Appium vs Selendroid: Which One to Choose for Mobile Testing Needs?

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March 21, 2023

A Comprehensive Comparison of Appium and Selendroid: Which One Should You Choose for Your Mobile Testing Needs?

As the use of mobile devices continues to grow, so does the need for efficient and effective mobile testing tools. Appium and Selendroid are two popular open-source mobile testing frameworks that have gained popularity in recent years. In this comprehensive comparison, we’ll explore the similarities, differences, advantages, disadvantages, and how to use each tool for mobile testing. By the end of this article, you’ll be better equipped to determine which tool is right for your mobile testing needs.

Introduction to Mobile Testing Tools

Mobile testing tools are designed to help you test your mobile applications in a variety of ways. They help ensure that your applications are functioning as expected and providing the best possible user experience. There are a variety of mobile testing tools available, including both open-source and commercial options. In this article, we’ll focus on two open-source options: Appium and Selendroid.

What is Appium?

Appium is an open-source mobile automation framework that allows you to write tests for mobile applications using a variety of programming languages, including Java, Ruby, Python, and more. It supports both iOS and Android platforms and works with many testing frameworks, including JUnit and TestNG. Appium uses the WebDriver protocol to interact with mobile applications, making it easy to write cross-platform tests.

One of the key benefits of Appium is that it allows you to write tests in the same language that you use to write your application code. This makes it easier to write and maintain tests and reduces the learning curve for new team members. Additionally, Appium supports a wide range of testing scenarios, including functional testing, performance testing, and regression testing.

What is Selendroid?

Selendroid is another open-source mobile automation framework that allows you to write tests for mobile applications. It supports both Android and iOS platforms and works with a variety of programming languages, including Java, C#, and Ruby. Selendroid uses the WebDriver protocol to interact with mobile applications, making it easy to write cross-platform tests.

One of the key benefits of Selendroid is that it allows you to run tests on multiple devices simultaneously, reducing the time required to test your applications. Additionally, Selendroid supports a wide range of testing scenarios, including functional testing, performance testing, and regression testing.

Similarities between Appium and Selendroid

Both Appium and Selendroid are open-source mobile automation frameworks that allow you to write tests for mobile applications. They both support both Android and iOS platforms and use the WebDriver protocol to interact with mobile applications. Additionally, they both support a wide range of testing scenarios, including functional testing, performance testing, and regression testing.

Differences between Appium and Selendroid

While Appium and Selendroid share many similarities, there are also several key differences between the two tools. One of the biggest differences is the programming languages supported by each tool. Appium supports a wider range of programming languages, including Java, Ruby, Python, and more, while Selendroid primarily supports Java.

Another key difference is the level of support for iOS testing. While Appium provides robust support for iOS testing, Selendroid’s iOS support is more limited. Additionally, Appium is generally considered to be easier to set up and use than Selendroid, making it a better option for teams that are new to mobile testing.

Appium vs Selendroid: Which one to choose?

The decision of whether to choose Appium or Selendroid for your mobile testing needs ultimately depends on your specific requirements. If you need to support a wide range of programming languages or require robust iOS testing support, then Appium may be the better option for you. On the other hand, if you primarily use Java and need to run tests on multiple devices simultaneously, then Selendroid may be the better option.

Advantages and Disadvantages of Appium

One of the biggest advantages of Appium is its support for a wide range of programming languages, making it easier to write and maintain tests. Additionally, Appium provides robust support for iOS testing and is generally considered to be easier to set up and use than Selendroid. However, one of the biggest disadvantages of Appium is that it can be slower than other mobile testing tools, which can impact testing efficiency.

Advantages and Disadvantages of Selendroid

One of the biggest advantages of Selendroid is its ability to run tests on multiple devices simultaneously, reducing the time required to test your applications. Additionally, Selendroid is a good option for teams that primarily use Java. However, one of the biggest disadvantages of Selendroid is its limited support for iOS testing, which can be a significant drawback for teams that need to test iOS applications.

How to use Appium and Selendroid for Mobile Testing

To use Appium or Selendroid for mobile testing, you’ll need to follow a few key steps. First, you’ll need to set up your testing environment, including installing the necessary software and configuring your devices. Next, you’ll need to write your tests using the appropriate programming language and testing framework. Finally, you’ll need to run your tests and analyze the results to identify any issues or areas for improvement.

Appium and Selendroid: Comparison Table

	Appium	Selendroid
Platform Support	iOS and Android	Android
Programming Language Support	Compatible with Python, PHP, Ruby, Perl, and any WebDriver compatible language	Tests can be written in any language with client libraries that include law, CA Objective-C, perl, Haskell, PHP, JavaScript Python, and Ruby
Best Use Case	Pun parallel regression testing for large app with continuous feature updates and new functionalities	Cross-platform with reusable code between Windows, Android and iOS test suites
Limitations	No backward compatibility for Android below 4.2. Limited support for hybrid app testing. No support to run the Appium Inspector on Windows.	No compatibility for i0S. SDK is required. High consumption on computer memory Resources.

Appium and Selendroid: Comparison Table

Conclusion

In most cases, Appium is the right choice out of the two. It has a lot more versatility, wide-ranging support and comes with a Selendroid mode. Although Selendroid does a few things better than Appium and has a few similar features, Appium is comparatively a lot more versatile and has a larger community, making it a stronger choice for testing mobile apps.

