New Year Countdown Timer Effect without Writing Any Single Code - Very Easy
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
New Year Countdown Timer Effect without Writing Any Single Code - Very Easy
In our digital world, software is king. In a world so heavily dependent on software, poor code quality can result in grave consequence, from billions of dollars in lost revenue, to even fatal accidents. Here’s where Embold comes in—a static code analysis product aimed at empowering developers to do their best work.
Embold is a general-purpose static code analyser that has been designed for developers to analyse and improve their code by identifying issues across four dimensions, including design and duplication. We, at Analytics India Magazine, spoke to founder and CEO, Vishal Rai, to understand how Embold can detect anti-patterns in code for seamless integration.
Embold started a decade ago, with the vision of creating a product that can revolutionise the way developers write and design code. According to Vishal Rai, the idea was to develop a tool for software engineers and developers to write code faster and of better quality. And, after a time of extensive research and development, Vishal Rai, along with his partner Sudarshan Bhide launched their product in 2018.
“We have noticed an interesting trend — as teams started becoming bigger, the issues in software started increasing as well and it was very frustrating when you were on programs which weren’t achieving their stated goals because of poor quality,” said Rai. “And that’s where we saw the opportunity of helping companies to write the product as great as Google or Apple and decided to reinvent software analytics.”
Developers always undergo immense pressure of building their products faster at the best quality possible, and such pressure can lead to compromised code quality. This impact of one line of code or one weak link can create significant issues and can massively affect the entire company. And that is why Rai believes that developers need support in being more productive. With Embold, Vishal and Sudharshan brought in a technology that can help developers be more efficient in their work and make the process of software development easy. Explaining the technology, Rai compared it with “autocorrect for code.” He said, “If you look at the legacy tools, they were built for the waterfall model, aka linear-sequential life cycle model, where one release took six months which gave enough time to test the tools. But in the current time, everything is fast, and thus developers require tools that can help them work fast and give them feedback that can be easily implemented in their workflow.” And that’s where Embold’s platform fits into the workflow that helps them find problems and maintain their code. As a matter of fact, Rai acknowledges that there have been many great tools, already in the market, but all of them have been created for great engineers. However, today, not every engineer is necessarily as experienced as others, and in such cases, it is imperative to make tools that are easy to use and help developers analyse their software. “Embold has been built for the future, the technologies that we have ingrained have neural networks, state-of-the-art in-memory databases and analysers that are far more evolved than legacy tools,” said Rai. “Embold has been created to enable people who aren’t as skilled to write better codes. Not only it fills the skills gap but also brings the new age developers closer to the best developers on the planet.”
#featured #bugs and errors in code #embold a autocorrect for code #embold find bugs and errors in code #embold for developers #embold help developers #embold maintains code quality #embold revolutionise writing code #static code analyser
I’m working through Cory House’s Pluralsight course, Clean Coding Principles in C#, and something interesting has caught my attention.
House makes a strong case between writing code and writing prose. As a writer myself, I’ve long seen the parallels between writing and coding. However, House takes the analogy further, and draws many parallels between writing clean code and the writing you’d find in a book or article.
The analogy is a good one because it’s concrete and relatable: all of us read books and all of us write code.
We know a book is well written when reading it is easy. That’s because:
We can apply the same principles found in writing prose as in writing code. House shows us how.
“Imagine reading a book with random, one-letter abbreviations throughout,” House suggests. Here’s an example: “P was very angry with G for insulting her M…He slept on the C.”
Despite the bad names, House acknowledges, you can probably get the main idea of what’s going on. But you have to work at it.
That’s an important point: the reader shouldn’t have to work at it. It’d be much clearer and easier to read something like this: “Pam was very angry with George for insulting her mom…He slept on the couch.” The non-letter abbreviations have been replaced with clear, meaningful names.
The same principle is true when writing code. It’s clearer and easier to read a variable like employee_total instead of seeing a seemingly random letter, e. “Understanding the programmer’s intent is the biggest problem,” House says. “So we must strive to be crystal clear about what we meant to do.”
Key point: Be intentional with your names.
