**Software quality control with code linters. **Software quality control has always been an important and integral part of the building and maintaining a software.Even though we have the sense of measuring the quality of a product by our own interpretations, it is not always possible to perfectly ensure the quality without conforming to the standards defined in the industry.
Most of us are aware of the terminologies but do not know the key differences among them. This article is all about defining software quality control, pointing out the difference between quality control of software and software quality assurance and later we will use an example of code linters to signify a particular software quality control technique.
Take for an example, a product you purchased from a shopping mart. What thing goes into your mind before and after you pick it up and pay for it?Obviously, you may think of its quality, check for any flaws in it and only after using it finally, you decide how good or bad it is.
This is from the buyers point of view i.e. the buyer is following a set of steps to make sure he/she is buying a proper product. The analogy we described can be applied to software as well. That was from the buyers context (the consumer).
In the context of software, software quality control is set of procedures that the organisation’s key players use to ensure that their software product will meet the required quality.
According to Wikipedia’s definition software quality control is commonly referred to as Testing.
It is hard to differentiate between the two terms just by looking at them, they seem synonymous.However, one is the subset of the other. If Software Quality Control is a set of procedures to follow while developing a software product and adhering to the software engineering principles then Software Quality Assurance is the means of monitoring the entire software development process. It includes the following phases:
Quality Control at the very basic level includes test cases written by developers /testers to use cases of each module. Quality Analysts use applications to create good specifications and software design.
Almost all companies have product/ project managers who decide whether or not the changes should be made to a feature or not depending on the review result. All these aspects of the software engineering process is encompassed under Software Quality Assurance.
#coding #software-development #quality-software #code-linters #technology
We are a top-rated software quality assurance & testing company leveraging our potential to profound expertise in delivering quality tested applications to businesses.
In the past 16 years, we have delivered over 4200 quality-assured software to a global clientele catering to various industries such as healthcare, adtechs, eLearning, and more.
Planning to outsource software QA services? Or would you like to hire an offshore software testing team?
#software quality assurance testing services #software quality assurance services #quality assurance testing services #quality assurance software testing company #quality assurance software testing
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.
#development #programming #software-development #coding #coding-skills #software-engineering #code-quality #code
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
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