Code is Not Enough: The Role of Documentation in Software Development

By

Mehdi

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March 10, 2023

0

The Vital Role of Documentation in Software Development | Image generated with Midjourney

As a software developer, I have come to understand the importance of documentation in software development. Documentation is a crucial aspect of software development that cannot be ignored. It is the process of creating, collecting, and maintaining documents that describe the software being developed. In this article, I will be discussing the importance of documentation in software development, the benefits of proper documentation, types of documentation in software development, best practices for documentation in software development, common challenges in documentation and how to overcome them, tools for documentation in software development, examples of good documentation in software development, documentation as part of the software development life cycle, documentation for open-source software projects, and finally, the conclusion and final thoughts.

Introduction to the Importance of Documentation in Software Development

Documentation is an essential aspect of software development that should not be overlooked. It is a process of creating, collecting, and maintaining documents that describe the software being developed. Documentation is critical to the success of software development projects because it helps developers and users understand the software being developed.

Documentation is necessary for software development because it helps developers understand how the software works, how it is designed, and how it should be used. Documentation is also critical for users because it helps them understand how to use the software and how it works. Documentation is an essential part of the software development process because it provides a clear and concise description of the software being developed.

Benefits of Proper Documentation

Proper documentation has many benefits for software development projects. Some of the benefits of proper documentation include:

1. Improved Communication

Proper documentation improves communication between developers and users. It helps developers understand what users need, and it helps users understand what developers are doing.

2. Easier Maintenance

Proper documentation makes it easier to maintain software. It helps developers understand how the software works, which makes it easier to fix bugs and make improvements.

3. Easier Upgrades

Proper documentation makes it easier to upgrade software. It helps developers understand how the software is designed, which makes it easier to add new features and functionality.

4. Improved Quality

Proper documentation improves the quality of software. It helps developers understand how the software works, which makes it easier to identify and fix bugs.

Types of Documentation in Software Development

There are several types of documentation in software development. Some of the most common types of documentation include:

1. Technical Documentation

Technical documentation describes how the software works, how it is designed, and how it should be used. The technical documentation includes things like user manuals, installation guides, and system requirements.

2. Functional Documentation

Functional documentation describes the features and functionality of the software. It includes things like use cases, user stories, and process flows.

3. Design Documentation

Design documentation describes how the software is designed. It includes things like system architecture, database design, and network topology.

4. Test Documentation

Test documentation describes how the software was tested. It includes things like test plans, test cases, and test scripts.

Best Practices for Documentation in Software Development

There are several best practices for documentation in software development. Some of the best practices include:

1. Start Early

Documentation should start early in the software development process. Developers should create documentation as they are developing the software.

2. Keep It Simple

Documentation should be kept simple and easy to understand. Developers should avoid using technical jargon and use plain language instead.

3. Use Templates

Developers should use templates for documentation. Templates help ensure that all necessary information is included in the documentation.

4. Update Documentation Regularly

Documentation should be updated regularly. Developers should update documentation as changes are made to the software.

5. Use Version Control

Documentation should be stored in version control. Version control helps developers keep track of changes to the documentation.

Common Challenges in Documentation and How to Overcome Them

There are several common challenges in documentation, and developers should be aware of these challenges. Some of the common challenges include:

1. Lack of Time

Developers may not have enough time to create documentation. To overcome this challenge, developers should make documentation a priority and set aside time for it.

2. Lack of Resources

Developers may not have the necessary resources to create documentation. To overcome this challenge, developers should use tools and templates to make documentation easier.

3. Lack of Interest

Developers may not be interested in creating documentation. To overcome this challenge, developers should understand the benefits of documentation and how it can help them.

Tools for Documentation in Software Development

There are several tools for documentation in software development. Some of the most common tools include:

1. Microsoft Word

Microsoft Word is a popular tool for creating documentation. It is easy to use and has many features that make it ideal for creating technical documentation.

2. Google Docs

Google Docs is a cloud-based tool for creating documentation. It is easy to use and allows for collaboration with other team members.

3. Confluence

Confluence is a collaboration tool that is ideal for creating documentation. It allows for collaboration and includes features like templates and version control.

4. GitHub

GitHub is a version control system that is ideal for storing documentation. It allows for collaboration and includes features like version control and issue tracking.

Examples of Good Documentation in Software Development

There are several examples of good documentation in software development. Some of the best examples include:

1. Django Documentation

Django documentation is a great example of good documentation. It is well-organized, easy to understand, and includes examples and tutorials.

2. Bootstrap Documentation

Bootstrap documentation is another great example of good documentation. It includes clear and concise instructions on how to use the framework.

3. WordPress Documentation

WordPress documentation is a great example of good documentation. It includes tutorials and examples that make it easy for users to understand how to use the platform.

Documentation as Part of the Software Development Life Cycle

Documentation is an essential part of the software development life cycle. It is necessary for every stage of the software development process. Some of the stages where documentation is required include:

1. Planning

Documentation is required during the planning stage of the software development process. It includes things like project plans, requirements documents, and feasibility studies.

2. Design

Documentation is required during the design stage of the software development process. It includes things like system architecture, database design, and network topology.

3. Development

Documentation is required during the development stage of the software development process. It includes things like user stories, use cases, and process flows.

4. Testing

Documentation is required during the testing stage of the software development process. It includes things like test plans, test cases, and test scripts.

5. Maintenance

Documentation is required during the maintenance stage of the software development process. It includes things like bug reports, change requests, and user manuals.

