Leveraging 11 Advanced Debugging Techniques in Custom Software Development

Custom software development is an intricate process. Thus, even the most expert coders can write code that harbors hidden bugs. The only way to tackle these errors is through robust and
advanced debugging techniques.

This blog has 11 most efficient debugging techniques a software development agency can leverage.

So, let's begin!

11 modern debugging techniques in custom software development

Meticulous debugging in the software development cycle is essential for efficient deployment. Below are some efficient techniques:

1. Brute force method

This straightforward approach involves strategically inserting console.log() statements (JavaScript) or print() statements (Python) throughout the code. These statements output the values of variables at specific points in the program's execution. By examining the printed values, developers can pinpoint discrepancies between expected and actual values, ultimately leading to the erroneous code section.
Here's a code sample in Javascript:

function calculateArea(length, width) {
  // ... potentially buggy code ...
  console.log("Length:", length);
  console.log("Width:", width);
  console.log("Calculated Area:", area);
  return area;
}
const calculatedArea = calculateArea(10, 5);
console.log("Expected Area:", 50);

Here, "console.log" statements print the "length", "width", and calculated "area" values. By comparing these printed values with the expected area (50), the developer can identify any discrepancies and locate the source of the error.

2. Cause elimination method

This systematic approach in custom software development involves creating a list of potential causes for the observed error. Each potential cause is then eliminated through testing or code modifications. If eliminating a cause resolves the error, that cause was the culprit. This method becomes more efficient with a clear understanding of the error behavior and the codebase involved.

For example, an error can be a function returning unexpected results.

Potential causes can be as follows:

• An incorrect formula is used in calculations.
• Data type mismatch between variables.
• Off-by-one error in loop iterations. Testing methods can be as follows:
• Verify the formula against known good implementations.
• Ensure variables are of the expected data type (e.g., number vs. string).
• Check loop conditions and counters for potential off-by-one errors.

Systematically eliminating each potential cause helps the developer identify the origin of the error.

3. Backtracking

This technique involves starting from where the error manifests and working backward through the code's execution. By examining the values of variables and the logic flow at each step, the developer can trace the error back to its source. Backtracking is particularly useful for identifying errors that cause unexpected behavior or crashes later in the program's execution.

An example in Python can be as follows:

def process_data(data):
  # ... potentially buggy code ...
  if result is None:
    raise ValueError("Data processing failed")
# Error occurs here (ValueError: Data processing failed)
processed_data = process_data(user_input)

The error occurs during "process_data" but doesn't provide specific details. Backtracking involves examining the code within "process_data" line by line, looking for potential causes that would lead to a "None" value for "result."

4.Program slicing

Like backtracking, program slicing focuses on a specific variable's value at a particular point in the code. Custom software development services include this technique, which involves analyzing the "slices" of code that influence that value. Developers can analyze smaller code sections that impact a specific variable or program output. This allows for a more focused approach, aiding in faster identification of the error's root cause. Static program analysis tools can be utilized to automate program slicing for complex codebases.

5.Thread Management

Threads are lightweight units of execution within a single process. They share the process's memory space and resources but allow for concurrent execution of code blocks. Threading libraries provide APIs for thread creation, synchronization, and communication. Popular threading models in custom software development include the following:
POSIX threads (pthread): Library for creating and managing threads on Unix-like systems.
Java Thread API: Built-in Java API for thread creation, synchronization, and scheduling.
C# Thread Class: Class for creating and managing threads in C# applications.
Below is an example of code in Java Thread API:

// A class that implements Runnable to define the task to be performed by a thread
public class MyRunnable implements Runnable {
@Override
public void run() {
// Print the name of the current thread
System.out.println("Thread: " + Thread.currentThread().getName());
}
}

// The main class containing the entry point of the program
public class Main {
public static void main(String[] args) {
// Create a new thread with MyRunnable as its task
Thread thread1 = new Thread(new MyRunnable());
thread1.start(); // Start the thread, which calls the run() method of MyRunnable

// Create another new thread with MyRunnable as its task
Thread thread2 = new Thread(new MyRunnable());
thread2.start(); // Start the thread, which calls the run() method of MyRunnable
}
}

Here, the "MyRunnable" class implements the "Runnable" interface and defines the code to be executed by the thread. The "main" method creates two threads with the same code and starts their execution using the "start()" method.

6.Breakpoints and stepping

These tools enable developers to pause program execution at specific points (breakpoints) and examine the program's state. This allows for a step-by-step inspection of variables, function calls, and memory usage in custom software development.
Integrated Development Environments (IDEs): Popular IDEs are Visual Studio Code, PyCharm, and IntelliJ IDEA. They offer robust debugging functionalities with breakpoint settings and step execution options.
Debuggers: Standalone debuggers like GDB (GNU Debugger) or LLDB (Low-Level Debugger) provide granular control over program execution. These can also be used across various programming languages.
Below is a code sample of Python with PyCharm:
def calculate_area(length, width):

    area = length * width
    print("Area:", area)
# Set breakpoint at the line calculating area
length = 5
width = 10
# Run the program in debug mode
# PyCharm pauses execution at the breakpoint
area = length * width # This line is paused
print("Area:", area)

A breakpoint is set at the line "area = length * width." When the program runs in debug mode, execution pauses at this point. This allows the developer to inspect the values of "length" and "width" before proceeding.

