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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.)
#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer
One of the best things about working in the data science industry is that it’s full of free tools. The data science community is, by and large, quite open and giving, and a lot of the tools that professional data analysts and data scientists use every day are completely free.
If you’re just getting started, though, the sheer number of resources available to you can be overwhelming. So rather than bury you in a list of open-source goodies, we’ve picked out some of our absolute favorites: the best free tools for data science using Python, R, and SQL.
#resources #free #free tools #tools
“AI for fun” — a phrase that we commonly don’t hear in the industry. Artificial intelligence has always been considered a revolutionary technology that has emerged to solve complex real-world problems like high-level computation, omitting manual labour, or data-driven optimisation. However, with its endless possibilities, there are many applications of AI that make this technology more accessible to the average layman person or kids at home.
To get people’s head around this sophisticated technology developers all around the world are continuously developing some fun AI tools that can be easily accessed online to get hands-on. Not only are these AI tools fun but also provide a good understanding of this technology to the users.
Here is a list of 10 exciting artificial intelligence tools that are available online for anyone to have fun with.
#opinions #ai tool online #ai tools #artificial intelligence tools #fun ai tools
DevOps is about Continuous Integration, Continuous Deployment and… continuously looking for the best free tools. Even if you are a super experienced DevOps Engineer you can’t be aware of every new service for your work, which of course appear very often. So, I decided to gather 7 of the best free tools for DevOps in one list and share it with you.
This list is based on my opinion and dozens of opinions from DevOps engineers who told me about these tools. Many of these services are small startups and you won’t find them on the usual DevOps websites or other DevOps tools lists.
All of them have free plans, so you can test features before buying or even use a free plan forever. In any case, let’s try something new! And, please, share in the comments which tools were new for you, which you’ve tried and which you liked — and most importantly, why.
Screenshot from StatusGator
Free plan: Yes. This service’s free plan offers lots of functions that can be enough for small teams or individual specialists.
Paid plan starts from $29.99 per month
Almost every experienced DevOps engineer is tired of manually monitoring every third-party service in use. If you want to stop sitting with red eyes while monitoring every necessary status page, try StatusGator. This tool continuously monitors more than 850 cloud services including Google Cloud, Intercom, Mailgun and others. You can receive notifications via tools that you already use. StatusGator works with Teams, Slack, Flowdock, Discord, SMS, email and webhook. And the greatest part? You don’t need to check anything manually.
If you want to reduce your downtimes with StatusGator, you don’t need any budget because you can sign up for free. The paid plan for StatusGator is very reasonable and starts at only $29.99 per month.
#free-tools #free #devops-tool #devops
Swift is a fast and efficient general-purpose programming language that provides real-time feedback and can be seamlessly incorporated into existing Objective-C code. This is why developers are able to write safer, more reliable code while saving time. It aims to be the best language that can be used for various purposes ranging from systems programming to mobile as well as desktop apps and scaling up to cloud services.
Below here, we list down the 10 best online resources to learn Swift language.
(The list is in no particular order)
#developers corner #free online resources to learn swift language #learn swift #learn swift free #learn swift online free #resources to learn swift #swift language #swift programming