Documentation for Open Source Software Projects

Documentation is critical for open-source software projects. Open-source software projects rely on documentation to help users understand how to use the software. Some of the best practices for documentation in open-source software projects include:

1. Make Documentation a Priority

Documentation should be a priority for open-source software projects. Developers should understand the importance of documentation and make it a priority.

2. Use Collaboration Tools

Collaboration tools like GitHub and Confluence are ideal for open-source software projects. They allow for collaboration and include features like version control and issue tracking.

3. Use Templates

Templates are ideal for open-source software projects. They help ensure that all necessary information is included in the documentation.

4. Encourage Contributions

Open-source software projects should encourage contributions to the documentation. Users should be encouraged to contribute to the documentation to help improve it.

Conclusion and Final Thoughts

Documentation is an essential aspect of software development that cannot be ignored. Developers must create, collect, and maintain documents that describe the software being developed. Proper documentation has many benefits, including improved communication, easier maintenance, easier upgrades, and improved quality.

There are several types of documentation in software development, including technical documentation, functional documentation, design documentation, and test documentation. Best practices for documentation in software development include starting early, keeping it simple, using templates, updating documentation regularly, and using version control.

Common challenges in the documentation include lack of time, lack of resources, and lack of interest. Developers should use tools like Microsoft Word, Google Docs, Confluence, and GitHub to make documentation easier.

There are several examples of good documentation in software development, including Django documentation, Bootstrap documentation, and WordPress documentation. Documentation is necessary for every stage of the software development life cycle, including planning, design, development, testing, and maintenance.

Documentation is critical for open-source software projects. Developers should make documentation a priority, use collaboration tools, use templates, and encourage contributions to the documentation. In conclusion, documentation is an essential aspect of software development that should not be overlooked.

Data Quality vs Quantity: Why Quality Should Always Come First

By

Mehdi

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February 17, 2023

0

Estimating the quality of data depends on criteria such as exactness, completeness, uniformity, trustworthiness and whether it’s current. Assessing data quality levels can help organizations recognize flaws in the data that must be fixed and assess whether the data in their IT systems is appropriate for its intended use.
As the use of data has become more closely connected with business activities and businesses are increasingly relying on data analytics to make decisions, the focus on data quality in business systems has increased. Data quality management is a fundamental part of the overall data management process, and endeavors to enhance data quality are oftentimes closely related to data governance programs that try to ensure data is formatted and used uniformly across the organization.

Why data quality is important

Poor-quality data can have a bad effect on organizations. This is because Low-grade information is commonly seen as the cause of operational issues, inaccurate analytics, and ill-informed business plans. Economic losses due to data quality issues may include extra costs when goods are sent to the wrong customer, sales opportunities lost because of inaccurate or incomplete customer accounts, and penalties for incorrectly reported financial or regulatory compliance.

Also, corporate CEOs and high ranked managers often state that a lack of trust in data is a primary factor that prevents the use of Business Intelligent and data analytics tools to enhance decision-making within organizations. Consequently, it is important to have an efficient data quality management plan.

What is good data quality?

The precision of data is a significant element of superior data. To bypass technical issues in operational systems and incorrect outcomes in analytics applications, the data that is employed ought to be precise. Faulty data must be detected, listed, and fixed to make sure that corporate executives, data analysts, and other end users are dealing with valid data.

In addition to that, other facets which are fundamental parts of excellent data quality are as follows:

completeness, with data sets containing all of the data elements they should;
consistency, where there are no conflicts between the same data values in different systems or data sets;
uniqueness, indicating a lack of duplicate data records in databases and data warehouses;
timeliness or currency, meaning that data has been updated to keep it current and is available to use when it’s needed;
validity, confirming that data contains the values it should and is structured properly; and
conformity to the standard data formats created by an organization.

Evaluating the data quality

Organizations usually take the initial step of cataloging their data assets and performing a baseline study to measure the correctness, uniqueness, and legitimacy of data sets. The baseline results can then be compared to the data in the systems over time to help identify any new data quality issues.
In addition, companies often design a set of data quality rules based on the business needs for both operational and analytics data. These rules outline the essential quality levels in data sets and explain what information various data elements should include to be validated for accuracy, consistency, and other data quality qualities. After the rules are in place, a data management team typically carries out a data quality assessment to gauge the quality of data sets and document data mistakes and other problems – this process can be consistently repeated to keep the best data quality levels possible.

Data quality vs. data integrity

Data integrity and data quality are sometimes thought of as the same thing or have data integrity looked at as a part of data accuracy or as an additional characteristic of data quality. Generally speaking, data integrity is considered to be a more comprehensive idea that encompasses data quality, data governance and data security measures to deal with accuracy, uniformity and security all at once.

With the more comprehensive view, data integrity has two aspects: logical and physical. Logical integrity involves data quality standards and database elements such as referential integrity, which ensures that connected data components in different database tables are valid. Physical integrity consists of access control and other security protocols meant to stop data from being modified or harmed by unapproved people, as well as back-up and disaster recovery measures.

Advantages of data quality

From a monetary point of view, having a high data quality level allows organizations to bring down the expense of recognizing and rectifying incorrect data in their systems. Companies can also avoid operational errors and business process breakdowns that could upsurge operational costs and drop revenues.
Moreover, good data quality enhances the precision of analytics applications, which can result in better business decision-making that boosts sales, improves internal processes and gives organizations an advantage over competitors. High-grade data can support the usage of BI dashboards and analytics tools too – if analytics data is accepted as dependable, business users are more likely to rely on it instead of depending on gut feeling or their own spreadsheets.
Additionally, effective data quality management also gives data management teams the opportunity to concentrate on more productive tasks instead of cleaning up data sets.

