Developers Embrace AI Tools but Express Concerns Over Code Quality and Context Insights

In a recently published survey by Qodo, a leading AI coding platform, over 600 developers shared insights that highlight a paradox in the ongoing embrace of artificial intelligence in software development. While an astounding 82% of respondents reported using AI coding tools frequently, trust in the quality of the code generated by these tools remains tenuous.

Key Findings from the Survey

The 'State of AI Code Quality 2025' report offers data that reveals the dual nature of AI's impact on developers. On one hand, 78% of those surveyed acknowledged increases in productivity resulting from AI tools. However, a significant 76% admitted to a lack of complete trust in the efficacy and accuracy of the code produced.

Developers expressed that while they benefited from the productivity enhancements of AI tools, they also identified vital challenges. A striking 65% highlighted that these AI assistants often miss critical context necessary for tasks such as refactoring, writing tests, and reviewing code. Such shortcomings could potentially undermine the very effectiveness that AI tools aim to deliver.

The Landscape of AI Tool Utilization

Interestingly, the survey revealed that the AI coding tool ecosystem is not monopolized by a single entity, with 59% of developers indicating they employed three or more different AI tools in their workflows. This diversity suggests that developers are searching for the right solutions to match their unique coding demands.

Despite the noted drawbacks, the overall sentiment regarding job satisfaction appears positive, with 57% of developers stating that AI enhances their enjoyment at work and alleviates stress. In stark contrast, only 20% reported feelings of increased burnout due to AI tool integration.

AI-Driven Code Review: A Turning Point?

One of the report's most revealing insights is the correlation between AI code reviews and higher code quality. Teams that implement AI-driven code reviews saw a remarkable 35% increase in code quality improvements over those that do not use such automated systems. This phenomenon indicates a potential 'Confidence Flywheel' effect, in which higher quality leads to increased trust in AI-generated outputs, facilitating a faster and more effective software development cycle.

Disparities Between New and Experienced Developers

Notably, the experience level of developers played a role in their perceptions of AI-generated code. Senior developers, those with ten or more years in the field, derived the most significant perceived quality benefits from AI (68.2%) but demonstrated a cautious outlook when it came to deploying AI-generated code without review. Conversely, junior developers, typically with less than two years of experience, reported lower quality improvements (51.9%) but exhibited a higher degree of confidence (60.2%) in releasing AI-generated code without thorough checks.

This finding underscores an essential reality: experienced developers possess a more nuanced understanding of AI capabilities, leading to greater caution in trusting AI outputs. In contrast, less experienced developers may overestimate the reliability of AI assistance, suggesting the importance of education and experience in utilizing these tools effectively.

Conclusion

The Qodo survey paints a complex picture of the evolving relationship between developers and AI coding tools. While the potential for productivity gains is evident, the underlying concerns regarding trust and context are pressing for the future of AI in coding. As the landscape of software development continues to evolve, integrating AI with traditional quality assurance practices appears to be a vital strategy for fostering trust and efficiency within development teams.

About Qodo
Founded in 2018, Qodo has established itself as a significant player in AI-assisted software development, offering tools that help ensure code quality among teams. The company is at the forefront of combining AI with human expertise to address complex coding challenges effectively.