7 Best Practices in GIT for Your Code Quality

There is no doubt that Git plays a significant role in software development. It allows developers to work on the same code base at the same time. Still, developers struggle for code quality. Why? They fail to follow git best practices. In this post, I will explain seven core best practices of Git and a Bonus Section.

1. Atomic Commit

Committing something to Git means that you have changed your code and want to save these changes as a new trusted version.

Version control systems will not limit you in how you commit your code.

  • You can commit 1000 changes in one single commit.
  • Commit all the dll and other dependencies
  • Or you can check in broken code to your repository.

But is it good? Not quite.

Because you are compromising code quality, and it will take more time to review codeSo overall, team productivity will be reduced. The best practice is to make an atomic commit.

When you do an atomic commit, you’re committing only one change. It might be across multiple files, but it’s one single change.

2. Clarity About What You Can (& Can’t) Commit

Many developers make some changes, then commit, then push. And I have seen many repositories with unwanted files like dll, pdf, etc.

You can ask two questions to yourself, before check-in your code into the repository

  1. Are you suppose to check-in all these files?
  2. Are they part of your source code?

You can simply use the .gitignore file to avoid unwanted files in the repository. If you are working on more then one repo, it’s easy to use a global .gitignore file (without adding or pushing). And .gitignore file adds clarity and helps you to keep your code clean. What you can commit, and it will automatically ignore the unwanted files like autogenerated files like .dll and .class, etc.

#git basics #git command #git ignore #git best practices #git tutorial for beginners #git tutorials

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7 Best Practices in GIT for Your Code Quality

7 Best Practices in GIT for Your Code Quality

There is no doubt that Git plays a significant role in software development. It allows developers to work on the same code base at the same time. Still, developers struggle for code quality. Why? They fail to follow git best practices. In this post, I will explain seven core best practices of Git and a Bonus Section.

1. Atomic Commit

Committing something to Git means that you have changed your code and want to save these changes as a new trusted version.

Version control systems will not limit you in how you commit your code.

  • You can commit 1000 changes in one single commit.
  • Commit all the dll and other dependencies
  • Or you can check in broken code to your repository.

But is it good? Not quite.

Because you are compromising code quality, and it will take more time to review codeSo overall, team productivity will be reduced. The best practice is to make an atomic commit.

When you do an atomic commit, you’re committing only one change. It might be across multiple files, but it’s one single change.

2. Clarity About What You Can (& Can’t) Commit

Many developers make some changes, then commit, then push. And I have seen many repositories with unwanted files like dll, pdf, etc.

You can ask two questions to yourself, before check-in your code into the repository

  1. Are you suppose to check-in all these files?
  2. Are they part of your source code?

You can simply use the .gitignore file to avoid unwanted files in the repository. If you are working on more then one repo, it’s easy to use a global .gitignore file (without adding or pushing). And .gitignore file adds clarity and helps you to keep your code clean. What you can commit, and it will automatically ignore the unwanted files like autogenerated files like .dll and .class, etc.

#git basics #git command #git ignore #git best practices #git tutorial for beginners #git tutorials

Madyson  Reilly

Madyson Reilly

1604109000

Best Practices for Using Git

Git has become ubiquitous as the preferred version control system (VCS) used by developers. Using Git adds immense value especially for engineering teams where several developers work together since it becomes critical to have a system of integrating everyone’s code reliably.

But with every powerful tool, especially one that involves collaboration with others, it is better to establish conventions to follow lest we shoot ourselves in the foot.

At DeepSource, we’ve put together some guiding principles for our own team that make working with a VCS like Git easier. Here are 5 simple rules you can follow:

1. Make Clean, Single-Purpose Commits

Oftentimes programmers working on something get sidetracked into doing too many things when working on one particular thing — like when you are trying to fix one particular bug and you spot another one, and you can’t resist the urge to fix that as well. And another one. Soon, it snowballs and you end up with so many changes all going together in one commit.

This is problematic, and it is better to keep commits as small and focused as possible for many reasons, including:

  • It makes it easier for other people in the team to look at your change, making code reviews more efficient.
  • If the commit has to be rolled back completely, it’s far easier to do so.
  • It’s straightforward to track these changes with your ticketing system.

Additionally, it helps you mentally parse changes you’ve made using git log.

#open source #git #git basics #git tools #git best practices #git tutorials #git commit

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

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.”

  • J. Robert Oppenheimer

Outline

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.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

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:

Scanning

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., 7Bob, 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:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

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

Loma  Baumbach

Loma Baumbach

1601157360

Mirroring Git Changes From One Server to Another Server

Introduction

Hello all, nowadays most of the development teams using GIT version control, some of you may have a requirement of mirroring your team’s git changes from one server to another Git server. This article will help you to achieve the Git mirroring between one server to another server.

Business Case

I got one assignment wherein there will be 2 Git Servers, development will happen in one Git server and the changes should be synchronized to another Git server at regular intervals. But in my case, the complexity is both the servers are in different restricted network. So I have done the small experiment and it worked. And I am sharing the steps to you all in this article.

The Experiment Performed Using Below 2 GIT Servers

Main GIT Server: Let’s take our main git server is located in our office and can be accessed only in-office network.

**Mirror GIT Server: **The mirror server is located at the vendor/client-side, which can be accessible in a normal internet connection but not with our office network. Since the office proxy will block the outside URL’s.

#devops #git #git and github #git best practices #git cloning #git server

Tyrique  Littel

Tyrique Littel

1604023200

Effective Code Reviews: A Primer

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.

1. Keep the Changes Small and Focused

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.

2. Ensure Logical Coherence of Changes

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.

3. Have Automated Tests, and Track Coverage

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.

4. Self-Review Changes Before Submitting for Peer Review

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.

5. Automate What Can Be Automated

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.

6. Be Positive, Polite, and Respectful

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