In my next post(s) I shall try to cover `Data quality management tools and techniques`.

Python and Behave: A Perfect Match for BDD

By

Mehdi

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January 30, 2023

0

Behavior Driven Development (BDD) is a method of software development that emphasizes understanding and enhancing the interaction between programmers, stakeholders, and testers. It is an extension of Test Driven Development (TDD) that strives to make the expectations and needs of business stakeholders more understandable, precise, and unambiguous.

Using BDD has the benefit of encouraging better collaboration between multiple divisions, like development, testing, and product management. This helps to make sure that everyone is aware of the purpose of the software and that the final result satisfies the needs of all stakeholders.

This is a simple demonstration of BDD and how it works:

The utilization of BDD offers an extra benefit of making software testing and verification more straightforward. Tests can be written in a plain, understandable language for everybody involved in the development process, which makes it simpler to identify and mend flaws as well as to adjust the software as necessary.

Python and BDD

In the Python environment, there are several frameworks available for BDD testing, one of which is behave. Behave is a popular BDD testing framework that uses the Gherkin language to create tests in a natural, easy-to-read format. It is user-friendly, and supports various features including tags, tables and hooks.

Here’s a sample code snippet of how to use behave in Python:

Feature: Addition
  In order to avoid silly mistakes
  As a math idiot
  I want to be told the sum of two numbers

  Scenario: Add two numbers
    Given I have entered 50 into the calculator
    And I have entered 70 into the calculator
    When I press add
    Then the result should be 120 on the screen

This code specifies a function of a calculator that adds two numbers. It outlines the instructions for testing the feature and the expected result. This code can be implemented through behave, which will execute the tests and give a report on the results.

Getting Started to use Behave

To install the behave package in Python, you can use the pip package manager. Simply run the following command in your terminal or command prompt:

$ pip install behave

After the installation, you can use the following code to run the BDD example that was shown in the previous section:

from behave import *

@given("I have entered {number1:d} into the calculator")
def step_impl(context, number1):
    context.number1 = number1

@given("I have entered {number2:d} into the calculator")
def step_impl(context, number2):
    context.number2 = number2

@when("I press add")
def step_impl(context):
    context.sum = context.number1 + context.number2

@then("the result should be {result:d} on the screen")
def step_impl(context, result):
    assert context.sum == result, f"Expected {result}, but got {context.sum}"

This code defines the implementation of the steps in the BDD scenario. The @given, @when, and @then decorators are used to mark the functions as step implementations. The code uses the context object to store intermediate data, such as the numbers being added, and the sum. The assert statement is used to validate the expected result.

To run the tests, simply run:

$ behave

This will run the tests defined in your feature file, and produce a report on the results.

Recommended directory structure for a Behave project:

The recommended directory structure for a project using the behave framework is as follows:

project_root/
    features/
        feature_file.feature
    steps/
        step_definitions.py
    environment.py (optional)
    behave.ini (optional)

The features directory contains the feature files written in Gherkin syntax. A feature file describes a feature or behavior of the software being tested.
The steps directory contains the step definitions, which are the implementation of the steps described in the feature file. The step definitions are written in Python.
The environment.py file is an optional file that can be used to define setup and teardown actions that are performed before and after each scenario.
The behave.ini file is an optional configuration file that can be used to specify settings for the behave command.

This directory structure provides a clear separation of the feature descriptions and the step definitions, making it easier to maintain and scale the project over time.

Using of Tags in features:

A section of a feature file can be tagged so that the Behave is capable of verifying only a certain section of the feature file. A Scenario, Feature, Scenario Outline can only be tagged.

Also, a tag which is used for a feature shall be inherited by all its Scenarios and the Scenario Outlines. Tags are placed before a Scenario or a Feature that we want to tag. We can also have multiple tags which are separated by spaces within a line.

A tag begins with @ and is followed by the tag name. To run tests with specifying the tags you can run:

$ behave file_name.feature --tags=tag1 --tags=tag2

Do more with BehaveX

BehaveX is an enhancement of the Python Behave platform, offering additional features for pipeline testing. It is possible to begin using BehaveX from the outset with the common Behave principles, or to add it to existing Behave projects. In essence, this wrapper takes the existing Behave capabilities and enhances them to include the following:

Perform parallel test executions (multi-process executions): By feature and By scenario
Get additional test execution reports: HTML and JSON report
Provide additional evidence as part of execution reports
Generate test logs per scenario
- Whatever you log in test steps using the logging library, it will generate an individual log report for each scenario
Mute test scenarios in build servers
- By adding @MUTE tag to test scenarios, they will be executed, but they will not be part of the JUnit reports
Generate metrics in HTML report for the executed test suite
- Automation Rate, Pass Rate and Steps executions & duration
Execute dry runs and see the full list of scenarios into the HTML report
- This is an override of the existing Behave dry run implementation
Re-execute failing test scenarios
- By adding @AUTORETRY tag to test scenarios, so when the first execution fails the scenario is immediately re-executed

After installing BehaveX: pip install behavex you can run the tests with more flags and params:

$ behavex -t @TAG –parallel-processes 4 –parallel-scheme scenario

$ behavex -t @TAG –parallel-processes 3 –parallel-scheme feature

For more info on BehaveX see its Github repository:

https://github.com/hrcorval/behavex

Beautiful Data Visualization with Python and Plotly

By

Mehdi

-

January 20, 2023

0

Data visualization is an important stage for any data scientist or analyst. It allows us to understand data quickly and easily, and also to communicate our findings to others. Data visualization helps us to identify patterns, trends, and correlations in our data, as well as uncover outliers or anomalies.

