What is debugging?

Coding errors such as logical errors, runtime errors, syntax errors and semantic errors can lead to crashes, incorrect or inaccurate outputs, security vulnerabilities and data loss. Unlike software testing, which enables developers to investigate the effects of these errors in a program’s source code, debugging seeks the root cause and remediation of these errors.

Through the debugging process, software developers conduct root cause analysis to make sure bugs that are found in computer programs are fixed and do not happen again. Bugs can negatively impact software stability, reliability and user experience. Debugging tools and strategies help optimize the debugging process.

Debugging typically involves six steps:

-Reproduce the conditions
-Find the bug
-Determine the root cause
-Fix the bug
-Test to validate the fix
-Document the process

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 engineers to observe the error firsthand and collect contextual data for the rest of the debugging process.

The next step is to pinpoint the source of the bug as accurately as possible by thoroughly scrutinizing the code and reviewing any available logs. During this step, developers typically rely on debugging tools that make it easier to search through large pieces of code, rather than working manually.

Developers determine what causes a 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.

This step 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. These revisions might involve several iterations as first attempts can fail 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.

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 make sure that that the fixed code does not impact application performance, that is, that the application hasn't regressed because of the bug fix.

As a final step, developers record the details of the repair process, including what caused the bug, how it was fixed and any other relevant information. Documentation is a valuable tool for programmers to draw from when similar bugs occur in the future.

Understanding the types of bugs that a system faces enables software engineers and developers to find the appropriate way to fix broken code when an error arises. Examples of common errors that require debugging include:

A piece of code that violates the rules of a coding language cause a semantic error. Unlike a logical error, which produces an incorrect output, a semantic error will not produce a meaningful output.

This error occurs when a developer misses an element of code such as a parenthesis, comma or other typographical error. Unlike in written human languages where a sentence with a typographical error might still be understood, missing pieces of code immediately cause errors.

This type of bug includes syntax that is technically correct but has incorrect directions that cause an undesired output. Because the syntax is correct, these errors can be difficult to detect. When a system does not immediately crash and the exact location of the incorrect code can be time-consuming to find.

These errors happen when an application is running or starting up. Runtime errors can occasionally be fixed by refreshing, restarting or reinstalling an application. Other times they can be a signal of a program that requires more memory or of another type of error such as a logical error.

The process of debugging can be challenging and labor-intensive. Understanding various approaches to the debugging process can make managing the task more effective.

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 effective when used alongside a debugger.

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 familiar with the code and the circumstances surrounding the bug.

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. When the problem segment is identified, it can be divided further and tested until the source of the bug is identified.

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

In this approach, developers “explain or talk out” the code, line by line, to any inanimate object. The idea is that by trying to explain the code out loud, developers can better understand its logic (or lack thereof) and spot bugs more easily.

Automated debugging relies on analytics, artificial intelligence (AI) and machine learning algorithms to automate one or more steps of the debugging process. AI-powered debugging tools can search through large sets of code more quickly to identify errors or narrow down sections of code that can then be more thoroughly examined by a developer.

Automated systems can replicate lines of code and automate tests to verify that a system is working as intended. Automation plays a large role in both continuous integration (CI) and continuous delivery (CD), two approaches that accelerate the testing and deployment of new code.

Typically deployed 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 time-consuming approach can also be useful for debugging small programs when the engineer or programmer doing the debugging isn't familiar with the codebase.

Debuggers are advanced tools and APIs that optimize software development by locating coding errors in an operating system or application development process. Debuggers represent a massive and growing business. With the ever-expanding landscape of computer and mobile apps and programs, it’s no surprise that the 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 (such as AI chatbots that can debug code)² and university researchers are creating tools that 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.

Examples of debugging tools include:

IDEs offer computer programmers comprehensive features for software development. Many IDEs such as Visual Studio, Eclipse and PyCharm come with a “debug mode.” These built-in debugging tools enable developers to run 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 plug-ins compatible with a range of programming languages such as Java, Python, JavaScript and TypeScript and scripting languages such as PHP.

Standalone debuggers such as GNU Debugger (GDB) offer advanced debugging features including conditional breakpoints and watchpoints. They also facilitate reverse debugging, which is when programmers run 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 and require more technical expertise.

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 useful for addressing bug issues that occur in production environments, where interactive debugging might not be feasible.

Static code analysis tools analyze code without running 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 detect common programming mistakes and enforce consistent coding styles.

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

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 inefficient operations.

Testing and debugging are complimentary processes in the development of new code. While their outcomes differ, both testing and debugging are used to produce error-free code.

Testing enables software developers to understand what happens to a system when bugs occur. These tests help developers know when a system has failed and the effects the failure might have on a piece of software. Automated testing enables developers to run continuous tests on new code to gain insights into various scenarios. Testing is a key part of software development, but it does not explain why an error has occurred.

Debugging strategies and tools are what developers use to find the root cause of errors, fix them and document them to prevent them from reoccurring. When used together, debugging and testing help teams build a streamlined approach to developing code and creating better software products.