In school, most of us probably learned a golden rule of writing paragraphs: one idea per paragraph. A similar rule applies when writing functions: each function has a single purpose.
Key point: It’s easier to read and understand — both paragraphs and functions — that are clear, focused, and don’t do too much.
You don’t get a list of characters up front when reading a book, House points out. Instead, you’re introduced to a new character “just in time.”
For example, we get introduced to Bob when he enters the story. Otherwise we have to keep track of Bob, Alice, and a slew of other characters unnecessarily. As a reader, that’s burdensome. It makes reading confusing. It’s why authors don’t do this.
The same is true when coding.
If you initialize all of your variables at the top of your file, then the reader has a bunch of things to remember. That’s burdensome. It makes reading confusing. Don’t do this.
“Instead, well-structured functions should….initialize variables just in time,” House says. “When the variable is needed, bring it to life.” Then, get the variable “out of scope.” That way, the reader no longer has to think about it.
Key point: Give your readers what they need — when they need it.
It’s been said that easy reading is hard writing. That’s true for writing both prose and code. Make it easy for your readers: write clear, meaningful code.
#programming #writing #learning #learning-to-code #code #visual studio code
Peer code reviews as a process have increasingly been adopted by engineering teams around the world. And for good reason — code reviews have been proven to improve software quality and save developers’ time in the long run. A lot has been written about how code reviews help engineering teams by leading software engineering practitioners. My favorite is this quote by Karl Wiegers, author of the seminal paper on this topic, Humanizing Peer Reviews:
Peer review – an activity in which people other than the author of a software deliverable examine it for defects and improvement opportunities – is one of the most powerful software quality tools available. Peer review methods include inspections, walkthroughs, peer deskchecks, and other similar activities. After experiencing the benefits of peer reviews for nearly fifteen years, I would never work in a team that did not perform them.
It is worth the time and effort to put together a code review strategy and consistently follow it in the team. In essence, this has a two-pronged benefit: more pair of eyes looking at the code decreases the chances of bugs and bad design patterns entering your codebase, and embracing the process fosters knowledge sharing and positive collaboration culture in the team.
Here are 6 tips to ensure effective peer reviews in your team.
Code reviews require developers to look at someone else’s code, most of which is completely new most of the times. Too many lines of code to review at once requires a huge amount of cognitive effort, and the quality of review diminishes as the size of changes increases. While there’s no golden number of LOCs, it is recommended to create small pull-requests which can be managed easily. If there are a lot of changes going in a release, it is better to chunk it down into a number of small pull-requests.
Code reviews are the most effective when the changes are focused and have logical coherence. When doing refactoring, refrain from making behavioral changes. Similarly, behavioral changes should not include refactoring and style violation fixes. Following this convention prevents unintended changes creeping in unnoticed in the code base.
Automated tests of your preferred flavor — units, integration tests, end-to-end tests, etc. help automatically ensure correctness. Consistently ensuring that changes proposed are covered by some kind of automated frees up time for more qualitative review; allowing for a more insightful and in-depth conversation on deeper issues.
A change can implement a new feature or fix an existing issue. It is recommended that the requester submits only those changes that are complete, and tested for correctness manually. Before creating the pull-request, a quick glance on what changes are being proposed helps ensure that no extraneous files are added in the changeset. This saves tons of time for the reviewers.
Human review time is expensive, and the best use of a developer’s time is reviewing qualitative aspects of code — logic, design patterns, software architecture, and so on. Linting tools can help automatically take care of style and formatting conventions. Continuous Quality tools can help catch potential bugs, anti-patterns and security issues which can be fixed by the developer before they make a change request. Most of these tools integrate well with code hosting platforms as well.
Finally, be cognizant of the fact that people on both sides of the review are but human. Offer positive feedback, and accept criticism humbly. Instead of beating oneself upon the literal meaning of words, it really pays off to look at reviews as people trying to achieve what’s best for the team, albeit in possibly different ways. Being cognizant of this aspect can save a lot of resentment and unmitigated negativity.
#agile #code quality #code review #static analysis #code analysis #code reviews #static analysis tools #code review tips #continuous quality #static analyzer