11 answers
Donavan
Student
Sparks, Nevada
1
Question
Updated
1439 views
How do you approach debugging or troubleshooting issues in a large codebase?
I feel that there are a lot of debugging when it comes to coding, my experience in middle school/high school main concerns were about how to try and fix the code, but obviously its just a small portions, so how do actual engineers debug code while dealing with a database of codes and programs.
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11 answers
Sumitra Biswal
Independent researcher in the intersection of AI and Cybersecurity
40
Answers
India
Updated
Sumitra’s Answer
Hi Donavan,
Debugging large codebases feels tough at first, but the trick is to break it into smaller parts. Reproduce the bug clearly, then narrow down where it happens using logs or a debugger. Focus only on the related module instead of the whole system, and lean on tools like Git and automated tests. Over time, you’ll spot patterns faster and debugging will feel less like guesswork and more like detective work.
Warm regards,
Sumitra
Debugging large codebases feels tough at first, but the trick is to break it into smaller parts. Reproduce the bug clearly, then narrow down where it happens using logs or a debugger. Focus only on the related module instead of the whole system, and lean on tools like Git and automated tests. Over time, you’ll spot patterns faster and debugging will feel less like guesswork and more like detective work.
Warm regards,
Sumitra
Sankarraj Subramani
GenAI Opensource Contributor and GenAI Sr. SDET
13
Answers
Sugar Land, Texas
Updated
Sankarraj’s Answer
That’s a great question! Debugging in a large, real-world codebase is definitely more complex than fixing a small school project, but the core mindset is the same: break the problem into smaller, manageable parts.
From my experience leading QA automation at United Airlines, Freddie Mac, and USAA, here’s how I approach it:
Reproduce the Issue Clearly – In a massive codebase, the first step is to isolate the conditions under which the bug occurs. For example, in aviation systems I worked on, I’d simulate specific satellite handovers or weather disruptions to consistently reproduce the connectivity issue before even diving into the code.
Use Observability Tools – Large systems generate logs, metrics, and traces. I use tools like Kibana and AWS CloudWatch to spot error patterns, slow queries, or unusual behavior. Instead of manually searching thousands of lines of code, logs help me zoom in on where the problem lives.
Leverage Automated Tests – Since I design scalable frameworks like AutoTestX, I run regression and unit tests to quickly check if new changes broke existing functionality. This reduces guesswork and highlights exactly which module is failing.
Divide and Conquer – In complex projects, I focus on one layer at a time: first the frontend (UI), then APIs, then the database. At Freddie Mac, for instance, if a mortgage rule validation failed, I would check API responses using Postman or Rest Assured before digging into backend logic.
Version Control & Collaboration – In big teams, debugging isn’t solo work. I rely on GitHub/Jira for tracking issues and collaborate with developers to understand recent commits. Code reviews also prevent future bugs by catching mistakes early.
Apply AI & Predictive Debugging – A unique part of my work has been building AI-powered debugging assistants like AutoBugPredictX, which predicts defect-prone areas based on historical patterns. This helps prioritize where to look first in a huge codebase.
In short, debugging at scale is about systematic isolation, the right tools, teamwork, and automation. Instead of being overwhelmed by millions of lines of code, engineers use structured methods and observability platforms to narrow the scope, test hypotheses, and validate fixes. Over time, you develop intuition—but it always starts with breaking the problem into smaller, logical steps.
From my experience leading QA automation at United Airlines, Freddie Mac, and USAA, here’s how I approach it:
Reproduce the Issue Clearly – In a massive codebase, the first step is to isolate the conditions under which the bug occurs. For example, in aviation systems I worked on, I’d simulate specific satellite handovers or weather disruptions to consistently reproduce the connectivity issue before even diving into the code.
Use Observability Tools – Large systems generate logs, metrics, and traces. I use tools like Kibana and AWS CloudWatch to spot error patterns, slow queries, or unusual behavior. Instead of manually searching thousands of lines of code, logs help me zoom in on where the problem lives.
Leverage Automated Tests – Since I design scalable frameworks like AutoTestX, I run regression and unit tests to quickly check if new changes broke existing functionality. This reduces guesswork and highlights exactly which module is failing.
