Coding Simulations

SHL Coding Simulations

The World’s Most Powerful Virtual Coding Exercise

SHL Coding Simulations is the best on the market to win your tech talent.

It is the first and only AI model to score incomplete code on logical correctness and quality. Measure competency – don’t rule out candidates on 100% accuracy. Find tech talent across multiple roles, experience levels, and sectors.

Coding Simulations
  • Coding Simulations
  • Coding Simulations
  • Coding Simulations

An Immersive Experience with Online Coding Interviews

Coding simulation for 50+ languages
A real-world IDE, powered with IntelliSense, auto-save, custom test cases, on-the-fly language selection,  delivering an intuitive and engaging programming experience.

Uses AI to grade programs
Automatic evaluation of programming ability using AI algorithms.

Large, curated, and reliable question bank
Designed by in-house experts. Flexibility to create a custom question bank.

Comprehensive reporting
Detailed scorecard with objective and benchmarked scores, compilation statistics, test case details, proctoring, and more.

Hire the Best Coders—Faster

Shorten Time-to-Hire

Speed up assessment in minutes using 500+ coding problems and different variants. Shortlist faster with near real-time code analysis and detailed scorecard.

4x Reach Increase

Attract a higher volume of quality technology candidates from a larger talent pool, resulting in a more diverse range of candidates.

Identify 25% More Coders

AI grades reduce false rejects and increase pass rate of qualified talent pool by over 25%.


Reduce Hiring Costs up to 60%

Cut short hiring steps by replacing resume screening while ensuring you hire the best programmers. Reduce travel and operational hassle for recruiters.

Engaging Candidate Experience

Delight candidates every time with realistic coding simulation, 50+ programming languages, flexibility to switch between questions, and a shorter hiring cycle.

Coding Simulations

Top of the Funnel Consideration

SHL Coding Simulation:

  • Grades programs that are partially correct
  • Grades programs that have minor errors and may not compile
  • Uses a scalable question-independent model
  • Supervised ML model to evaluate code’s logical correctness
  • Fixes minor errors and relaxes compilation rules
  • Uses distance from good set of codes to grade the code

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