In this post, I’ll introduce you Plotly, a powerful Python library for data visualization. We’ll see what Plotly is, how to get started with it, and how to create basic and advanced charts. Finally, we’ll look at some examples of Plotly visualizations. So let’s get started!

What is Data Visualization?

Data visualization is the process of transforming raw data into meaningful visuals, such as charts, graphs, and maps. It allows us to quickly and easily identify patterns and trends in our data, as well as uncover outliers or anomalies. It also helps us to communicate our findings to others in a more effective way.

Data visualization is a powerful tool for any data scientist or analyst, as it allows us to quickly and easily identify patterns and trends in our data. Data visualization can be used in many different fields, such as business, economics, and social sciences, and can be used to answer questions or make decisions.

What is Plotly?

Plotly is an open-source data visualization library for Python and R written in JavaScript, making graphs inherently interactive. They can be modified tremendously and are easily exported as images, HTML, etc. for seamless integration into a number of applications.

Plotly is highly customizable, allowing users to customize their charts with various colors, styles, and layouts. It also offers advanced features such as animation, annotations, and interactive elements.

Getting Started with Plotly

Getting started with Plotly is easy. All you need to do is install the library, which can be done using the pip command:

pip install plotly

Once you have installed the library, you can import it into your project:

import plotly

Note: To use some of Plotly plotting features you need to install `pandas` as well:

pip install pandas

Plotly Basics

Plotly is a powerful library for creating visualizations with Python. It has a wide range of features, including histograms, line charts, scatter plots, and bubble charts. It is also highly customizable, allowing users to customize their charts with various colors, styles, and layouts.

When creating a visualization with Plotly, you can start by creating a figure object. This object will contain the data that you want to visualize. You can then add various elements to the figure, such as lines, bars, and markers. Finally, you can customize your visualization with colors, styles, and annotations.

Creating Basic Charts with Plotly

Sample Bar Chart:

import plotly.express as px
import pandas as pd
import random

# Create sample data
df = pd.DataFrame({
    'x': [i for i in range(1, 11)],
    'y': [random.randint(1, 20) for _ in range(10)]
})

# Create the bar chart
fig = px.bar(df, x='x', y='y', title="Bar Chart for Random values")

# Show the plot
fig.show()

Run your code you should have a plot like this:

You may also use `Graph Objects’ to create a plot:

import plotly
fig = plotly.graph_objs.Figure()
line = plotly.graph_objs.Line(x=[i for i in range(10)],
                              y=[random.randint(1, 20) for _ in range(10)])
fig.add_trace(line)

# Show the plot
fig.show()

You can also plot a histogram with Plotly:

import plotly.express as px

df = px.data.iris()# plotting the histogram
fig = px.histogram(df, x="sepal_length", y="petal_width")

# showing the plot
fig.show()

Run your code and you will have this:

Even Bubble Plots:

import plotly.express as px

df = px.data.iris()

# plotting the bubble chart
fig = px.scatter(df, x="species", y="petal_width",
    size="petal_length", color="species")

# showing the plot
fig.show()

And ofcourse Pie charts:

import plotly.express as px

df = px.data.gapminder().query("year == 2007").query("continent == 'Europe'")

df.loc[df['pop'] < 2.e6, 'country'] = 'Other countries' # Represent only large countries

fig = px.pie(df, values='pop', names='country', title='Population of European continent')

fig.show()

Customizing Your Charts

Plotly is a highly customizable tool and even after you have created your figure object, you can customize it with colors, styles, and annotations. For example, if you want to color the line red and add a title to the chart, you can do so by adding the following code to your figure object:

fig.update_layout( title='My Chart', line_color='red' )

You can also add a legend to the chart, which will allow you to easily identify the different elements of the chart.

Plotly also offers advanced features, such as animation and interactivity. You can animate your charts by adding a transition between different points in your data. You can also add interactivity by allowing your users to hover over the chart to see more information.

Advanced Plotly Features

Plotly offers a wide range of advanced features, such as animation and interactivity. You can animate your charts by adding a transition between different points in your data. You can also add interactivity by allowing your users to hover over the chart to see more information.

Plotly also offers features such as annotations and text boxes. Annotations allow you to add text or images to your chart, while text boxes allow you to add explanatory text or notes. You can also add shapes to your chart, such as arrows or circles.

Conclusion

Plotly is a powerful library for creating visualizations with Python. It has a wide range of features, including histograms, line charts, scatter plots, and bubble charts. It is also highly customizable, allowing users to customize their charts with various colors, styles, and layouts.

If you’re looking for a powerful library for data visualization, then Plotly is a great choice. It is easy to use and provides a wide range of features, making it a great tool for any data scientist or analyst.

Resources:

https://plotly.com/python

https://github.com/plotly

Poetry: A Poetic Way To Manage Python Dependencies

By

Mehdi

-

January 17, 2023

0

Python Poetry is an open-source tool that assists developers with constructing and managing a Python application or library. It offers a command line interface for quickly installing and managing packages, and additionally takes charge of the dependencies and packages of a project. Moreover, Poetry integrates with virtual environments, granting developers the ability to manage both the environment and packages with one program. This guarantees that each project will have its own distinct set of dependencies and packages, while diminishing the possibilities of compatibility or conflict issues.

