AI is Overturning Common Sense in Code Quality and Debugging

For IT engineers, maintaining code quality and the debugging process are critical tasks that determine the success or failure of a project. However, as systems become more complex and the demand for development speed increases, the burden of these tasks continues to grow.

"I can't spare enough time for refactoring." "I spent hours trying to identify an unknown bug." Many developers have likely had these experiences.

The key to solving this deep-rooted challenge is the rapidly advancing use of "AI (Artificial Intelligence)." This article explains how AI is transforming code quality improvement and debugging tasks, specific new methods, and the skills required of engineers from now on.

New Trends in AI-Driven Code Quality Improvement

Traditionally, methods for ensuring code quality included establishing coding standards, pair programming, and checks using linters (static analysis tools). However, AI is further advancing and automating these methods.



1. High-Precision Code Reviews by AI

AI coding assistance tools, represented by GitHub Copilot and Amazon CodeWhisperer, do more than just complete code.

AI performs real-time reviews of the code written by developers. In addition to syntax errors and style violations detected by conventional linters, AI can point out potential bugs, performance bottlenecks, and security vulnerabilities.

Furthermore, AI learns the project's overall context and past revision history to propose "more efficient and maintainable ways of writing." This achieves both a reduction in review time and an improvement in code quality simultaneously.

2. Advanced Refactoring Support

It is not uncommon for technical debt to accumulate simply because "it's scary to touch working code." AI serves as a powerful supporter for such refactoring tasks.

By presenting an existing code block to the AI and giving instructions like "Make this process more efficient" or "Improve readability," the AI generates optimized code suggestions. Developers can safely and efficiently continue to enhance the "quality" of their code simply by evaluating and adopting these proposals.

The Era of "AI Assistants" for Debugging

One of the tasks that troubles developers the most is debugging. Especially in complex systems, identifying the cause of a bug is extremely difficult. AI is bringing a revolution to this debugging process as well.

1. Anomaly Detection and Root Cause Analysis from Vast Logs

It is impossible for humans to check all the vast log data output by a system. AI analyzes this log data at high speed, automatically detecting unusual patterns and signs of errors.

AI immediately identifies "differences from normal operation" and narrows down and presents the code sections or related processes where a bug might have occurred. This frees developers from guesswork and allows them to quickly get to the core of the problem.

2. Automatic Suggestion of Bug-Fixing Code

After the cause of a bug is identified, thinking of a fix is also one of AI's strengths. Based on error messages and related code snippets, the AI automatically generates a code (patch) proposal for the fix.

Of course, AI's suggestions are not always perfect, but they are extremely useful as a "first draft" for the fix. By reviewing and making minor adjustments to the AI's proposal, developers can dramatically shorten the time spent on debugging.

The Skills Engineers Must Truly Hone in the AI Era

As AI assists with coding and debugging, will engineers' jobs disappear? The answer is "No." Rather, the quality of the skills required will change.

AI is a powerful tool, but it is not infallible. It is the human (the engineer) who must ultimately judge whether the code generated by AI is truly correct and meets the project requirements, and who takes responsibility for it.

Engineers of the future will increasingly be required to have "prompting skills" to give appropriate instructions to AI and "critical thinking skills" to accurately evaluate and review AI's proposals without taking them at face value. The ability to master AI not as a "threat" but as "the ultimate assistant to boost productivity" will be the mark of a high-value engineer in the market.

Conclusion

The evolution of AI technology is removing "simple tasks" and "inefficient time" from the development floor, creating an environment where engineers can concentrate on more creative and essential work.

AI-powered code reviews and debugging assistance are no longer things of the future. Actively learning these new methods and incorporating them into your daily work is directly linked to your own skill enhancement and career development.