Why Code Quality Matters More than Ever in the AI Era

Developers and the organizations that employ them are at an impasse. As software becomes more critical to businesses, these technical experts have the task of writing more code than ever before. While AI tools have been instrumental in keeping up with these demands, there is some concern about how they will affect the developer role as we know it.

Last year, a survey found that 71% of developers fear that AI will completely eliminate their roles, with 40% believing this change will happen sooner rather than later. However, when used correctly, AI has the power to enable developers to increase their skills and work more efficiently. With the proper tools, safeguards and mitigation techniques to reduce errors, developers will be able to embrace the power of AI’s code-writing capabilities as complementary to their crafting, designing and architecting of systems.

AI allows developers to focus on priority work rather than performing mundane, routine code-writing tasks that traditionally take up their time. As is, developers spend less than five hours a week actually writing new code. A future where developers embrace, rather than fear, AI depends on having the right tools employed to check the quality of AI-generated code. Trusted, automated tools allow developers to ensure that code is error-free while bolstering software security by finding bugs earlier in the software development life cycle (SDLC) and fixing them before they become problematic.

Automated tools can help alleviate some of the pressure on developers to check every single line of code written by AI, which in turn can decrease the mounting technical debt that plagues businesses and bogs down developer teams today. Research shows that poor-quality code is estimated to cost $2.4 trillion, and that’s just in the United States. The only way to decrease the technical debt associated with those costs is to avoid accumulating it in the first place. Overall, businesses that are serious about reducing costs and boosting revenue must look at high-quality code as a means to do so.

More Code Means Increased Need for Quality Assurance

AI-generated code is a reality for every organization grappling with increasing demands for new software. Developers simply can’t keep up with this demand alone. According to Google’s CEO, Sundar Pichai, over 25% of Google’s new code is now written by AI.

Developers understand that software quality begins at the code level. Poor-quality code carries risks and vulnerabilities and threatens a business’s reputation and bottom line. As developers are expected to produce more code than ever to enhance an organization’s software output, and turn to AI-coding tools for support, they must also stay vigilant about ensuring code quality, especially where AI is concerned.

While the United States employed fewer software developers in January 2024 than it did in 2018, IDC predicts that the vast majority of organizations (over 90%) will feel the pain of what it calls “the IT skills crisis” by 2026. This means that, while organizations have an increasing need for developers, they cannot necessarily meet that need.

AI is imperative in augmenting developer workflows, but the reality is that twice as much code means twice as many mistakes are possible. In fact, research from Stanford University shows that developers with access to AI coding assistants wrote significantly less secure code.

More code being written means more reviews of that code need to be performed to ensure software quality. The solution to these problems lies in arming developers with trusted, automated tools to help with code quality assurance.

Automated Tools Spot Bad Code Before Deployment

Organizations need to provide developers with the right tools to ensure the quality of AI-generated code. Code quality and security tools align with developers’ needs to make the most of their knowledge and experience, and allow them to catch issues in code, especially AI-generated code, before they become bigger problems.

Tools that start reviewing code in the integrated development environment (IDE) and continue scanning code throughout the CI/CD are the best way for developers to ensure that their code is consistent, maintainable, reliable and secure. These tools are developers’ greatest weapon in the battle against bad code and enable them to focus on high-impact and priority work.

The later a developer catches a problem, the more unwieldy it becomes. Bad code creates a domino effect and becomes more expensive and harder to resolve. Automated tools provide earlier safeguards that prevent errors and bugs in code from becoming more serious down the line.

By implementing the right automated solutions into their workflow, developers and organizations have the assurance that all code (AI-generated and human-developed) has been thoroughly scanned for issues and that projects using AI tools are meeting high standards of quality and security.

‘Start Left’: Less Technical Debt, Enhanced Dev Experience

Experts understand a “shift left” approach in which software analysis and review happen earlier in the SDLC, but that approach doesn’t go far enough. Starting left, instead, means that developers are using trusted tools right from the beginning of the SDLC, catching bugs in the process and remediating issues before they become long-term complications.

Automated tools are vital to a “start left” approach. They ensure software quality early, reduce technical debt and enhance the developer’s job experience. Especially with the widespread adoption of AI in coding, a “start left” approach is necessary to have trust in the quality of code. AI coding assistants can be extremely valuable in increasing software output, but they can’t be left to run without some form of verification and quality assurance. Automated tools that developers know and trust can aid in this effort.

While AI is already being incorporated into developer workflows, we’re still in the stages of seeing exactly what that looks like. Integrating AI tools won’t replace developers but will certainly change the way they work. Some of these changes will require developers to increase their skills, ultimately enhancing their careers and making them an even greater asset to organization. Gartner reports that 80% of the IT operations and engineering workforce will need to increase their skills to effectively use AI on the job.

Additionally, by having AI take on the mundane work that tends to drain time, developers will be able to focus on tasks that require critical thinking and creativity, ultimately having a greater impact on the business.

Combining generative AI with code quality and security tools, such as SonarQube (SonarQube Server, SonarQube Cloud, SonarQube for IDE), developers can significantly boost productivity with AI assistance while ensuring their code remains secure and stable and preventing bad code from moving to production.