Install Poetry using pip by simply running:

$ pip install --user poetry

or

$ curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python

This will install the latest version of poetry on your local machine.

To create a new project using poetry run:

$ poetry new project-name

This will create a directory with the given name with following files and sub-directory in bellow structure:

project-name/
├── README.rst
├── project_name
│   └── __init__.py
├── pyproject.toml
└── tests
    ├── __init__.py
    └── test_project_name.py

If you already have your project and want to initial poetry do:

$ cd existing-project
$ poetry init

This will create just the `pyproject.toml`. pyproject.toml is a configuration file used by Poetry, a package manager for Python. This file contains information about the project and its dependencies, as well as other configuration settings for Poetry. The pyproject.toml file is created in the root directory of a project when using Poetry, and it stores information such as the project name, version, description, author, and license. It also lists the dependencies of the project, including their versions and any additional information needed to manage them.

With pyproject.toml, developers can specify all the information needed for a project in a single file, making it easier to manage and share the project with others. The file can be used by Poetry to create a virtual environment, install dependencies, and build and distribute packages.

This is a sample pyproject.toml file content:

[tool.poetry]
name = "project-name"
version = "0.1.0"
description = ""
authors = ["Your Name <you@example.com>"]
readme = "README.md"
packages = [{include = "project_name"}]

[tool.poetry.dependencies]
python = "^3.10"
requests = "^2.28.2"

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

[tool.poetry]: This is a set of basic project metadata like name, version description, and author. This information is necessary if you would like to publish your project as a package on PyPI.
[tool.poetry.dependencies]: This is a list of project dependencies. On fresh projects, it lists only the Python version but can also include all package versions that normally would be described in the requirements.txt file.
[tool.poetry.dev-dependencies]: This is a list of dependencies that require local development, like testing frameworks or productivity tools. It is common practice to have a separate list of such dependencies as they are usually not required in production environments.
[build-system]: Describes Poetry as a build system used to manage the project.

To add new package for your project run:

$ poetry add <pakcage-name>

This will download, install this package and add its name as an entry in [tool.poetry.dependencies]

To freeze the Python dependencies when you are satisfy about your working project do:

$ poetry lock

This will create the file poetry.lock and store all required dependencies and their transitive dependencies we well.

Install update dependencies

Once there is a pyproject.toml file you can install/update all the specified dependencies.

$ poetry install
$ poetry update

# for production
$ poetry install --no-dev

Create and publish packages

You can use poetry to build python packages with:

$ poetry build

This will create both a whl and .tar.gz files. And then you can also upload it to PyPI with:

$ poetry publish

Conclusion

Poetry makes it easier for developers to deploy and publish their applications. It is fast at creating .lock files compared to pipenv. Writing setup.py/setup.cfg for the Python modules is also not required anymore.

poetry is a tool that every Python developer need. To know more about Poetry see its official documentation and source code:

Quality Assurance vs Quality Control, This Is The Question!

By

Mehdi

-

January 10, 2023

0

Software Quality Assurance (QA) and Software Quality Control (QC) are related but distinct practices in the software development process.

Quality Assurance (QA) is an anticipatory procedure that is aimed at averting mistakes and ensuring that the software is up to standard and matches the established criteria. This requires the formation and utilization of a set of methods and guidelines to make certain that the software is of a superior quality. This might comprise of procedures such as assessment, code review and process evaluations. The objective of QA is to discover and prevent errors before they enter the final product, and guarantee that the software is able to meet the expectations of the end-users.

Quality Control (QC) is an interactive process that concentrates on uncovering and fixing faults in the code. This involves assessing the program to guarantee that it adheres to the regulations and instructions. This may contain activities like hands-on testing, computerized testing, and accord testing. The purpose of QC is to detect mistakes in the software and make sure they are remedied before the product is propelled.

The difference between Software Quality Assurance and Software Quality Control is that QA focuses on preventing defects, while QC focuses on detecting and correcting defects. Quality assurance (QA) ensures that the software meets specified requirements, while quality control (QC) ensures that it is of high quality. QA and QC are both essential in the software development process because they work together to ensure that the quality of the product is up to par and meets user needs.

Brief Comparison between Quality Assurance and Quality Control

Quality Assurance (QA)	Quality Control (QC)
It is a procedure that focuses on providing assurance that quality requested will be achieved	It is a procedure that focuses on fulfilling the quality requested.
QA aims to prevent the defect	QC aims to identify and fix defects
It is a method to manage the quality- Verification	It is a method to verify the quality-Validation
It does not involve executing the program	It always involves executing a program
It’s a Preventive technique	It’s a Corrective technique
It’s a Proactive measure	It’s a Reactive measure
It is the procedure to create the deliverables	It is the procedure to verify that deliverables
QA involves in full software development life cycle	QC involves in full software testing life cycle
In order to meet the customer requirements, QA defines standards and methodologies	QC confirms that the standards are followed while working on the product
It is performed before Quality Control	It is performed only after QA activity is done
It is a Low-Level Activity, it can identify an error and mistakes which QC cannot	It is a High-Level Activity, it can identify an error that QA cannot
Its main motive is to prevent defects in the system. It is a less time-consuming activity	Its main motive is to identify defects or bugs in the system. It is a more time-consuming activity
QA ensures that everything is executed in the right way, and that is why it falls under verification activity	QC ensures that whatever we have done is as per the requirement, and that is why it falls under validation activity
It requires the involvement of the whole team	It requires the involvement of the Testing team
The statistical technique applied on QA is known as SPC or Statistical Process Control (SPC)	The statistical technique applied to QC is known as SQC or Statistical Quality Control

Measurement and Metrics in Software Development

By

Mehdi

-

December 21, 2022

0

Software measurements and indicators are used to gauge the quality, performance, and growth of software projects. Metrics may measure code complexity, testing coverage, and project velocity, among others. These metrics are used to identify areas for improvement, monitor progress over time, and make more informed decisions regarding how to manage and prioritize work.