Divide and Conquer – In complex projects, I focus on one layer at a time: first the frontend (UI), then APIs, then the database. At Freddie Mac, for instance, if a mortgage rule validation failed, I would check API responses using Postman or Rest Assured before digging into backend logic.
Version Control & Collaboration – In big teams, debugging isn’t solo work. I rely on GitHub/Jira for tracking issues and collaborate with developers to understand recent commits. Code reviews also prevent future bugs by catching mistakes early.
Apply AI & Predictive Debugging – A unique part of my work has been building AI-powered debugging assistants like AutoBugPredictX, which predicts defect-prone areas based on historical patterns. This helps prioritize where to look first in a huge codebase.
In short, debugging at scale is about systematic isolation, the right tools, teamwork, and automation. Instead of being overwhelmed by millions of lines of code, engineers use structured methods and observability platforms to narrow the scope, test hypotheses, and validate fixes. Over time, you develop intuition—but it always starts with breaking the problem into smaller, logical steps.
Shilpa Ponugoti
Data and Analytics Leader
2
Answers
Chandler, Arizona
Updated
Shilpa’s Answer
For me, writing good code begins with making it easy to debug and maintain. I prioritize adding clear debug statements, structured logging, and meaningful comments so new team members can quickly understand what each process does.
When an issue arises, I start by executing the process and capturing the exact errors. Good logging usually points me to where it’s happening, giving me a baseline to start.
I test multiple scenarios like positive, negative, and edge cases, because issues often surface only under specific conditions. Once I’ve narrowed down the source, I check version control to see if it was introduced in a recent release and whether a rollback or patch is feasible.
Finally, I implement the fix, retest thoroughly, and then release, ensuring that any new learnings are reflected in documentation or additional tests for the future.
When an issue arises, I start by executing the process and capturing the exact errors. Good logging usually points me to where it’s happening, giving me a baseline to start.
I test multiple scenarios like positive, negative, and edge cases, because issues often surface only under specific conditions. Once I’ve narrowed down the source, I check version control to see if it was introduced in a recent release and whether a rollback or patch is feasible.
Finally, I implement the fix, retest thoroughly, and then release, ensuring that any new learnings are reflected in documentation or additional tests for the future.
Kier McGuirk
Software Engineering
1
Answer
Dublin, County Dublin
Updated
Kier’s Answer
Hello Donavan!
Great question! You're right—bugs often pop up in large codebases. I've been a software engineer for nearly four years, and here are some tips that have really helped me:
1) To be a great debugger, focus on knowing "where" to look, not just "how" to fix it. Often, the tricky part is finding the source of the problem. When I started, I spent too long on bugs because I didn't know where to look. In big codebases, it's easy to get lost in details. Take time to understand the context of the bug first. This will save you a lot of time. A good debugger knows the system well enough to spot patterns and issues quickly.
2) Once you find the problem, consider all possible causes. Fixing a bug might seem simple, but it's important to think about other ways the code might break. This proactive approach saves time in the future and is crucial in large codebases where many things can go wrong.
3) After fixing the problem, ensure it doesn't happen again. It's easy to "fix and forget," but that can lead to the same issue later. Use the bug as a chance to create new tests that prevent it from reoccurring. In large codebases, tests help catch unintended changes. Make sure to add checks to keep the fix permanent.
These tips are more about mindset than a step-by-step guide. From my experience, this mindset helps separate novice debuggers from expert ones in complex systems and large codebases.
Best of luck with your career!
Great question! You're right—bugs often pop up in large codebases. I've been a software engineer for nearly four years, and here are some tips that have really helped me:
1) To be a great debugger, focus on knowing "where" to look, not just "how" to fix it. Often, the tricky part is finding the source of the problem. When I started, I spent too long on bugs because I didn't know where to look. In big codebases, it's easy to get lost in details. Take time to understand the context of the bug first. This will save you a lot of time. A good debugger knows the system well enough to spot patterns and issues quickly.
2) Once you find the problem, consider all possible causes. Fixing a bug might seem simple, but it's important to think about other ways the code might break. This proactive approach saves time in the future and is crucial in large codebases where many things can go wrong.
3) After fixing the problem, ensure it doesn't happen again. It's easy to "fix and forget," but that can lead to the same issue later. Use the bug as a chance to create new tests that prevent it from reoccurring. In large codebases, tests help catch unintended changes. Make sure to add checks to keep the fix permanent.
