Hiring fresher software engineers is a critical task for any organization looking to build a strong engineering team. As freshers typically have limited real-world experience, it's essential to design a coding challenge that assesses their problem-solving abilities, programming skills, and algorithmic thinking in a way that helps you identify the most promising talent. At WeCP, we believe in evaluating candidates holistically to ensure they possess not only the basic programming skills but also the analytical and optimization skills necessary for growth in a fast-paced engineering environment.
[Mind-map] 3-category of Software Engineers by Calibre.
Level 1: These freshers have implementation mindset. They are only good at writing code to build and ship features. Their primary expectation from them is to ensure functionality, translating requirements into working code.
Level 2: These freshers have optimization mindset. These engineers not only implement features but they also can ensure that the code is robust, efficient, and handles edge cases. They think beyond just getting the feature to work, ensuring performance and resilience under various conditions.
Level 3: These freshers have architect mindset. These engineers design features with scalability in mind, ensuring that the application can handle a large number of users in the industry. They build for the future, enabling long-term growth and system stability.
There are three coding problems that WeCP recommends to assess for the aforementioned mindsets. And the same is popularly internally known as WeCP 3 Coding Problems Framework.
Here’s how you can design a comprehensive and reliable coding challenge, assessing candidates through logical, greedy, and dynamic programming tasks:
1. Tasks to spot Level 1 SE
These are basic logical programming tasks that can assess a candidate's ability to write basic programs, implement logic, and demonstrate foundational programming skills.
Logical coding tasks test whether candidates can use fundamental programming constructs like loops, conditionals, and basic data structures. For fresher engineers, it’s important to determine whether they have a solid grasp of syntax, problem-solving logic, and the ability to convert a real-world problem into code. This serves as a baseline for more advanced tasks, ensuring that candidates possess the essential skills needed to grow into proficient software engineers. These tasks are largely around:
Basic Syntax Understanding: Can the candidate write correct, syntactically sound code in their chosen language?
Logical Thinking: Can they translate a basic real-world problem into a series of programming steps?
Attention to Detail: Are they able to avoid off-by-one errors, handle edge cases (like empty lists), and produce reliable output?
For Example:
Implement a basic fraud detection system where you flag a transaction as fraudulent if it exceeds a certain threshold (e.g., $10,000) or if the transaction occurs outside the user's home country. You are given a list of transactions, each with the transaction amount, location, and user information. Write a program that processes these transactions and returns a list of flagged transactions.
Here the expectation is that the engineer should implement basic code that checks if each transaction meets the given criteria (amount threshold and location) and flags it accordingly. Their basic Implementation of basic rules, ensuring that each transaction is processed and flagged correctly based on simple conditions.
For a fresher, the ability to grasp and apply these core principles is essential as they start their journey in software engineering.
2. Tasks to spot Level 2 SE
These are greedy algorithm based tasks that can evaluate a candidate's ability to solve problems using a greedy algorithm, making a series of choices that lead to an optimal solution. Greedy algorithms are a common approach to solving optimization problems, where candidates need to make decisions step by step without re-evaluating prior decisions. This tests whether a candidate can think algorithmically and understand how to make efficient decisions in a structured way. It also highlights their efficiency awareness and algorithmic reasoning—skills that are critical for solving real-world problems where resources (time, space, etc.) are limited. These tasks are largely around:
Algorithmic Thinking: Can the candidate come up with a series of decisions that lead to an optimal result?
Efficiency: Does the candidate avoid brute-force approaches and make the solution as efficient as possible in terms of time and space complexity?
Problem Recognition: Can they identify problems that can be solved using a greedy algorithm, and do they know when it's the correct approach?
For Example: In addition to basic fraud detection, consider a scenario where you need to flag fraudulent behavior based on a sequence of transactions. Specifically, a fraud alert is triggered if there are multiple transactions within a short time frame (e.g., within 5 minutes) that exceed a certain threshold. Use a greedy algorithm to process the list of transactions in order, flagging potential fraud as soon as conditions are met. The system should stop further checks once fraud is detected in a sequence.
Here the expectation is that the engineer should write a program that processes transactions in a greedy manner, checking each transaction sequentially. Once a sequence of transactions is flagged as fraudulent, no further checks for that user in the same window should be made. Their key Focus here is Efficient use of a greedy algorithm to flag transactions in real-time, prioritizing immediate detection of fraud without recalculating past transactions unnecessarily.
Being able to solve problems with greedy algorithms shows that a candidate is well-versed in making strategic decisions, which is crucial in building scalable solutions.
3. Tasks to spot Level 3 SE
These are dynamic programming tasks that can assess the candidate’s ability to break down complex problems into simpler subproblems and solve them using dynamic programming (DP) method. Dynamic programming is a powerful technique for solving optimization problems with overlapping subproblems. It tests a candidate’s ability to think recursively, understand optimal substructure, and optimize solutions using memoization or tabulation. A DP task highlights a candidate’s ability to solve complex real-world problems efficiently, a skill that’s essential as they grow into more challenging roles in your organization. These tasks are broadly around:
Recursive and Iterative Thinking: Does the candidate know how to define the problem in terms of smaller subproblems and build a solution either recursively or iteratively?
Optimization Skills: Can they avoid redundant calculations, minimizing time and space complexity while maintaining correctness?
Understanding of Advanced Algorithms: Do they have the depth of knowledge to apply dynamic programming concepts correctly, which are critical for solving large, complex problems?
For Example: Design a fraud detection system that identifies the optimal way to flag fraudulent transactions based on multiple factors like transaction amount, location, and frequency over different time windows (e.g., 5 minutes, 1 hour, 24 hours). You need to ensure that the system can dynamically adjust its fraud detection thresholds over time based on the evolving transaction patterns of users. Use dynamic programming to store previously computed results for each time window, avoiding recomputation, and ensuring scalability for millions of users.
Here the expectation is that the engineer should design and implement a dynamic programming solution that optimizes fraud detection over time windows. This involves storing subproblem solutions for different time periods and combining them to make optimal fraud detection decisions while ensuring the system scales efficiently. Here their key focus is use of dynamic programming to manage overlapping time windows, ensuring optimal fraud detection and efficiency when processing a massive number of transactions.
Dynamic programming assessments allow you to see whether a candidate can solve problems in an optimized manner, reducing the computational cost—something essential for building efficient systems.
Why does it matter?
Each of the three problem types focuses on different areas of problem-solving—logical thinking, algorithmic reasoning, and optimization—ensuring that candidates can handle various challenges they will encounter in real-world engineering roles. Freshers who perform well across these problem types demonstrate not only their current abilities but also their potential to learn and grow into more complex and higher-level problems, a critical factor for long-term success in your organization. Engineering in the real world is rarely about solving simple tasks. It requires understanding complex problems, optimizing solutions, and thinking strategically. This type of assessment prepares candidates for the kind of challenges they will face in a professional environment.
Assessing candidates on just one dimension—like basic coding skills—is not enough to gauge their potential for long-term growth. By incorporating logical coding, greedy algorithms, and dynamic programming problems into your coding challenges, you ensure that your evaluation is comprehensive and multi-dimensional.
Designing a coding challenge for fresher software engineers isn’t just about testing their immediate skills but about evaluating their potential for growth. By using a variety of tasks—like logical coding, greedy algorithms, and dynamic programming—you can assess their problem-solving abilities holistically, ensuring you hire the right candidates who can grow with your organization and contribute to building scalable, efficient, and innovative solutions.
Incorporating these three key problem types, as recommended by WeCP, will help you select the most promising fresher engineers, equipping your team for long-term success.