Hiring managers should be aware that the use of chatbots or language models like ChatGPT for coding tests can potentially undermine the integrity and effectiveness of the hiring process. While ChatGPT is a powerful language model that can generate code and provide solutions to coding problems, it is not designed to understand the nuances and complexities of real-world programming.
Moreover, using language models to complete coding tests could result in candidates with weaker programming skills passing the test and being hired, leading to a negative impact on the quality of the development team. Additionally, the use of language models in the hiring process could result in a lack of diversity among candidates, as candidates who are not familiar with language models may be at a disadvantage.
Therefore, it is recommended that hiring managers create coding tests that are designed to assess the candidate’s actual programming skills and experience, rather than their ability to use language models. Hiring managers can also take steps to prevent cheating, such as monitoring the test-taking environment and implementing proctoring software. Ultimately, it is important to ensure that the hiring process is fair, unbiased, and accurately reflects the candidate’s programming abilities.
It may be difficult to determine definitively whether a potential hiring candidate is using ChatGPT or any other language model for their coding test, as these tools can be used in a way that mimics human coding patterns. However, there are some signs that may indicate that a candidate is using a language model for their coding test:
- Unusually fast or perfect code: If a candidate’s code is completed unusually quickly or contains no syntax errors, it may indicate that they are using a language model. However, it’s important to note that some highly skilled programmers may also be able to write code quickly and without errors.
- Generic code structure: If the candidate’s code has a generic or template-like structure that is not tailored to the specific problem, it may indicate that they are using a language model. This could include code that follows a standard template or algorithm, rather than a unique solution that is specific to the problem.
- Repetitive or identical code: If the candidate’s code is identical or highly similar to code found online or in other resources, it may indicate that they are copying and pasting code generated by a language model.
- Inability to explain their code: If the candidate is unable to explain the logic or reasoning behind their code, or if they struggle to answer questions related to their code, it may indicate that they did not actually write the code themselves and are relying on a language model.
It’s important to note that these signs are not conclusive and should not be used as the sole basis for rejecting a candidate. It’s always best to approach the situation with an open mind and ask follow-up questions to better understand the candidate’s thought process and approach to problem-solving.