Product Metrics vs Process Metrics

Software measurement and metrics can be divided into two main categories: product metrics and process metrics. Product metrics focus on the characteristics of the software product itself, such as code complexity, maintainability, and performance. These metrics help to evaluate the quality of the software and identify areas that need improvement. Examples of product metrics include:

Lines of code (LOC): A measure of the size of the software
Cyclomatic Complexity: A measure of the complexity of the code
Code coverage: A measure of how much of the code has been tested
Defect density: A measure of the number of defects per thousand lines of code

Process metrics, on the other hand, focus on the software development process itself, such as project velocity, productivity, and efficiency. These metrics help to evaluate the performance of the development team and identify areas where the process can be improved. Examples of process metrics include:

Lead time: The time it takes to go from code committed to code deployed
Mean Time To Recovery (MTTR): The average time taken to fix a failure
Velocity: The rate at which the team delivers working software
Defect escape rate: The percentage of defects that are not caught during the development process and are found later, in production or by the customer.

Using software measurement and metrics is an important part of managing software development projects, as they provide a way to objectively assess the progress and quality of the work, and to identify areas that need improvement. They can also be used to make data-driven decisions about how to manage and prioritize work, and to track progress over time.

How to apply software development metrics?

There are several ways to apply software development metrics in practice:

Establish a set of metrics: Before you can start tracking metrics, you need to decide which metrics you want to measure. This will depend on the goals of your project and the specific characteristics of your development process.
Collect data: Once you have a set of metrics, you need to collect data in order to measure them. This can be done using tools such as code analysis tools, testing tools, and project management software.
Analyze the data: After collecting the data, you need to analyze it in order to extract meaningful insights. This can include identifying trends, comparing performance over time, and identifying areas for improvement.
Communicate the results: It’s important to communicate the results of your metric analysis to relevant stakeholders. This can include the development team, project managers, and senior management.
Take action: After identifying areas for improvement, you should take action to address them. This can include making changes to the development process, investing in new tools or training, or re-prioritizing work.
Continuously measure: Software development metrics should be continuously measured, analyzed, and reported. This will help you to track progress over time and make more informed decisions about how to manage and prioritize work.

It’s also worth mentioning that not all metrics are equaly useful, and it’s important to know that a few key metrics that align with the goals of the project, can be more informative. It’s also important to note that mentioned metrics should not be used as the only driver for decision making, but more as a tool for informed decision making.

CMMI: A Model For Optimizing Development Processes

By

Mehdi

-

October 10, 2022

0

The Capability Maturity Model Integration (CMMI) is a model that provides a framework for improving the processes and practices used in software development and maintenance. It is a collection of best practices that can be used to assess, improve and benchmark the performance of an organization’s processes. The CMMI is used to evaluate an organization’s ability to deliver software products and services and is divided into five maturity levels that range from initial to optimized. Organizations can use the CMMI model to evaluate the maturity of their processes and identify areas for improvement. This model helps organizations to achieve better quality, better delivery times, and better customer satisfaction. It also increases the chances of delivering projects on time, on budget, and with high quality.

History of CMMI

The history of the Capability Maturity Model Integration (CMMI) goes back to the early 1990s, when the Software Engineering Institute (SEI) at Carnegie Mellon University developed the Capability Maturity Model (CMM) to help organizations improve their software development processes. The CMM was initially developed for the United States Department of Defense (DoD) to evaluate the maturity of software development processes used by contractors. It was later expanded to include other industries and organizations.

In 2000, the SEI released the CMMI, which was an integration of the CMM and other models that had been developed by the SEI and other organizations. The CMMI was designed to provide a more comprehensive framework for process improvement and to be more applicable to a wider range of industries, including software, engineering, and services.

The CMMI model has been updated and revised several times since its initial release, with the latest version being CMMI V2.0, which was released in 2018. Today, the CMMI is widely used by organizations around the world to improve the quality and effectiveness of their software development processes.

CMMI Maturity Levels

The CMMI model includes five maturity levels:

Maturity Level 0 – Incomplete: At this stage work “may or may not get completed.” Goals have not been established at this point and processes are only partly formed or do not meet the organizational needs.
Maturity Level 1 – Initial: Processes are viewed as unpredictable and reactive. At this stage, “work gets completed but it’s often delayed and over budget.” This is the worst stage a business can find itself in — an unpredictable environment that increases risk and inefficiency.
Maturity Level 2 – Managed: There’s a level of project management achieved. Projects are “planned, performed, measured and controlled” at this level, but there are still a lot of issues to address.
Maturity Level 3 – Defined: At this stage, organizations are more proactive than reactive. There’s a set of “organization-wide standards” to “provide guidance across projects, programs and portfolios.” Businesses understand their shortcomings, how to address them and what the goal is for improvement.
Maturity Level 4 – Quantitatively managed: This stage is more measured and controlled. The organization is working off quantitative data to determine predictable processes that align with stakeholder needs. The business is ahead of risks, with more data-driven insight into process deficiencies.
Maturity Level 5 – Optimizing: Here, an organization’s processes are stable and flexible. At this final stage, an organization will be in constant state of improving and responding to changes or other opportunities. The organization is stable, which allows for more “agility and innovation,” in a predictable environment.