These tips are more about mindset than a step-by-step guide. From my experience, this mindset helps separate novice debuggers from expert ones in complex systems and large codebases.
Best of luck with your career!
Carolina Herbster
Software Engineering
2
Answers
Fortaleza
Updated
Carolina’s Answer
This is a great question! It's true that debugging software in an "actual" company is a different experience from debugging a small program for class. You got some great tips, so I'll just go over some points that I usually have in my mind while debugging:
1 - Trying to reproduce the issue first is always a good first step. This can vary from very easy to super challenging, depending on the system you're working with. Some times a bug will be a "race condition", which is caused by (simplifying a bit) parts of the program taking different times in different systems, which it makes it harder to reproduce in a controlled setting. That's why having lots of logging is very helpful, as it can be your only source of information when you can't repro the issue. Even if you can't repro it, just running the code can help you understand less familiar areas of the codebase and show you where to look.
2 - If you can't pinpoint where to look, especially in a system you're not familiar with, ask other engineers! Junior engineers are usually a bit shy to ask questions, but it's a very important way to learn. Whenever you ask something, try to give as much context as possible to the other person so they can answer you more effectively. An example, you can say, "hey, I was looking into a login bug in software XYZ, but I don't know what part of the codebase does the login, could you help me find it?". Sometimes they won't know of it, but they know another engineer who knows this code, etc, etc... you may have to go through a chain of people to find the right one 😂 And that's ok! Some codebases are just too big for a single person to know it all.
3 - When you do have an idea of what part of the code to look in, then you can start reading the code. If you do have logs, search where they are emitted. Look at the functions being called in this part of the code, look at error conditions and reason out why they could be triggered. It's a bit like playing detective, gathering clues and deriving conclusions from them. If your codebase uses Git, you can use some handy commands like git bisect to "step in time" through code, and git blame to find people who have worked on this code. There's another well known trick called "rubber-duck debugging", where you explain the code in natural language out loud to... well, anything, including actual rubber ducks 🦆, and just by doing this, you can see the problem in a different perspective. Continuing the detective metaphor, it's like Sherlock explaining the mystery to Watson.
4 - You may need to repeat the above steps a few times, each time narrowing down the cause of the bug. It can be infuriating to be looking at the program for very long and not finding the solution, but when you finally find it, it's a great feeling of being a Sherlock!
5 - If you get stuck, don't despair! You can take a break, work on a different project for a bit and/or discuss the bug with colleagues. It really helps to "freshen up" your mind and again, help you see the problem in a different perspective. I heard of colleagues who went to bed and ended up dreaming the (correct) solution of the bug 😂
Hopefully all my rambling has given you a few insights! And happy debugging!
1 - Trying to reproduce the issue first is always a good first step. This can vary from very easy to super challenging, depending on the system you're working with. Some times a bug will be a "race condition", which is caused by (simplifying a bit) parts of the program taking different times in different systems, which it makes it harder to reproduce in a controlled setting. That's why having lots of logging is very helpful, as it can be your only source of information when you can't repro the issue. Even if you can't repro it, just running the code can help you understand less familiar areas of the codebase and show you where to look.
2 - If you can't pinpoint where to look, especially in a system you're not familiar with, ask other engineers! Junior engineers are usually a bit shy to ask questions, but it's a very important way to learn. Whenever you ask something, try to give as much context as possible to the other person so they can answer you more effectively. An example, you can say, "hey, I was looking into a login bug in software XYZ, but I don't know what part of the codebase does the login, could you help me find it?". Sometimes they won't know of it, but they know another engineer who knows this code, etc, etc... you may have to go through a chain of people to find the right one 😂 And that's ok! Some codebases are just too big for a single person to know it all.
3 - When you do have an idea of what part of the code to look in, then you can start reading the code. If you do have logs, search where they are emitted. Look at the functions being called in this part of the code, look at error conditions and reason out why they could be triggered. It's a bit like playing detective, gathering clues and deriving conclusions from them. If your codebase uses Git, you can use some handy commands like git bisect to "step in time" through code, and git blame to find people who have worked on this code. There's another well known trick called "rubber-duck debugging", where you explain the code in natural language out loud to... well, anything, including actual rubber ducks 🦆, and just by doing this, you can see the problem in a different perspective. Continuing the detective metaphor, it's like Sherlock explaining the mystery to Watson.
