Coding Tests in the Age of AI: Yay or Nay?

When it comes to hiring developers, the take-home coding test has long been a staple. For years, these tests have helped employers evaluate technical skills, problem-solving abilities, and whether a candidate has what it takes to thrive in their role. But as the hiring landscape evolves, so do the tools and expectations on both sides of the equation.

Enter AI. With tools like ChatGPT and GitHub Copilot now at a developer’s fingertips, the coding test has become a battleground of mixed opinions. Some argue that these tests are more relevant than ever, while others claim they’re outdated, unfair, or even counterproductive.

So, what’s the verdict? Are coding tests still useful, or have they become relics of a pre-AI era? Let’s unpack this.

Why Coding Tests Matter (and Why They Frustrate)

The purpose of a coding test is simple: to ensure a candidate has the technical chops to do the job. They’re designed to:

• Assess problem-solving skills.

• Gauge familiarity with programming languages and tools.

• Identify a candidate’s approach to debugging and optimization.

Sounds straightforward, right? But coding tests often come with frustrations for both candidates and employers:

The Candidate Perspective:

1. Time Consumption: “Great devs are BUSY.” Asking a talented developer to spend hours—sometimes days—on an unpaid coding challenge can feel unreasonable.

2. Fairness Concerns: Some candidates wonder if their test results are judged consistently or fairly across the board.

3. AI Assistance: With AI tools readily available, many candidates are asking: “Am I allowed to use them?”

The Employer Perspective:

1. Cheating Risks: “How do I know who actually solved the test?”

2. Candidate Drop-Offs: Long, unpaid tests can scare off top-tier talent who simply don’t have the time.

3. Validity of Results: A passing test score doesn’t always translate into strong on-the-job performance.

The AI Factor: Cheating or Productivity?

AI has added a new layer of complexity to the coding test debate. Tools like ChatGPT can:

• Write entire functions.

• Suggest optimized algorithms.

• Debug code quickly.

The question becomes: is this cheating, or simply smart use of tools? After all, in the real world, developers frequently use AI to speed up their work. Penalizing candidates for leveraging these tools in a test might be counterproductive.

Rethinking Coding Tests for the AI Era

Instead of abandoning coding tests entirely, it might be time to rethink how they’re structured. Here are some strategies to make them more effective and appealing:

1. Keep Tests Short

Long take-home tests are a surefire way to lose great candidates. Aim for challenges that can be completed in under two hours. This keeps the process manageable and respectful of the candidate’s time.

2. Pay for Their Time

Offering compensation for take-home tests shows respect for candidates’ effort and time. It also signals that you value their contributions, even during the hiring process.

3. Use Live Coding Sessions

Live coding interviews can help you see a candidate’s thought process in real-time. These sessions aren’t about solving the problem perfectly; they’re about collaboration, communication, and problem-solving under pressure.

4. Review GitHub Portfolios

A developer’s open-source contributions and personal projects can tell you far more than a one-off test. Reviewing their GitHub profile can provide insights into their coding style, creativity, and areas of expertise.

5. Focus on Real-World Examples

Instead of abstract algorithm challenges, design tests that reflect real-world tasks a candidate would handle on the job. This approach not only feels more relevant to candidates but also helps you evaluate practical skills.

What’s Working for You?

As startups, we need to adapt quickly to the changing hiring landscape. Coding tests may still have their place, but only if they’re designed with empathy, fairness, and relevance in mind. The goal isn’t just to evaluate skills—it’s to create an experience that engages and excites candidates about joining your team.

So, how are you handling coding tests in the AI era?

• Are you keeping them short and sweet?

• Offering alternatives like portfolio reviews?

• Embracing AI as part of the process?

Let’s hear your thoughts. Is it time to rethink coding tests entirely, or are they still a critical piece of the hiring puzzle? Share your experiences below!