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.

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). 

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.

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. 

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).

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.  

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.   

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.

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.

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.

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

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.

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 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. 

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 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. 

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 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.