Moving from Selenium to Protractor for Test Automation

Protractor is an end-to-end Testing Framework for testing Angular as well as AngularJS applications. It helps you runs tests against your application running in a real browser, interacting in exactly the same way a user would. The first version of Protractor was released in the month of July 2013, when the framework was just a prototype of a particular testing framework. Google, however, with the support of the testing community, is evolving the framework to follow the evolution of AngularJS and to meet the needs of the community that is using AngularJS.

Why use Protractor over Selenium?

Test Your Application like a User

Protractor framework is built on top of WebDriverJS, which uses native events and browser-specific drivers to interact with your application exactly like a user would. It is based on Behaviour Driven approach which allows even a non automation tester to test the application without expertise in automation tool. Example –

describe(‘angularjs homepage’, function() {
 it(‘should greet the named user’, function() {
   // Load the AngularJS homepage.
   browser.get(‘http://www.angularjs.org’);    
   element(by.model(‘yourName’)).sendKeys(‘Julie’);

   var greeting = element(by.binding(‘yourName’));

   // Used to assert that the text element has the required expected value.
   // Protractor patches ‘expect’ to understand promises.

   expect(greeting.getText()).toEqual(‘Hello Julie!’);
 });
});

Advantages over Selenium

JavaScript automation frameworks involve working on asynchronous execution, callbacks, anonymous functions and promise, which is a sequential activity just like finding an object and perform operations on it. Another advantage of transitioning to Protractor/JavaScript is that both the application and the test codebase would be written in the same language.

For Angular Apps

Protractor provides support for Angular-specific locator bindings, which allows you to test Angular-specific web elements without any need for additional setup effort. It has extra locators compared to selenium webdriver. Examples include model, repeater, binding etc.

Angular JS applications have some extra HTML attributes like ng-repeater, ng-controller, ng-model which are not included in Selenium locators. Selenium is not able to identify those web elements using today used Selenium code. Protractor on top of Selenium can handle and control these operations in Web Applications.

Example –

element(by.model(‘locator’)).sendKeys(‘text’);
element(by.binding(‘locator’)).click();

Automatic Waiting

When it comes to waiting for elements on a web page, there is no need to add waits and sleeps to your test. Protractor automatically executes the next step in your test the moment a webpage finishes all pending tasks. There is no need to worry about waiting for your test and webpage to sync in. Protractor, moreover, also speeds up your testing as it avoids the requirement for a lot of “sleeps” and “waits” in your tests, which in turn optimizes sleep and wait times.

Supports Extensibility

Since protractor is a node.js application, can utilize the wide variety of packages that are available in the node. One can extend the framework or add additional features by installing node packages. For example, if you need HTML report you can just use Jasmine HTML Reporter for the clean code you can install eslint or tslint. Likewise, you can install node packages of your choice.

Supports Control Flow

Application Programming Interface (API) is based on promises, which are managed by control flows and adapted for Jasmine. Protractor APIs are purely asynchronous. It maintains a queue of pending promises, called the control flow, to keep execution organized.

Jasmine System Architecture
Jasmine System Architecture

Asynchronous Behavior

Works on NodeJS, so that the asynchronous process helps to speeding up the execution.
Here is how you it is achieved.
 
1) Promise Manager/ Control Flow

It is an abstraction that makes every action to be called one by one, like a queue. Every action returns a special object – Promise. These represent the result of async operation.

2) Second way – async/await

It is new abstraction around promises objects and allows easily chaining actions one by one. The advantage in this is native language construction, instead of Promise Manager, which makes your code look like synchronized, with try/catch and other familiar constructions.

describe(‘angularjs homepage’, function() {
 it(‘should greet the named user’, async function() {
   await browser.get(‘http://www.angularjs.org’);
   await element(by.model(‘yourName’)).sendKeys(‘Julie’);
   var greeting = element(by.binding(‘yourName’));
   expect(await greeting.getText()).toEqual(‘Hello Julie!’);
 });

“await” is like “suspend code execution until a promise returned from the action is resolved”.

Images and Screenshots

Image comparison is very easy in protractor and it works great. Protractor helps you take screenshots on demand and create them in any place needed. You just need to specify the type of Reporter that you want to use.

