Types of Errors in Programming: 10 Common Errors and How to Fix Them

“If debugging is the process of removing software bugs, then programming must be the process of putting them in.” This observation, often attributed to Edsger Dijkstra, rings true for every developer since. Errors are an unavoidable part of writing code. The difference between a junior developer and a senior one is not the number of errors they make, but how fast they recognize and fix them.

Understanding the different types of errors in programming is the first step toward faster debugging. When you can look at unexpected behavior and immediately classify the error type, you narrow your search from “something is wrong” to a specific category with known causes and known fixes.

This guide covers the 10 most common types of programming errors, with code examples and concrete fixes for each one. Whether you are debugging your first Python script or reviewing production code, these are the errors you will encounter again and again.

What are errors in programming?

Errors in programming are flaws in source code that cause a program to produce incorrect results, behave unexpectedly, or fail to run at all. Every programming language generates errors, and every codebase of meaningful size contains them. The key is understanding which type of error you are dealing with so you can apply the right debugging strategy.

The 10 most common types of programming errors are:

Syntax errors

Logic errors

Runtime errors

Compilation errors

Arithmetic errors

Type errors

Resource errors

Interface errors

Concurrency errors

Off-by-one errors

Each type has distinct causes, symptoms, and fixes. Let’s walk through them.

1. Syntax errors

If you’ve been programming for more than a week, you’ve hit this one. A syntax error occurs when code violates the grammatical rules of the programming language. The interpreter or compiler cannot parse the code, so the program never runs. Syntax errors are the most common type of error for beginners and the easiest to fix, because the error message tells you exactly where the problem is.

What causes syntax errors

Missing punctuation, mismatched brackets, incorrect indentation, misspelled keywords, and forgotten colons all cause syntax errors. In Python, indentation itself is part of the syntax, so a single misplaced space can break your program.

# Syntax error: missing colon after if statement
def check_age(age):
if age >= 18
print("Adult")
else:
print("Minor")

How to fix syntax errors

Read the error message. Python’s SyntaxError output points to the exact line and often the exact character where parsing failed. In the example above, Python reports the error on the if line. Adding the missing colon fixes it:

# Fixed: colon added after condition
def check_age(age):
if age >= 18:
print("Adult")
else:
print("Minor")

Modern IDEs catch most syntax errors before you run the code. If you see red underlines in your editor, fix them before executing anything. Linters like pylint or flake8 will also flag syntax issues on save.

2. Logic errors

A logic error occurs when code runs without crashing but produces the wrong result. The syntax is valid, the program executes, and no error message appears. The output is incorrect because the programmer’s reasoning was flawed. Logic errors are among the hardest types of programming errors to find because there is no error message pointing you to the problem.

A real-world scenario

A developer writes a function to calculate the area of a rectangle. The code runs, the tests pass for square inputs (where length equals width), and it ships. Weeks later, a user reports wrong values. The developer used addition instead of multiplication. The function worked for equal sides (2 + 2 = 4, same as 2 * 2) but broke everywhere else. That is a logic error in miniature: code that works under some conditions and silently fails under others.

What causes logic errors

Incorrect formulas, wrong operator choices, flawed conditional logic, and misunderstood requirements all cause logic errors. They happen when the code does what you told it to do instead of what you meant it to do.

# Logic error: using addition instead of multiplication
def calculate_area(length, width):
return length + width # Should be length * width

result = calculate_area(5, 3)
print(result) # Outputs 8, but correct answer is 15

How to fix logic errors

Write unit tests that compare actual output against expected output. When a test fails, the gap between expected and actual results tells you what the logic error is doing. For the example above:

# Fixed: correct operator
def calculate_area(length, width):
return length * width

# Unit test to catch this in the future
assert calculate_area(5, 3) == 15
assert calculate_area(0, 10) == 0

Rubber duck debugging works well for logic errors. Explain your code line by line to someone (or something). The act of articulating what each line does often reveals where your logic diverges from your intent.

3. Runtime errors

# Runtime error: IndexError
scores = [85, 92, 78]
print(scores[3]) # Only indices 0, 1, 2 exist

This code is syntactically correct, but it crashes the moment Python executes it. That is a runtime error: code that compiles or parses without issues, then hits a condition it cannot handle during execution. In Python, runtime errors raise exceptions like ZeroDivisionError, FileNotFoundError, or IndexError (see the full list of Python’s built-in exceptions).