It’s worth mentioning that the CMMI model is not a one-time assessment, organizations are expected to go through continuous assessment and improvement to reach the highest level.

CMMI Capability Levels

The CMMI also has capability levels that are used to appraise an organization’s performance and process improvement as it applies to an individual practice area outlined in the CMMI model. It can help bring structure to process and performance improvement and each level builds on the last, similar to the maturity levels for appraising an organization.

The capability levels are:

Capability Level 0 – Incomplete: Inconsistent performance and an “incomplete approach to meeting the intent of the practice area.”
Capability Level 1 – Initial: The phase where organizations start to address performance issues in a specific practice area, but there is not a complete set of practices in place.
Capability Level 2 – Managed: Progress is starting to show and there is a full set of practices in place that specifically address improvement in the practice area.
Capability Level 3 – Defined: There’s a focus on achieving project and organizational performance objectives and there are clear organizational standards in place for addressing projects in that practice area.

CMMI Certifications

There are several CMMI certifications available for organizations and individuals:

CMMI Institute Partner: This certification is awarded to organizations that provide CMMI consulting and training services.
CMMI Lead Appraiser: This certification is awarded to individuals who have been trained and certified to conduct CMMI appraisals.
CMMI Instructor: This certification is awarded to individuals who have been trained and certified to teach CMMI courses.
CMMI High Maturity Lead Appraiser: This certification is awarded to individuals who have been trained and certified to conduct CMMI High Maturity appraisals.
CMMI SCAMPI Lead Appraiser: This certification is awarded to individuals who have been trained and certified to conduct CMMI SCAMPI appraisals.
CMMI Dev Lead Appraiser: This certification is awarded to individuals who have been trained and certified to conduct CMMI Dev appraisals.
CMMI for Services Lead Appraiser: This certification is awarded to individuals who have been trained and certified to conduct CMMI for Services appraisals.

These certifications are offered by the CMMI Institute, which is the organization responsible for developing and maintaining the CMMI model.

Some Useful Books About CMMI

“CMMI: Guidelines for Process Integration and Product Improvement” by Mary Beth Chrissis, Mike Konrad and Sandy Shrum
“CMMI for Development: Guidelines for Process Integration and Product Improvement” by Mary Beth Chrissis, Mike Konrad and Sandy Shrum
“CMMI for Services: Guidelines for Superior Service” by Mary Beth Chrissis, Mike Konrad and Sandy Shrum
“Achieving CMMI-DEV Level 3: A Practical Approach” by Pankaj Jalote
“CMMI Survival Guide: Just Enough Process Improvement” by Richard Turner
“Implementing CMMI: A Practical Guide to Process Integration” by Margaret Kulpa and Kent Johnson
“The CMMI (R) Appraisal Framework: Motivating Positive Change” by David N. Card, Mark C. Paulk, Charles V. Weber
“CMMI for Development: Guidelines for Process Integration and Product Improvement” by Mary Beth Chrissis, Mike Konrad, and Sandy Shrum.

Simplicity is not simple: How to Improve Your API Testing Workflow with Postman

By

Mehdi

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January 28, 2022

0

API testing is an indispensable aspect in the programming process, as it aids in the verification of your program’s backend functions properly and that the API endpoints are giving the anticipated results. Among the most preferred tools for API testing is Postman, a feature-rich platform that makes it straightforward to evaluate and debug APIs. In this blog post, we will discuss some of the reasons why you should apply Postman for API testing and how it can help refine your development procedure.

To begin with, let us mention the interface. Postman has a straightforward interface that makes it uncomplicated to generate, examine, and arrange API requests. This is especially helpful when dealing with complicated APIs that have various endpoints and parameters. The possibility of saving and organizing requests in collections makes it convenient to monitor distinct API calls and test them in different settings.

Postman has an excellent capability which enables it to handle various groupings of requests. This feature allows the user to make distinct sets for the API requests, such as development, testing, and implementation. This makes it easier to assess the API in various stages and ensure that it functions properly in each environment. In addition, Postman has inbuilt support for common types of requests like GET, POST, PUT, and DELETE, making it effortless to evaluate various API calls and observe the outcomes instantly. Furthermore, one can quickly add customized headers and body data to their requests, which is beneficial when dealing with APIs that necessitate authentication or require specific data to be sent.

Postman offers a variety of debugging options, such as being able to check JSON and XML answers, and an integrated JavaScript test administrator. This facilitates the recognition and correction of mistakes in your API calls, as well as verifying that your API is producing the anticipated output.

Moreover, Postman offers collaboration abilities, making it simple to share collections of requests with team members which is particularly advantageous when working on large projects with several developers.

Additionally, Postman makes it easy to write automated tests for your APIs with the help of its scripting feature, which makes it possible to do automatic tests to ensure that the API is running correctly. Furthermore, Postman has various integrations with other tools, for example Continuous Integration systems such as Jenkins, Github, and a lot more. This makes it possible to incorporate Postman into your existing workflow and use it with other tools.

Postman is a powerful and feature-rich API testing tool that can help improve your development workflow. Its user-friendly interface, support for multiple environments, built-in support for common request types, and debugging capabilities make it easy to test and debug APIs. Additionally, its collaboration features and ability to write automated tests make it a valuable tool for any development team. If you’re not already using Postman for API testing, we highly recommend giving it a try.