As a programmer, we have a lot to do and keep ourselves updated. It is normal for programmers to make mistakes and learn from them. But there are some silly mistakes that programmers make while coding and a few of them we have mentioned in this article.
In this coding mistakes article, we are going to share some most common error that should be taken care by coders.
But before we start, do you know what terms we used to call coding mistakes.
Here are some another names of coding mistakes:
- Bug: This is the most common term and refers to a general error in the code that causes unexpected behavior.
- Defect: Similar to a bug, but can also refer to a flaw in the design or requirements that leads to coding mistakes.
- Error: A general term for any mistake, including typos, syntax errors, or logical errors.
- Glitch: Often used for temporary or intermittent issues caused by coding mistakes.
- Mistake: A more neutral term simply acknowledging an error made in the code.
- Logic Error: A mistake in the program’s logic that causes it to produce incorrect results.
- Syntax Error: A violation of the programming language’s grammar rules.
- Compile-Time Error: An error caught by the compiler before the program even runs.
- Runtime Error: An error that occurs while the program is running.
- Security Vulnerability: A coding mistake that can be exploited for malicious purposes.
1. Code Formatting Mess
Beginners make the mistake of writing codes hard to understand. Messy code formatting can occur due to not focusing on the following:
♦ White space
♦ Indentation
♦ Use of comments
♦ Conventions naming and capitalization
♦ Style and spelling of variables, functions, and more
2. Not Using Lower And Upper case Consistently
The beginner also tends to use small case and large case variable names at random causing the code to become messy. It becomes difficult for others to understand the code.
3. Unrelated Variable And Function Names
Using bad variable and function names makes it difficult to debug or modify code. Others or even the programmer may not understand the code at a later date due to using bad functions and variable names.
4. Excess Commenting
Over commented code becomes difficult to understand compared to the ones without any comments. People spend more time reading the comments than looking at the code.
5. Not having the idea about the expressive power of the programming language
Not knowing the breadth of the programming language can result in not knowing the best way to express and communicate it. It can result in writing mediocre code that can never make an impact.
6. No idea of the difference between languages, frameworks, platforms, and IDEs
Confusing between the languages, frameworks, IDEs, and platforms can cause performance or stability issues that can affect the project. Also, the project may not meet the client’s demands.
???? You Must Also Read: How to Hire an ASP.NET Developer – Step by Step Guide
7. Forgetting to backup work
Every programmer must learn to back up their work. Else, the data can get lost or compromised due to data corruption, internet outages, or power failures. Losing sensitive data can result in disastrous consequences.
8. Not using debugging tools
Debugging is a time-consuming and complicated process. Not using the debugging tools can result in a loss of time and effort. In some cases, it can lead to not understanding the bug and taking steps to fix it.
9. Making the mistake of thinking “If conditional requiring a comparison
There is no need for a comparison value after the “if” conditional. While this mistake may not cause any harm, it only shows the programmer’s lack of understanding of the programming language.
10. Having overconfidence in programming skills
With new technology and framework coming up every day, a programmer must keep themselves updated. They need to learn new coding to keep up with the demands of the clients.
11. Not maintaining code quality
Focusing on the output and not maintaining code quality can cause issues when the programmer hands the messy code to another person who has to continue or maintain it.
12. Not having a plan while writing the code
Writing code without thinking, researching, and planning can lead to not understanding the project requirements. Following the sequence of researching, planning, writing the code, validating, and modifying will prevent disasters.
Major Programming Erorrs by Python Developers
13. Having no main() or No main function
An automatic import can cause a problem when your program performs expensive computations. A no main function can also trigger when you invoke process spawning multiple threads. Using a modified version on _main_ can avoid the problem.
14. Misunderstanding Float data types
Programmers using Python make the mistake of considering float as the Simple type. But, the floating data type has internal representation. This subtle characteristic makes all the difference.
15. The trouble of unstoppable scripts
Unstoppable scripts can develop when programmers try to execute infinite loops used inside the scripts. It can lead to unexpected results. But, you can avoid it by explicitly mentioning catching the exceptions.
16. Confusion regarding Boolean
It is the problem cropping up in many programming languages. Remember, “none” used in Boolean may not mean “true”. An undefined variable used later can result in unexpected results.
17. Misusing mutable function arguments
The use of misused mutable types can create a problem. It occurs due to the evaluation of the default value occurring after defining the function. Reference of empty list occurs in following calls.
18. Trying to list mutation inside the iteration
To mutate a collection during the iteration, you can make use of the techniques provided by Python. Else, you end up writing big code to filter the list manually.
19. Clash of Module Name
It occurs when you name a module similar to the standard libraries in Python. The dependency tree can make the issue trickier. To avoid naming clashes, have a good idea regarding the relative and absolute module path imports.
20. The difference between References, copies, and deep copies
Inexperienced programmers create a pointer and think they have created a variable’s independent copy. Using an assignation operator, you get references. Copy command gives a shallow copy while deep copy gives you a different variable with modifying feature.
21. To Use or Not Use Class variables
Inexperienced programmers can face difficulty when an object-oriented program tries to structure problems mimicking the real world. Using class variables may not seem necessary in python.
22. Function arguments by references and value
It is the problem encountered by programmers using python after working on C++ and Java. This problem occurs due to not understanding the working of the interpreter with arguments.
???? You Must Also Read: Top 11 .Net Development Companies
Programming Mistakes by Data Scientists
23. Hardcore Inaccessible Paths
Data scientists cannot run their code when their hardcore paths fail to have access. It results in having to check in a different place for changing the paths manually.
24. Avoiding sharing of data referenced in code
Data scientists must understand that data science requires data and code. To reproduce the results, others need access to the data. Hence, they need to share data referenced in the code. But, many people forget it.
25. Mixing code and data
Dumping data with their code in the same directory is a common mistake committed by data scientists. They tend to save reports, images, and other information in the same directory. It causes a mess.
26. Choosing to write Functions instead of DAGs
Coders first learn functions. So, they try to organize the data science code as a linearly running series of functions. It can only trigger problems making the code bad.
27. Git Commit Data With Source Code
Data scientists tend to version control their code. When they try to share the data, they feel adding the data files to the version control can work well. While it may work for small files, Git cannot handle large data.
28. Not Documenting Code
Data scientists who fail to document code can find it difficult to update the code after some time as they cannot recall what they did with the code. Not documenting can also confuse when someone else runs the code.
29. Not Writing Unit Tests
The code can break when the parameters, data, or user input chance without getting noticed by the coders. It results in bad output. Other than the coders, when someone makes a decision on bad output, it leads to bad decisions.
30. Saving Data In CSV Format
CSV does not have a schema. Saving data as CSV can result in people parsing dates and numbers again. Hence, CSV is not recommended for storing large datasets.
Conclusion:
These are some programming mistakes that a developer should avoid while coding. But, if you pay attention and take care of these issues, you can create code and programs more efficiently. These will save a lot of time for you in eliminating bugs and errors, for sure.
Here is a bonus for you. If you are fascinated by programming then you should see other modern technology blog –
– Future of ASP .NET – Is it frightening?
– Cool Facts About Coding/Programming You Didn’t Know
– Agile vs. Lean Software Development: Differences and Similarities
– .NET 6 – New Features, Release Date, Preview 1-7, Download Source
– Digital Healthcare Solutions – Everything You Need to Know