What causes runtime errors

Division by zero, accessing a list index that does not exist, opening a file that has been deleted, running out of memory, and passing invalid input all cause runtime errors. They depend on conditions that exist only when the program runs, which is why the compiler cannot catch them.

How to fix runtime errors

Use exception handling to catch predictable failure points. Validate input before processing it. Check that files exist before opening them. Verify list lengths before accessing indices.

# Fixed: bounds checking before access
scores = [85, 92, 78]
index = 3

if index < len(scores):
print(scores[index])
else:
print(f"Index {index} is out of range. List has {len(scores)} items.")

For critical code paths, use try/except blocks to handle exceptions gracefully instead of letting the program crash:

try:
result = 100 / user_input
except ZeroDivisionError:
print("Cannot divide by zero. Please enter a non-zero value.")

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4. Compilation errors

A compilation error occurs when the compiler cannot translate source code into executable code. The program never runs. Compilation errors are common in statically typed languages like Java, C++, and Go, where the compiler checks types and declarations before producing an executable. Python is interpreted rather than compiled, but tools like mypy perform similar static checks. Undeclared variables, type mismatches, missing imports, incompatible function signatures, and referencing classes or methods that do not exist all trigger compilation errors. In compiled languages, any violation of the type system stops the build.

# Python equivalent: using a variable before defining it
def greet():
print(message) # NameError: 'message' is not defined

greet()

How to fix compilation errors

Read the compiler output from top to bottom. Fix the first error before addressing later ones, because a single early error often causes a cascade of secondary errors. In the example above, defining the variable before use fixes it:

# Fixed: define variable before use
def greet():
message = "Hello, world!"
print(message)

greet()

In Python, running mypy catches many issues that a traditional compiler would flag. Adding type hints to your functions gives you compile-time-style checking in an interpreted language:

def add_numbers(a: int, b: int) -> int:
return a + b

5. Arithmetic errors

An arithmetic error occurs when a mathematical operation produces an incorrect or undefined result. Division by zero is the most famous arithmetic error, but floating-point precision issues cause far more bugs in production code. Every programmer eventually discovers that 0.1 + 0.2 does not equal 0.3 in most languages, and every programmer is annoyed by it. In 1996, the Ariane 5 rocket self-destructed approximately 37 seconds after launch because the software converted a 64-bit floating-point number to a 16-bit signed integer, causing an overflow. The failure destroyed the rocket and its payload of four Cluster satellites, at an estimated cost of $370 million as detailed in the inquiry board's report.

What causes arithmetic errors

Division by zero, integer overflow, floating-point precision loss, and incorrect order of operations all cause arithmetic errors. Floating-point errors are especially dangerous because they produce results that look almost correct.

# Arithmetic error: floating-point precision
price = 0.1 + 0.2
print(price) # Outputs 0.30000000000000004
print(price == 0.3) # Outputs False

How to fix arithmetic errors

For financial calculations or any context where precision matters, use the decimal module instead of floating-point arithmetic. For comparisons, use a tolerance threshold instead of exact equality.

from decimal import Decimal

# Fixed: using Decimal for precise arithmetic
price = Decimal("0.1") + Decimal("0.2")
print(price) # Outputs 0.3
print(price == Decimal("0.3")) # Outputs True

# Alternative: tolerance-based comparison for floats
import math
print(math.isclose(0.1 + 0.2, 0.3)) # Outputs True

Always validate denominators before division, and use range checks when converting between numeric types.

6. Type errors

# Type error: concatenating string and integer
age = 25
message = "I am " + age + " years old"
# TypeError: can only concatenate str (not "int") to str

Python refuses to guess what you meant here, and that is a good thing. A type error occurs when an operation receives a value of the wrong data type. Trying to add a string to an integer, calling a method that does not exist on a given type, or passing a list where a function expects a dictionary all produce type errors. In dynamically typed languages like Python and JavaScript, type errors appear at runtime. In statically typed languages, the compiler catches them before the program runs.

What causes type errors

Implicit type coercion, unvalidated user input, inconsistent return types from functions, and incorrect assumptions about data structures all cause type errors. APIs that return different types depending on conditions (a single object vs. a list of objects) are a frequent source.

How to fix type errors

Convert types explicitly before combining them. Use f-strings in Python to handle type conversion automatically. Add type hints and run mypy to catch type mismatches before runtime.

