Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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According to an analysis, a developer creates 70 bugs per 1000 lines of code on average. As a result, he spends 75% of his time on debugging. So sad!
Bugs are born in many ways. Creating side effects is one of them.
Some people say side effects are evil, some say they’re not.
I’m in the first group. Side effects should be considered evil. And we should aim for side effects free code.
Here are 4ways you can use to achieve the goal.
Just add use strict; to the beginning of your files. This special string will turn your code validation on and prevent you from using variables without declaring them first.
Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.
Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.
“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”
We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.
We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.
Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use
ast module, and wide adoption of the language itself.
Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:
As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:
The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.
A token might consist of either a single character, like
(, or literals (like integers, strings, e.g.,
Bob, etc.), or reserved keywords of that language (e.g,
def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.
Python provides the
tokenize module in its standard library to let you play around with tokens:
code = b"color = input('Enter your favourite color: ')"
for token in tokenize.tokenize(io.BytesIO(code).readline):
TokenInfo(type=62 (ENCODING), string='utf-8')
TokenInfo(type=1 (NAME), string='color')
TokenInfo(type=54 (OP), string='=')
TokenInfo(type=1 (NAME), string='input')
TokenInfo(type=54 (OP), string='(')
TokenInfo(type=3 (STRING), string="'Enter your favourite color: '")
TokenInfo(type=54 (OP), string=')')
TokenInfo(type=4 (NEWLINE), string='')
TokenInfo(type=0 (ENDMARKER), string='')
(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)
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