7.Binary search

Isolating the source of an error can be time-consuming for large datasets or complex algorithms. Binary search offers an efficient approach by repeatedly dividing the search space in half, focusing on the half most likely to contain the error.
Divide-and-conquer algorithms: Binary search falls under the category of divide-and-conquer algorithms. This breaks down problems into smaller, easier-to-solve subproblems in custom software development.
Time complexity: Binary search boasts a time complexity of O(log n), significantly faster than a linear search (O(n)) for large datasets (n).
Below is a code sample in Javascript:

function binarySearch(arr, target) {
  let low = 0;
  let high = arr.length - 1;
  while (low <= high) {
    let mid = Math.floor((low + high) / 2);
    if (arr[mid] === target) {
      return mid;
    } else if (arr[mid] < target) {
      low = mid + 1;
    } else {
      high = mid - 1;
    }
  }
  return -1; // Target not found
}
const numbers = [1, 3, 5, 7, 9];
const target = 7;
const index = binarySearch(numbers, target);
if (index !== -1) {
  console.log("Target found at index:", index);
} else {
  console.log("Target not found");
}

This code implements a binary search function that considers the target value and sorted array as inputs. It iteratively halves the search space until the target is found or the entire array is searched.

8.Rubber ducking

This lighthearted technique involves explaining the code and thought process to an inanimate object, like a rubber duck. Verbalizing the steps can often reveal logical flaws or misunderstandings in the code.
Pair programming: While a rubber duck can't offer feedback, pair programming, where two developers work together, utilizes a similar principle. Explaining code to a partner can often lead to the identification of errors.

9.Log analysis

Logs are detailed records of program execution, including function calls, variable values, and error messages. Analyzing these logs gives valuable insights into program behavior and pinpoint the root cause of issues.
Logging libraries: Most programming languages offer logging libraries that simplify the process of writing informative log messages. Popular options include Log4j (Java), NLog (.NET), and Winston (Node.js).
Log management tools: Centralized log management tools can aggregate and analyze logs from various sources in custom software development. This helps identify patterns and trends that might indicate errors.
Here's a code sample from Python with a logging module:
import logging

logging.basicConfig(filename="my_app.log", level=logging.DEBUG)
def calculate_area(length, width):
    if length <= 0 or width <= 0:
        logging.error("Invalid input: Length and width must be positive")
        return None
    area = length * width
    logging.debug(f"Calculated area: {area}")
    return area
# Example usage
try:
  result = calculate_area(5, 10)
  print("Area:", result)
except TypeError as e:
  print("An error occurred:", e)

Here, the "logging" module is used to write debug messages to a file named "my_app.log." The "calculate_area" function now includes a check for invalid input (non-positive length or width) and logs an error message if encountered. Additionally, it logs a debug message with the calculated area.

10.Clustering bugs

Similar bugs often stem from a common underlying issue. By grouping bugs with similar symptoms (clusters), developers can identify the root cause more efficiently. This technique is particularly helpful for complex codebases with numerous bugs.
Bug tracking systems: Bug tracking systems like Jira, Asana, or Trello can be used to categorize and group bugs based on various criteria, including symptoms, severity, or affected code sections.
Machine learning: This and other advanced technologies can be leveraged to automate bug clustering based on analyzing code patterns and bug reports.

Debugging multi-threaded and multiprocess Applications in custom software development

Debugging multi-threaded and multiprocess applications brings unique challenges to software development firms compared to single-threaded programs. The concurrent nature of execution can lead to race conditions, deadlocks, and other synchronization issues. Here's how to tackle these challenges:

Debugging tools

Popular debugging tools with multi-threading support include the following:

• Visual Studio with parallel debugging: Offers advanced features for debugging multi-threaded and asynchronous applications in C# and other .NET languages.
• Eclipse with Thread Debugging: Provides thread-aware debugging capabilities for Java applications.
• GDB with pthreads extension: Enables debugging of multi-threaded applications written in C and C++.
• Chrome DevTools: Integrated debugger for Chrome browser, offering extensive features for web development.
• LLDB (Low-Level Debugger): Modern debugger supporting a wide range of languages and platforms.

Synchronization techniques

To prevent race conditions and deadlocks, developers should employ synchronization mechanisms like

• Mutexes: Mutual exclusion locks that ensure access to a shared resource for only one thread at a time. Semaphores: Signaling mechanisms that control access to a limited number of resources.

• Monitors (Java): High-level synchronization constructs encapsulating shared data and access methods.

  Debugging strategies

• Identify thread-related issues: Analyze error messages and logs for clues related to specific threads or concurrent operations.

• Isolate the problem thread: Utilize debugging tools to identify the thread experiencing the issue and examine its call stack and variable states.

• Simplify and reproduce: Break down the problematic code into smaller, testable units to isolate the root cause.

• Utilize debugging tools: Leverage the features of multi-threaded debuggers to step through code, inspect thread states, and identify synchronization problems.

  Conclusion

  This was all about advanced debugging techniques to help developers solve development complexities. With these techniques, any custom software development company can minimize deployment delays and deliver solid applications.