A new report by software engineering platform GitClear has raised concerns about the impact of AI coding assistants on code quality. According to the report, which analyzed 211 million code lines from 2020 to 2024, there was a remarkable decline in code reuse last year. This decline is particularly noteworthy, as code reuse is a common practice that helps build redundant systems.

The report's findings suggest that AI coding assistants, such as GitHub Copilot, may be contributing to this decline in code quality. While these tools are designed to boost productivity, they may be having the opposite effect. Instead of producing high-quality code, they may be generating code that requires more time and effort to debug and maintain.

This is not the first time that concerns have been raised about the quality of code produced by AI coding assistants. Several recent surveys have shown that these tools tend to produce mixed results. For example, a survey by software vendor Harness found that the majority of developers spend more time debugging AI-generated code and security vulnerabilities compared to human-written contributions.

A Google report also highlighted the limitations of AI coding assistants. While the report found that AI can quicken code reviews and benefit documentation, it noted that this comes at the cost of delivery stability. This suggests that the use of AI coding assistants may be trading off short-term gains in productivity for long-term problems with code quality.

The decline in code reuse is particularly concerning, as it is a key practice that helps to ensure the reliability and maintainability of software systems. By reusing code, developers can avoid duplicating effort and reduce the risk of errors. The fact that code reuse is declining suggests that AI coding assistants may be undermining this important practice.

The implications of this report are significant. As AI coding assistants become more widespread, there is a risk that code quality will continue to decline. This could have serious consequences for the software industry as a whole, leading to more bugs, security vulnerabilities, and system failures. It is therefore essential that developers and organizations take a critical approach to the use of AI coding assistants, and ensure that they are not sacrificing code quality for short-term gains in productivity.

In conclusion, the report by GitClear highlights the need for caution in the adoption of AI coding assistants. While these tools may offer some benefits, they also pose significant risks to code quality. As the software industry continues to evolve, it is essential that we prioritize the development of high-quality code, and ensure that the use of AI coding assistants does not undermine this goal.