# Fixed: explicit type conversion
age = 25
message = "I am " + str(age) + " years old"

# Better: use f-strings
message = f"I am {age} years old"

For functions that accept multiple types, validate the input type at the start:

def process_items(items):
if not isinstance(items, list):
items = [items] # Wrap single item in a list
for item in items:
print(item)

7. Resource errors

A resource error occurs when a program cannot access or properly manage system resources like memory, files, network connections, or database handles. Memory leaks, unclosed file handles, and exhausted connection pools are all resource errors. They often do not appear during development and testing, then cause outages in production under real traffic. The usual culprits: opening resources without closing them, creating objects in loops without freeing memory, holding database connections longer than necessary, and failing to handle cleanup during exceptions.

# Resource error: file handle never closed
def read_config():
f = open("config.txt", "r")
data = f.read()
# If an exception occurs here, the file is never closed
return data

How to fix resource errors

Use context managers, known as with statements in Python, to guarantee that resources are released, even if an exception occurs. Context managers handle cleanup automatically.

# Fixed: context manager ensures file is closed
def read_config():
with open("config.txt", "r") as f:
data = f.read()
return data

For memory issues, use profiling tools like Python's tracemalloc to identify which objects are consuming memory. For database connections, use connection pooling libraries that manage lifecycle automatically.

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8. Interface errors

An interface error occurs when two systems or components fail to communicate correctly. Mismatched API request formats, incorrect function call signatures, wrong data encodings, and incompatible library versions all produce interface errors. As software grows more modular and API-dependent, interface errors have become one of the most common types of programming errors in production systems.

What causes interface errors

Sending JSON when an API expects XML, using the wrong HTTP method, passing arguments in the wrong order, and upgrading one library without updating the code that depends on it all cause interface errors.

# Interface error: wrong argument order
def create_user(name, email, role):
return {"name": name, "email": email, "role": role}

# Caller passes arguments in wrong order
user = create_user("admin", "Alice", "alice@example.com")
# Results in: {"name": "admin", "email": "Alice", "role": "alice@example.com"}

How to fix interface errors

The fix is almost always simpler than you'd expect. Use named arguments (keyword arguments) instead of relying on position. This makes the intent clear and prevents ordering mistakes.

# Fixed: use keyword arguments
user = create_user(name="Alice", email="alice@example.com", role="admin")

For API integrations, validate responses before processing them. Check status codes, verify response schemas, and handle unexpected formats gracefully. Pin dependency versions in your requirements file to prevent surprise breaking changes from library updates. Using reusable code templates for API call boilerplate also reduces the chance of misformatted requests.

9. Concurrency errors

A concurrency error occurs when multiple threads or processes access shared resources simultaneously and produce unpredictable results. Race conditions, deadlocks, and data corruption from unsynchronized writes are all concurrency errors. They are notoriously difficult to debug because they depend on timing and may not appear consistently.

What causes concurrency errors

Shared mutable state without synchronization is the root cause of almost every concurrency error. When two threads read and write the same variable without coordination, the result depends on which thread runs first, and that order can change between executions.

import threading

# Concurrency error: race condition on shared counter
counter = 0

def increment():
global counter
for _ in range(100000):
counter += 1 # Not atomic: read, increment, write

thread1 = threading.Thread(target=increment)
thread2 = threading.Thread(target=increment)
thread1.start()
thread2.start()
thread1.join()
thread2.join()

print(counter) # May not equal 200000 due to unsynchronized access

Note: On CPython 3.10 and later, the GIL may prevent this race condition from producing visibly incorrect results in this specific example. The code is still unsafe. No Python implementation guarantees thread safety through the GIL, and future releases may change this behavior.

How to fix concurrency errors

Use locks to synchronize access to shared resources. In Python, the threading.Lock class ensures that only one thread modifies the shared variable at a time.

import threading

# Fixed: lock protects shared state
counter = 0
lock = threading.Lock()

def increment():
global counter
for _ in range(100000):
with lock:
counter += 1

thread1 = threading.Thread(target=increment)
thread2 = threading.Thread(target=increment)
thread1.start()
thread2.start()
thread1.join()
thread2.join()

print(counter) # Always 200000

Better yet, avoid shared mutable state entirely. Use message queues, immutable data structures, or process-based parallelism where each process has its own memory space. Python's multiprocessing module and the concurrent.futures library provide safer patterns than manual thread management.

10. Off-by-one errors

There are only two hard problems in computer science: cache invalidation, naming things, and off-by-one errors. An off-by-one error occurs when a loop iterates one time too many or one time too few, or when an index is one position away from the correct value. These errors are so common that they have their own abbreviation: OBOE. The name comes from the fact that the error is always exactly one unit off, whether that is one extra iteration, one missing element, or one wrong index.