4 - You may need to repeat the above steps a few times, each time narrowing down the cause of the bug. It can be infuriating to be looking at the program for very long and not finding the solution, but when you finally find it, it's a great feeling of being a Sherlock!
5 - If you get stuck, don't despair! You can take a break, work on a different project for a bit and/or discuss the bug with colleagues. It really helps to "freshen up" your mind and again, help you see the problem in a different perspective. I heard of colleagues who went to bed and ended up dreaming the (correct) solution of the bug 😂
Hopefully all my rambling has given you a few insights! And happy debugging!
James Constantine Frangos
SOFTWARE ENGINEER SINCE 1972; NUTRITIONIST SINCE 1976.
7094
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Good Day Donavan!
With lots of patience! Some people reckon you have 'lost it.' You cannot afford to be an enemy of good old-fashioned hard work!
Get an 'unbeatable' mindset!
An immense faith in yourself. Like 20 years ago when I was working on the C++ language trying to learn that! A program threw over 500 errors at me and I'll tell you I was pretty daunted by that. It was more bugs than you could imagine! Anyway I gave up on C++ after that and reverted back to something which was more rewarding to me which was Visual Basic.
Now I run a much tighter ship because I've got 27,949 lines of code in the Nutrition Wizards and I get really upset over one error. Do I know how to promote my software not really. It will probably die with me although I have bequeathed the software to an American. She could possibly sell it for a few hundred thousand dollars. Worked on it over 100,000 hours.
It will be valuable to school children to teach them about nutrition and it is based on the US Department of Agriculture database. With the state the United States Education Department is in I do not think the program will be bought by them. Been programming since 1972!
GOD BLESS!
With lots of patience! Some people reckon you have 'lost it.' You cannot afford to be an enemy of good old-fashioned hard work!
Get an 'unbeatable' mindset!
An immense faith in yourself. Like 20 years ago when I was working on the C++ language trying to learn that! A program threw over 500 errors at me and I'll tell you I was pretty daunted by that. It was more bugs than you could imagine! Anyway I gave up on C++ after that and reverted back to something which was more rewarding to me which was Visual Basic.
Now I run a much tighter ship because I've got 27,949 lines of code in the Nutrition Wizards and I get really upset over one error. Do I know how to promote my software not really. It will probably die with me although I have bequeathed the software to an American. She could possibly sell it for a few hundred thousand dollars. Worked on it over 100,000 hours.
It will be valuable to school children to teach them about nutrition and it is based on the US Department of Agriculture database. With the state the United States Education Department is in I do not think the program will be bought by them. Been programming since 1972!
GOD BLESS!
Ruchira Moitra
software engineer
3
Answers
Bengaluru, Karnataka, India
Updated
Ruchira’s Answer
Steps to Debug an Issue:
1. Review Logs: Start by checking any existing logs for clues about the problem.
2. Find Code Locations: Locate where these logs appear in the code to understand their context.
3. Add Debug Points: Insert more debug statements or breakpoints to gather detailed information.
4. Reproduce the Issue: Try to recreate the problem in a test environment to see it directly.
5. Identify the Cause: Analyze the debug data and system behavior to find the root of the issue.
When Logs Aren't Helpful:
1. Start from the API: Begin debugging at the API entry point and follow the execution path closely.
2. Trace the Code: Go through the code step-by-step, adding debug points until you find the problem.
If the Problem Isn't in the Code:
1. Check System Health: Look into server status, network connections, and resource usage for potential issues.
2. Review Configurations: Ensure configuration files, environment variables, and third-party services are set up correctly.
3. Work with Teams: Collaborate with DevOps or infrastructure teams to address deployment or cloud service issues.
4. Monitor System Logs: Use system logs and monitoring tools to gain insights beyond the application.
1. Review Logs: Start by checking any existing logs for clues about the problem.
2. Find Code Locations: Locate where these logs appear in the code to understand their context.
3. Add Debug Points: Insert more debug statements or breakpoints to gather detailed information.
4. Reproduce the Issue: Try to recreate the problem in a test environment to see it directly.
5. Identify the Cause: Analyze the debug data and system behavior to find the root of the issue.
When Logs Aren't Helpful:
1. Start from the API: Begin debugging at the API entry point and follow the execution path closely.
2. Trace the Code: Go through the code step-by-step, adding debug points until you find the problem.
If the Problem Isn't in the Code:
1. Check System Health: Look into server status, network connections, and resource usage for potential issues.
2. Review Configurations: Ensure configuration files, environment variables, and third-party services are set up correctly.
