How To Hire A Super Programmer Even If You’re Not One?

AI comes to help

In this new age of digital services and products, it’s a premium to possess programming skills. Much to the recruiter’s dismay, programming cannot be assessed simply by skimming through a candidate’s CV, asking a few questions or going through a list of projects. How can you be sure that someone who claims to have skills in Ruby or C++ or Python “actually” has them?

 

How do You Hire a Super Programmer?

You can start by asking a candidate to write a code for a programming problem –and then have a software engineer evaluate her skills. But will that surely help identify super programmer? Not really!

There are many skills needed for one to be a super programmer. Writing a code that is functionally correct and passes test cases is just one basic criteria. A super programmer must be able to write a code that is efficient, maintainable and at the same time conforms to industry best practices. Testing for all these parameters isn’t easy, for manual evaluators. What you need are AI-based program evaluations that score candidates on all these metrics comprehensively, in one go.

While there are several conventional automated programming assessments in the market that “claim” to evaluate coding ability, they assess only on the basis of test cases passed. Only AI-based ones actually help find the real diamonds. They accurately test candidates' coding abilities from the standpoint of algorithmic/logical thinking. Additionally, they assess coders on code maintainability, scalability and time complexity.

What is more? These tools can even understand non-compilable codes. Using smart static analysis of codes they can automatically derive features, signatures of the logic of the program and grade a code that doesn’t compile. This means that you do not miss out on even a single good candidate due to a silly mistake while coding!

In a recent case, a sample of 90,000 US college students took Automata, Aspiring Minds’ AI-based automated programming assessment. These were seniors graduating in computer science/engineering and keen on working in the IT sector. They were scored on both test-case metric and correctness of logic as detected by Automata's machine learning algorithm. Scoring based on 'test-cases passed' rendered only 36% of the students eligible for further testing rounds, rejecting the remaining 64% However, Automata scores revealed that out of those "left out", a good 16% had the right logic and thought process but made some silly mistakes. Smart ML algorithms could spot good candidates that were totally missed by test cases. Further, Automata also found 20% super programmers who write maintainable and efficient code. These programmers are the dream of any IT product company! Conventional assessment tools would have missed these candidates.



Organizations using AI-based programming assessments have increased their recruitment efficiency by as much as 75%. These tests let you cut costs, look beyond resumes and identify the best candidates without missing anyone meritorious. They are highly scalable and evaluate 1000s of candidates in one go, something that’s missing in the case of manual evaluation.

AI-based programming evaluations also provide plagiarism checks that compare the code similarity to other submissions in an event to find if a candidate may have used unfair means to complete the test. And, what about candidate feedback? AI algorithms resolve this too! They can provide feedback to all candidates, even those whose codes do not compile. They can give them tips to crack the correct logic and provide feedback on the stylistic and maintainability issues in their code.

AI can help you decode the secret to finding the super programmer you’ve been long searching for! So what if you’re not one yourself. That’s one secret worth keeping!

Author Bio

Varun Aggarwal is Co-Founder & CTO of Aspiring Minds. Visit www.aspiringminds.com Connect Varun Aggarwal