What is debugging?
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What is debugging?

Debugging is the process of finding and resolving coding errors or “bugs” in a software program. Bugs (logical errors, runtime errors, syntax errors and others) can lead to crashes, incorrect or inaccurate outputs, security vulnerabilities, data loss and more.

Debugging is critical for preventing software functionality issues. However, debugging isn’t just about fixing code; it’s also about understanding why a bug happened in the first place and finding ways to prevent it in the future. If software developers can identify the root cause of a bug, they can improve the overall stability, reliability and performance of their products.

Both debugging and software testing are vital to successful software engineering, but It’s important to note that debugging and testing are related—but not synonymous—processes. Testing allows developers to investigate what happens if there’s a bug or error in a program’s source code. While this can help developers understand the bug’s impact on a program, it cannot help them locate or fix the bug. Debugging, on the other hand, starts after the bug has been identified, and helps teams identify bugs and prevent recurrences.

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The debugging process

Debugging typically involves six steps.

Step 1: Reproduce the conditions

The debugging process requires specificity; engineers cannot rely on a second-hand description of the problem to accurately diagnose it. Therefore, the first step in the debugging process is to replicate the conditions that caused the bug to appear. Reproducing the bug allows programmers and/or engineers to observe the error firsthand and collect contextual data for the rest of the debugging process.

Step 2: Find the bug

The next step is to poinpoint the source of the bug as accurately as possible by thoroughly scrutinizing the code and reviewing any available logs. For this step developers typically rely on debugging tools (see 'Debugging tools' below). 

Step 3: Determine the root cause

Here developers determine what caused the bug by examining the logic and flow of the code and how different components of the code interact under the specific conditions in which the bug occurs.

Step 4: Fix the bug

This typically involves troubleshooting and revising the code to rectify the issue, and recompiling and re-running the software to make sure the bug is fixed. This typically involves several iterations as first attempts can fail and/or inadvertently introduce new bugs. Most developers use a version control system to track changes, so they can easily roll back any modifications that don't solve the problem or create new ones. 

Step 5: Test to validate the fix

Tests run after a bug fix include

  • Unit tests, which test the individual code segment changed to fix the bug

  • Integration tests, which test the whole module containing the fixed bug

  • System tests, which test the entire system in which changed module runs

  • Regression tests, which ensure that that the fixed code does not impact application performance (i.e., that the application hasn't regressed as a result of the bug fix).
Step 6: Document the process

As a final step developers record the details of the repair process, including what caused bug, how it was fixed and any other relevant information. Documentation can prove invaluable if similar bugs occur in the future.  

Debugging strategies

The process of debugging can be challenging and labor-intensive. Understanding various approaches to the debugging process can make the task much easier to manage.


In this approach, developers work backward from the point the error was detected to find the origin of the bug. Specifically, they retrace the steps the program took with the problematic source code to see where things went wrong. Backtracking can be particularly effective when used alongside a debugger.   

Cause elimination

A hypothesis-driven debugging technique, cause elimination requires the team to speculate about the causes of the error and test each possibility independently. This approach works best when the team is very familiar with the code and the circumstances surrounding the bug.

Divide and conquer

When debugging large code bases, teams can divide lines of code into segments—functions, modules, class methods or other testable logical divisions—and test each one separately to locate the error. Once the problem segment is identified, it can be divided further and tested until the source of the bug is identified.

Print/log debugging

The print/log debugging strategy involves adding print statements and/or “logs” to the code to display values of variables, call stacks, the flow of execution and other relevant information. This approache is especially useful for debugging concurrent or distributed systems where order of execution can impact the program's behavior.

Rubber duck debugging

In this approach, developers 'explain' the code, line by line, to a 'rubber duck' (or to any inanimate object). The idea is that by trying to explain the code to someone else, developers can better understand its logic (or lack thereof) and spot bugs more easily.

Automated debugging

Automated debugging relies on analytics, artificial intelligence (AI) and machine learning algorithms to automate one or more steps of the debugging process.

Brute force debugging

Typically deployed only when other methods have failed, brute force debugging involves going through the entire codebase, line by line, to identify the source of the problem. This approach can also be useful for debugging small programs when the engineer or programmer doing the debugging isn't familiar with the codebase.

Debugging tools

Debuggers are advanced tools and APIs that optimize software development by locating coding errors in an operating system or application development process. And they represent a massive—and growing— business. With the ever-expanding landscape of computer/mobile apps and programs, it’s no surprise that global debugger market is projected to grow significantly by the end of the decade1. Corporations are investing millions in the development of sophisticated debugging tools (like AI chatbots that can debug code)2, and university researchers are creating tools can debug video games autonomously3 and debug domain-specific programming languages4.

Tools and technologies can vary widely in their capabilities, but essentially, they all offer command-line interfaces that help organizations find and fix bug issues. Most also offer remote debugging features and tutorials that make the software more approachable to beginners.

What follow are a few of the most commonly used debugging tools.

Integrated development environments (IDEs)

IDEs offer computer programmers comprehensive features for software development. Many IDEs (e.g., Visual Studio, Eclipse, PyCharm) come with built-in debugging tools that allow developers to execute code line-by-line (step debugging), stop program execution at specified points (breakpoints), and examine the state of variables and memory at any point in time, among other capabilities. IDEs are also available as open-source plugins compatible with a range of programming languages (e.g., Java, Python, JavaScript, TypeScript, etc.) and scripting languages (such as PHP).

Standalone debuggers

Standalone debuggers like GDB (GNU Debugger) offer advanced debugging features, like conditional breakpoints and watchpoints, and facilitate reverse debugging (executing a program backwards). They tend to be more powerful and versatile than debuggers built into IDEs or other developer tools but they also have a steeper learning curve for users. 

Logging utilities

These tools provide ways to log a program's state at various points in the code; the logs can then be analyzed to find anomalies or problematic patterns. Logging is particularly useful for addressing bug issues that only occur in production environments, where interactive debugging may not be feasible. 

Static code analyzers

Static code analysis tools analyze code without executing it, looking for potential errors  and fixing bugs and deviations from coding standards. Instead of focusing on syntax (as interpreters and compilers do), these tools analyze the semantics of the source code, helping developers catch common programming mistakes and enforce consistent coding styles. 

Dynamic analysis tools

Essentially the opposite of static code analyzers, dynamic analysis tools monitor software as it runs to detect issues like resource leaks or concurrency problems. This helps development eams catch bugs that static analysis might miss, such as memory leaks or buffer overflows.

Performance profilers

Performance profilers enable developers to identify performance bottlenecks in their code. These systems can measure CPU usage, memory usage and IO operations, helping locate slow and/or inefficient.

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Global software debugging market analysis [2023-2030], Benzinga, 5 September 2022. (This page resides outside ibm.com)

Google’s Bard AI chatbot can now generate and debug code, TechCrunch+, 12 April 2023. (This page resides outside ibm.com)

Autonomously debugging video games, University of Southern California-Viterbi School of Engineering, 5 April 2023. (This page resides outside ibm.com)

An easier way to get bugs out of programming languages, MIT News, 7 April 2023. (This page resides outside ibm.com)