3. Work with Teams: Collaborate with DevOps or infrastructure teams to address deployment or cloud service issues.
4. Monitor System Logs: Use system logs and monitoring tools to gain insights beyond the application.
John Everson
Software Engineering Manager at Visual Concepts
20
Answers
Austin, TX
Updated
John’s Answer
Typically you start by trying to reproduce the issue. Many times something unexpected or broken will produce an error message or exception that can be caught, or will be something that can be caught by logging which can greatly narrow down where we have to look for issues. In instances where that is not the case, usually reproducing the issue can provide additional clues to help narrow down where to begin looking for an issue.
Sometimes this context may point to unfamiliar areas of the code. A larger codebase typically means more engineers who also specialize in different areas, so what may be a totally arcane issue to someone may be something familiar to an expert in a given part of a codebase. When we're really lost on where to start, we will often ask other engineers for advice. Sometimes its just as simple as "does this look like anything to you?"
Once you've narrowed down where the issue is occurring, you can usually diagnose why its occurring and tailor a fix for it.
Sometimes this context may point to unfamiliar areas of the code. A larger codebase typically means more engineers who also specialize in different areas, so what may be a totally arcane issue to someone may be something familiar to an expert in a given part of a codebase. When we're really lost on where to start, we will often ask other engineers for advice. Sometimes its just as simple as "does this look like anything to you?"
Once you've narrowed down where the issue is occurring, you can usually diagnose why its occurring and tailor a fix for it.
Samuel Apoya
Student
10
Answers
Waterville, Maine
Updated
Samuel’s Answer
When debugging, focus on each function separately rather than the entire codebase. Consider what each function or class is supposed to do, then check how it is actually performing. This approach helps you fix problems step by step until everything works correctly.
Koressa L
1
Answer
Mountain View, California
Updated
Koressa’s Answer
One thing to be aware of is that once you have a feature or bug in mind that you are working on, you will naturally end up understanding the relevant parts of the codebase to your changes. Never be afraid to ask questions from colleagues if you are lost as within most companies, developers are expected to support each other, especially newer members, as nobody would want you to be stuck and unable to make progress. Typically different companies have different debugging tools, however, most tools work in a similar manner.
In more complex issues, you may need to rely on external data, such as logs, session ids, telemetry, database queries, etc.. You may potentially need to ask the customer or affected persons for more details and logs directly. This really depends on the tooling per company, but if you become good at analyzing this sort of data, as well as knowing where to emit your own telemetry in the code to help you debug issues in the future as well, it becomes a valuable sought-after skill. Since bugs are not always reproducible on your machine, understanding how to track down and analyze issues from telemetry can be crucial.
For some very specific tips...
- There are some tools I find very helpful in identifying code and its purpose, for example, if your editor has the GitLens extension, it allows you to preview the PR description from any line of code to help understand why it was written.
- Sometimes, if you are unsure where to even start, it is useful to identify unique strings and variable names and search the codebase for those references and place breakpoints as you go to see if they can hit.
- Additionally, make use of writing tests to help identify issues and allow the debugger to take you through the code from the test. Tests are great at scoping down and catching bugs at any point from now into the future before the bugs get lost in the depths of the code.
- Make full use of stack traces while debugging to pinpoint where variables and code paths came from.
- Make use of your teammates, if you get lost, never let yourself keep digging deeper with no goal forever and ask questions - use git blame to see who made changes to lines and consider contacting them to help understand an unfamiliar code area, even if they are not on your immediate team.
- Make sure your changes and fixes are gated properly if your codebase has a gating system to proactively enable/disable code for customers if an issue arises. This ensures that if something goes wrong, a lengthy code change process is not needed to directly disable that portion of code.
- Add telemetry to your bug-fix to track it in the future to check if the fix is properly resolving the issue, or to see if new issues arise.
