive perceptions concerning the usefulness of AI tools, as captured through descriptive statistics. Correlation analysis indicated all independent variables to have significant relationships with productivity; in particular, the quality of code suggestions had the highest correlation (r = .798). Subsequent analysis via multiple regression confirmed only the quality of suggestion to be a significant predictor of productivity (β = 1.162, p < .001). Suggestion usability, suggesting expert level, and task complexity while being controlled for each other exhibited varying or no significant impacts. Thematic analysis pointed to user trust, efficiency, and learning as primary advantages, while focus on over dependence, security, and ethics of AI coding raised other concerns. The results substantiated that AI driven code assistants boost productivity when suggestions are precise and the integration of tools is seam less. In addition, thoughtful design and monitoring are necessary to mitigate addiction and sustain software standards.
Source: Theseus (Ammattikorkeakoulujen)
Author: Thaw, Thin Thu Thu
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7047738039