The most common sources of off-by-one errors come down to a few recurring confusions:

What the developer writes | What they meant | The result
range(len(items) - 1) | Iterate all items | Skips the last element
for i in range(1, len(items)) | Start from the first item | Skips index 0
while i <= len(items) | Process all items | IndexError on the final iteration
items[len(items)] | Get the last element | IndexError (should be len(items) - 1)

Fencepost problems are a classic example: if you need to build a 100-meter fence with posts every 10 meters, you need 11 posts, not 10.

# Off-by-one error: skips the last element
def print_all_items(items):
for i in range(len(items) - 1): # Should be range(len(items))
print(items[i])

fruits = ["apple", "banana", "cherry"]
print_all_items(fruits)
# Prints "apple" and "banana" but misses "cherry"

How to fix off-by-one errors

In Python, prefer iterating directly over collections instead of using index-based loops. When you do need indices, double-check your boundary conditions with the first and last elements.

# Fixed: iterate directly over the collection
def print_all_items(items):
for item in items:
print(item)

# When you need the index, use enumerate
def print_numbered_items(items):
for i, item in enumerate(items):
print(f"{i + 1}. {item}")

Write boundary tests that check the first element, last element, and empty input. Off-by-one errors hide in edge cases, so test the edges explicitly.

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How to prevent programming errors

No technique eliminates all bugs, but a combination of practices catches most of them before they reach production. If you are new to error handling in Python, start with the official Errors and Exceptions tutorial.

Write tests first. Unit tests define expected behavior before you write the code. When you add a new feature, write a test that describes what correct output looks like. When you fix a bug, write a test that reproduces it. Over time, your test suite becomes a safety net that catches regressions.

Tools like pylint, flake8, and mypy catch syntax errors, type mismatches, and suspicious patterns before you run the code. Configure them to run automatically on save or as part of your CI pipeline. A linter that runs on every save will catch more bugs than a code review that happens once a week.

Review code in pairs. A second set of eyes catches logic errors, interface mismatches, and edge cases that the original author missed. Code review remains one of the most effective quality practices for reducing defect density, and it costs nothing beyond time.

When you hit an error, resist the urge to make random changes. Read the error message, form a hypothesis, test it, and iterate. Use print() statements or a debugger like pdb to inspect state at each step. Systematic debugging feels slower in the moment, but it closes bugs faster than trial and error.

Use code Snippets for repetitive patterns. Error handling blocks, logging templates, and boilerplate structures are easy to get wrong when you retype them from memory. TextExpander Snippets let you store correct patterns and insert them with a short abbreviation. For example, a try/except block with proper logging, a file-handling context manager, or a standard function docstring can all live as Snippets that expand in any editor. Your team can share a Snippet library so everyone uses the same tested patterns instead of writing error-prone boilerplate from scratch. Try TextExpander free to build your own library of error-free code templates.

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Frequently asked questions

What are the three main types of errors in programming?

The three main types of errors in programming are syntax errors, logic errors, and runtime errors. Syntax errors break the rules of the language and prevent the program from running. Logic errors produce incorrect results without crashing. Runtime errors cause the program to crash during execution when it encounters a condition it cannot handle, such as dividing by zero or accessing a file that does not exist.

What is the difference between a syntax error and a logic error?

A syntax error prevents the program from running at all because the code violates the language's grammar rules. The compiler or interpreter catches it immediately and tells you exactly where the problem is. A logic error lets the program run without any error message, but the output is wrong because the programmer's reasoning was incorrect. Syntax errors are easy to find and fix. Logic errors require you to compare expected output against actual output to identify the flaw.

How do I debug programming errors?

Start by reading the error message carefully. It tells you the error type, the file, and the line number. Next, reproduce the error with the smallest possible input. Then use print() statements or a debugger to inspect variable values at each step of execution. Compare what the variables contain against what you expected them to contain. The point where reality diverges from expectation is where the bug lives. For errors with no error message (logic errors), write unit tests that assert expected output and let the failing test guide you to the problem.

What are the hardest programming errors to find?

Concurrency errors like race conditions and deadlocks are the hardest to find because they depend on timing and may not appear consistently. A program can pass all tests and run correctly thousands of times, then fail unpredictably under different load conditions. Logic errors rank second because they produce no error message. Off-by-one errors are also notoriously tricky because the output looks almost correct, differing by only a single element or iteration.