In more complex issues, you may need to rely on external data, such as logs, session ids, telemetry, database queries, etc.. You may potentially need to ask the customer or affected persons for more details and logs directly. This really depends on the tooling per company, but if you become good at analyzing this sort of data, as well as knowing where to emit your own telemetry in the code to help you debug issues in the future as well, it becomes a valuable sought-after skill. Since bugs are not always reproducible on your machine, understanding how to track down and analyze issues from telemetry can be crucial.
For some very specific tips...
- There are some tools I find very helpful in identifying code and its purpose, for example, if your editor has the GitLens extension, it allows you to preview the PR description from any line of code to help understand why it was written.
- Sometimes, if you are unsure where to even start, it is useful to identify unique strings and variable names and search the codebase for those references and place breakpoints as you go to see if they can hit.
- Additionally, make use of writing tests to help identify issues and allow the debugger to take you through the code from the test. Tests are great at scoping down and catching bugs at any point from now into the future before the bugs get lost in the depths of the code.
- Make full use of stack traces while debugging to pinpoint where variables and code paths came from.
- Make use of your teammates, if you get lost, never let yourself keep digging deeper with no goal forever and ask questions - use git blame to see who made changes to lines and consider contacting them to help understand an unfamiliar code area, even if they are not on your immediate team.
- Make sure your changes and fixes are gated properly if your codebase has a gating system to proactively enable/disable code for customers if an issue arises. This ensures that if something goes wrong, a lengthy code change process is not needed to directly disable that portion of code.
- Add telemetry to your bug-fix to track it in the future to check if the fix is properly resolving the issue, or to see if new issues arise.
Prema Sequeira
Engineering Manager
27
Answers
Allentown, Pennsylvania
Updated
Prema’s Answer
Hi Donovan,
Here is my experience as a programmer. There are many reasons your code needs to be fixed.
Your first priority is to reproduce the issue. While as a student, most of these issues are caused by something you did or have access to. In the real world, they are caused by software that has existed for years, environments and settings that vary, and data that may be the issue.
All software has some debugging tools, ALWAYS use them.
Use the internet, very often code snippets and solutions are freely available
Use chatgpt, copilot etc and make sure to find which ones help with your particular coding task. Some programmers found chatgpt useful in some instances and copilot in others.
There are different ways of coding, assuming you code test code test, your issue might be limited to the area where you just coded. For others who code a larger chunk and test its harder to find issues as there could be multiple points of failure. In this case, address each issue individually before going to the next issue. Patience is key.
You would be surprised at how often the issue is not the logic but a syntax or misspelling issue. You only see what you think you coded. Sometimes having someone look at it solves the issue quickly.
In the real world a lot of issues are caused by not understanding the code base. Every code base has nuances that you just don't know what you don't know so I would recommend asking for help, solves issues much faster. Obviously, you need to do some due diligence but if after a certain point of time you are not making progress someone with more experience is the best option. In these cases, i would recommend documenting the issue and adding comments so future programmers don't struggle with those lines of code.
Good Luck! Hopefully this helps.
Here is my experience as a programmer. There are many reasons your code needs to be fixed.
Your first priority is to reproduce the issue. While as a student, most of these issues are caused by something you did or have access to. In the real world, they are caused by software that has existed for years, environments and settings that vary, and data that may be the issue.
All software has some debugging tools, ALWAYS use them.
Use the internet, very often code snippets and solutions are freely available
Use chatgpt, copilot etc and make sure to find which ones help with your particular coding task. Some programmers found chatgpt useful in some instances and copilot in others.
There are different ways of coding, assuming you code test code test, your issue might be limited to the area where you just coded. For others who code a larger chunk and test its harder to find issues as there could be multiple points of failure. In this case, address each issue individually before going to the next issue. Patience is key.
You would be surprised at how often the issue is not the logic but a syntax or misspelling issue. You only see what you think you coded. Sometimes having someone look at it solves the issue quickly.
In the real world a lot of issues are caused by not understanding the code base. Every code base has nuances that you just don't know what you don't know so I would recommend asking for help, solves issues much faster. Obviously, you need to do some due diligence but if after a certain point of time you are not making progress someone with more experience is the best option. In these cases, i would recommend documenting the issue and adding comments so future programmers don't struggle with those lines of code.
Good Luck! Hopefully this helps.