Decoding Subjectivity in Coding Test Grading: How to Ensure Fair Assessments

by Codeaid Team

In 2022, poor-quality software in the US cost about $2.41 trillion.
So, it’s no wonder that an increasing number of tech recruiters are searching for more effective ways to find the best talent.

subjectivity in coding test grading

In 2022, poor-quality software in the US cost about $2.41 trillion

So, it’s no wonder that an increasing number of tech recruiters are searching for more effective ways to find the best talent.  

Here’s where pre-hire coding tests come in. 

Such tests are a great way to assess technical talent as they give a detailed ground-truth assessment of the candidate’s skills.  

Additionally, such tests can eliminate bias and even replace technical interviews, saving lots of time and costs.  

But there’s a catch. 

Coding tests have to be carefully graded. Otherwise, an inaccurate assessment might lead to making the wrong hiring choices. 

Let’s cover all about code test grading and how it works. We’ll also explore the pros and cons of manual and automatic grading, including how to eliminate subjectivity in manual code test grading.  

What is Code Review in Coding Tests? 

A coding test review refers to evaluating a pre-hire coding skills test the candidate completes as part of the job application process. Coding test reviews are a crucial step of the hiring process as they evaluate the candidate’s coding skills, ability to implement best practices, problem-solving capabilities, and technical knowledge. 

Such reviews usually involve assessing the code’s structure, correctness, readability, and efficiency. This gives you a clear understanding of the candidate’s coding strengths and weaknesses and helps you make better decisions. 

The way you review programming tests is crucial for the accurate and consistent assessment of all potential hires. Overall, there are two main types of code test reviews – manual and automated. Let’s take a closer look. 

Automated Code Test Grading: Definition, Pros & Cons 

Automatic grading is fast, unbiased, and convenient.  

The automated code review is performed with the help of automated tools that analyze the source code to identify potential errors. To ensure consistent and correct analysis, the tool uses predefined rules and best practices. 

However, if the automated coding challenge review tool is not well-calibrated to show the expected average scores for junior, intermediate, and senior developers and the test scores are not well distributed, this might lead to inaccurate assessment. 


  • Speed: Automated reviewing tools can scan thousands of lines of code in a matter of seconds. This makes them a time-efficient way to review programming tests. 
  • Cost efficiency: Automatic grading does not require a highly skilled programmer to go through the code. Their ease of use will help you cut costs as you won’t have to involve highly skilled specialists in the hiring process. 
  • Consistency: Automated code review tools are consistent in the results they provide. They do not have subjectivity issues or bias like human reviewers do.  
  • Scale: Such processes are easy to scale as implementing them does not require extra resources, and they can be used across a larger pool of candidates.  


  • Mistakes: While automated grading tools are good at catching minor mistakes (often missed by the human eye), they are unable to assess the bigger picture and take into account things like the business logic behind the code. In turn, this can lead to false positive and inaccurate assessment results. 
  • Lacks context: Automated reviewing is consistent, but it fails to consider the intent behind specific excerpts of code. Additionally, automatic grading tools cannot comment on the structure of the code (e.g., if the developer used the best design patterns or class structure). This might lead to inaccurate grading and failure to accurately reflect the candidate’s skills.  
  • Scope limitations: An automated tool can only detect vulnerabilities that it already knows. This means there’s a possibility of the tool missing any unknown errors.  

Manual Code Test Grading: Definition, Pros & Cons 

This type of code review is performed by an experienced developer who evaluates the candidate’s language mastery and the code’s structure, reusability, conciseness, and readability. This allows the reviewer to provide their expert feedback on the candidate’s skills and knowledge

While automated test review is convenient, not all technical skills tests can be automatically graded. For example, DevOps and Security challenges can be difficult to grade automatically. Additionally, candidates applying for project managers, designers, and QA will also need to be graded manually. 

So, manual grading is inevitable in some scenarios.   

However, if not done in a constructive manner, manual grading can lead to subjectivity, decreasing the grading accuracy. 

