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With the rise of globalization and the worldwide lockdown due to the pandemic, most of the work has been done by remote working processes and professionals from their homes. This lockdown has proved the efficiency of remote development and enhanced the trust in offshore software development industry.
To make the most out of the benefits of offshore software development, you should understand the crucial factors that affect offshore development. This is why you should read this guide for the best practices when hiring an offshore software development company. Despite the size and the industry of the business, offshore software development is not beneficial for every entrepreneur in many aspects to make the optimum use of talents in technology across the globe.
Here are some of the top reasons why offshore development is beneficial for your business.
To avail of all these benefits, you should have clear goals, a list of requirements, and features that are mandatory for your software product.
Here are a few tips to help you find the best offshore software development company. Build a top-notch software application by following the listed best practices.
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There is no better moment for me than starting a brand new project.
Smells like new project spirit… (Whatever it means)
Starting a new project is funny. Everything seems to be in the right place. But as the projects grow and the deadlines come closer the things begin to boiling.
So, let’s talk about signals that can tell us if our code sucks and we how we can avoid that.
I guess we all have known at least one project that anyone wants to touch, or heard the phrase:
It works, don’t touch it!
Well, that’s not a good signal. I know there are complex projects, big projects, but if nobody in your team can touch it without breaking something, then there is something wrong with that code.
Code is like a garden, it needs to be treat and maintained, if it grows in size or complexity with no control, then will be harder to maintain and easily can get death.
Code grows out of control when there are no conventions to work in it, team practices, even solo practices are important to keep our code under control.
If you see yourself in a scenario where is hard to add things to your project, then you should rethink the whole thing.
If only one person in your team can understand a project, then that’s a problem and hopefully that person never gets sick or goes on vacation.
If you are working by yourself please don’t write overcomplicated code; in my experience simplicity is better; writing code that anyone can read is the right thing to do.
t is clear today may not be that clear in a couple of weeks, even for you.
Use comments on your code. Do not comment on every single line but put enough comments on the complicated and crucial parts.
I have to insist on this. Simple is better; there is no need to show anyone how abstract you can be or how much you know the language. Keeping things simple is way much more productive than trying to show off your knowledge and skill.
Keep your code as readable as possible, simple as possible. Clear variable names, descriptive functions names, clear statements. This will save time for you and your team.
A good way to measure how readable your code is is to overcome the necessity of comments. If the code does not need many comments to describe it, then it means the code is readable enough.
The best code is not only the one that is fast and performant; the best code is also the one you enjoy working on. I’ve had nightmares of codebases that I had to work with, and I also have had codebases that I enjoy.
Coding is a team sport, and every member of the team must be able to play the game, so write for the team.
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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.)
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