Code quality in game development is no longer a matter solely of how code looks or of developers’ preferences. According to a presentation that JetBrains republished after the Game Development Day 2026 event, quality encompasses six interconnected dimensions: correctness of results, performance, stability, security, maintainability, and reusability.
These dimensions are particularly important in games whose service may continue for 10 to 12 years. Slow or unstable code does not remain an internal problem for the development team; it may appear to players as long loading times, crashes, security vulnerabilities, or negative reviews and refunds.
Artificial Intelligence Accelerates Development and Increases Code Volume
The article says that schedule pressure is driving many teams to use artificial intelligence to assist with programming. According to the Unity Game Developer Report 2026, 95% of Unity developers use artificial intelligence in their work to assist with the programming process.
This makes it possible to create prototypes and experiment with new mechanics in days instead of months, while also reducing the time spent on repetitive work. However, speed may mean producing 10,000 lines at a time when a developer previously wrote only a few hundred, thereby expanding the surface area of problems that require review.
JetBrains cites a study from CodeRabbit indicating that AI-generated code is not necessarily worse overall than human-written code, but it may fail in different ways. According to the study, it produced 1.7 times more logical problems and errors, along with additional concerns related to maintainability and security. The article cautions that the results of this type of study, including data from AI tool vendors, require critical examination.
What Does Static Analysis Add?
The presentation proposes using static analysis as a foundational layer for checking code before it is merged. It can cover health checks, test-coverage limits, performance, memory and resource safety, resource leaks, exceptions, outdated dependencies, and flow analysis to detect more complex problems.
It also helps detect code smells, complexity, code duplication, and compliance with the team’s internal standards. These points are particularly important with automatically generated code, as the article says that artificial intelligence tends to produce duplication and is not equally inclined toward refactoring.
The main advantage of static analysis is determinism: the tool produces the same results when checking the same code, and each result can be linked to a specific check that explains the cause of the problem. It also does not consume tokens and is usually faster and less expensive than a review based entirely on artificial intelligence models.
The Practical Model: Two Layers and a Verification Loop
JetBrains does not view static analysis as a replacement for artificial intelligence. Pattern-based analysis does not always understand the intent of the code or logical relationships across the project’s files and components, areas in which artificial intelligence can add value.
The proposed model begins with writing the code, whether by a developer or an artificial intelligence tool, followed by running static analysis within the continuous integration pipeline. Some findings can be addressed automatically through quick fixes, while legacy problems can be placed in a baseline so that they do not obstruct new changes. Artificial intelligence then handles logical problems or refactoring tasks, after which the code returns to static analysis to verify that it complies with quality and security standards before being merged.
The article mentions two studies, one of which indicates a reduction in token use ranging from 72% to 92% when static analysis is combined with large language model calls, while the other found that vulnerabilities in linguistically generated code could be reduced by up to 33% before reaching the main branch.
What Changes Practically for Teams?
The practical implication is not to add a lengthy review stage, but to distribute the work among tools, each with a clearer function. Pull requests in Qodana can be configured to check only the changed files, so the check takes, according to the article, approximately one or two minutes. JetBrains also presents the Hooks feature in Rider, where code-formatting and problem-checking operations are invoked with every piece of code generated by the agent, and the result is then returned to it so that it can correct the code according to the team’s standards.
This approach still requires human adjustment of quality standards, continuous review of development pipelines, and independent verification of studies and results. However, it offers a clear foundation: increasing the speed of code generation does not eliminate the need to measure its quality; it makes systematic checking more important.