Subjectivity is almost always present in the following scenarios: 

  • If the reviewer receives the code without any predefined grading structure and criteria: Guidelines like “Evaluate the quality of the code” are too general and might lead to inaccurate scoring. That’s why evaluation instructions should be as specific as possible. The grading process should also be structured, and all reviews – done against the same criteria.  
  • If the system allows anyone from the hiring team to grade the assignment: Company recruiting staff that constantly work with engineering resources know who has the skills, competence, and availability to review the coding tests. That’s why recruiters should invite only a few selected reviewers. Additionally, technical recruiters should be able to assign multiple reviewers when needed. This gives them the ability to later correlate reviews with new hire performance and see whose reviews are most accurate. 


  • Better understanding: A human reviewer can go beyond the lines of code and provide a more holistic assessment. Manual reviewing also allows the detection of more subtle code inefficiencies and vulnerabilities not included in the preset rules of automated tools. This also allows for understanding the logic behind the code, leading to more accurate coding assessments.  
  • Customization: When manual grading is applied, the reviewers can modify the grading criteria. Usually, employers who want to tailor the grading process to specific position requirements favor manual reviews as this allows them to assign greater importance to different criteria and adjust the grades accordingly. On the other hand, an automated grading tool will spit out the same result breakdown even if you want to include or remove some of the preset grading factors.  
  • High accuracy: Since manual reviews are performed by highly skilled individuals, their grades will be accurate, considering all criteria vital for assessing the candidate as a whole.  


  • Cost and time inefficiencies: Manual reviews involve specialists coming in and performing the review, resulting in a longer grading process and higher costs. Also, the best reviewers are usually senior developers who are quite busy and hard to schedule, which additionally prolongs the whole process. 
  • Subjectivity: Since different reviewers bring different expertise and unconscious biases to the table, they might be subjective when reviewing the coding test. This means you would have to run the code through a few reviewers to get a more balanced assessment. 
  • Scalability: Manual reviews are not easy to scale as they involve more human resources, and this can be challenging for bigger organizations. 

Codeaid: Superior Code Grading to Hire the Best Talent 

Codeaid is a coding test website that offers various real-world simulation coding tests and both automatic and manual grading features.  

This gives you the freedom to decide which coding challenge review approach to adopt. 

Furthermore, Codeaid’s automatic grading feature does not only do the hard job for you, but it also gives you a highly granular report on how the grade was compiled. This allows you to see all key insights and details on which sections the candidates passed and which they failed. 

On the other hand, the manual grading feature offers an even more refined evaluation. This comprehensive approach is based on a predefined framework of various criteria, reducing subjectivity and ensuring a fair assessment. This coding assessment method is ideal for coding tests where automated grading is not possible (for example, for architecture positions).  

You can finally ditch the superficial coding tests for interviews and try a refined and accurate coding test tool that will help you find top talent. 

Manual vs. Automatic Code Test Grading: The Takeaway 

Overall, both manual and automatic coding tests have their pros and cons. Which grading approach you choose depends on a few factors, including what position you’re hiring for.  

Whether you choose manual or automatic code review, you should ensure the tool you’re using offers best of both. Automatic grading can give you quick and easy access to key insights into the candidate’s performance and abilities. On the other hand, manual grading provides a well-controlled assignment process, grading process, and reviewer evaluation process so subjectivity is minimized as much as possible. 

Ready to try a superior coding test website like Codeaid? Check out our detailed coding tests and our latest AI Interviewer features

Related Blogs

  • Integrating AI Tools in your existing HR workflows

    Would you want to save 75% of the time you put into initial screenings during recruitment?

    Read More
  • futuristic-networking-technology-remix-with-woman-using-virtual-screen 1

    Evaluating AI Interview Software: What Features Matter Most?

    The advent of artificial intelligence in recruitment changed the rules of the game for hiring in organizations – especially those in the competitive tech industry. In fact, the AI recruitment market is worth $661.5 million as of December 2023.

    Read More
  • AI vs Traditional Interviews

    AI and Traditional Interviews: Can Both Coexist in Modern Hiring? 

    AI interviewing for technical hires is now a reality. According to Statista, HR tech’s annual revenue was projected to reach 62.6 billion U.S. dollars in 2022, with a forecast indicating a growth to 91.8 billion U.S. dollars by 2026. This gives HR and hiring managers options but also added complexity when deciding what the hiring pipeline should now look like. Does AI interviewing replace or compliment traditional manual interviews? This is what we discuss in this article.

    Read More