Example –

jasmine.getEnv().addReporter(new HtmlReporter(){
this.specDone = function(result){
if(result.failedExpectations.length >  0){
//take Screenshot
}
}
}

Conclusion/ Summary

There is a big world of Protractor out there and there are hundreds of packages available in the market offered by NPM to add more features to your test in addition to simple test scenarios.

Which are the Frameworks for Automation Testing?

Test automation framework utilizes software for executing tests and after that find out final the end results and the projected results are the same or not. Each and every company needs software testing satisfactorily and fast too. To achieve this, organizations are changing to utilize automated testing strategies and methods. In short, the best framework or Automation Testing is a valuable mix of a few guidelines, coding ideas, coding standards, methodology, practices, hierarchies, modularity, test data injections, reporting mechanism, and so on to build automation testing. In this manner, the client can follow such core principles while automating application to take the advantages of beneficial results.

Types of Test Automation Frameworks

The best framework for automation testing is as follows:

Carina

Carina is chiefly a Java-based test automation framework built on top of the well-admired open-source solutions (TestNG, Selenium, and Appium) which allows reducing dependence on a specific technology stack. Unites every single testing layers: mobile applications (hybrid, native, web), WEB applications, databases, REST services; Assists each common and the famous browsers (Chrome, Firefox, IE, Safari) and mobile devices (Android/ iOS) – it reuses test automation code between Android/ IOS up to 75-85%; As far as this framework is constructed in Java, it is cross-platform. Tests may be simply executed both on UNIX or Windows OS.

Selenium

Selenium is an incredibly admired open-source automation testing tool. There are two important parts to Selenium. One is Selenium WebDriver, which is the base framework that assists you to deal things like click buttons, set text in fields, and check values on the screen. Another part is known to be as Selenium IDE, a plug-in for FireFox that you can utilize to record the actions you take and the export them to the language (any) to run later.

Serenity

If you are searching for a Java-based framework that integrates with Behavior-driven development (BDD) tools like Cucumber and JBehave (keep your test scenarios at a high level) while accommodating low-level execution facts in your reports, Serenity (also called as Thucydides) might will be the best tool. This tool is perfectly designed to make writing automated acceptance & regression tests easy. It acts as a wrapper on top of BDD and Selenium WebDriver tools.

Cucumber

It is a Behavior Driven Development (BDD) tool which is used for writing acceptance tests for the web applications. The key qualities are as follows:

  • Fast and easy set up and execution;
  • Allows reusing code in the tests;
  • Cross-platform;
  • Previously implemented in Ruby, extended to Java framework;
  • Both specifications, & test documentation, is uploaded in a sole up-to-date document;
  • Useful for the users not familiar with testing. In short, those who can’t read the code;

Cypress

Compared to other tools on this list, Cypress is a more developer-centric framework that significantly focuses to make TDD a reality for developers. It has a separate architecture than Selenium. The fact is while Selenium WebDriver runs slightly outside the browser, Cypress runs inside of it. It also makes it easy for dropping a debugger into your application, which in turn, makes it easier to use the developer tools while you are developing.

Watir

Web Application Testing in Ruby is the oldest framework which is perfectly designed to support users to automate testing a web browser. Just like Selenium, it is a group of tools. The different library in the WATIR suite offers exceptional functions. Whilst WATIR will only support IE running on the Windows Operating System, you can access many others using an exceptional execution of WebDriver called Watir-WebDriver

Appium

Appium is perfectly designed to test mobile applications. It is built with the plan that you shouldn’t be recompiling your application or modifies it in any way to test it.

Apache JMeter

Apache JMeter is flawlessly designed for load testing and can be used to test performance both on static and dynamic resources, Web dynamic apps. This specific tool can simulate a heavy load on a server, network, or object to test its strength or to scrutinize and calculate overall performance under diverse load types.

Robotium

Robotium is a test framework made to make the task simpler to write powerful and solid automatic black-box UI tests, particularly for Android. With the help of Robotium, developers are able to write system, function, & user acceptance test scenarios covering several Android actions.

These are the top test automation frameworks for 2019. It is always better to automate the testing process to save extra money, effort, time, and lessen the number of testing errors.

Trends of ERP Testing to Watch for 2019

ERP (Enterprise Resource Planning) systems no longer necessitate any sort of introduction. For businesses, investing in a good system no longer is an alternative. It is a necessity. Enterprise Resource Planning systems have been a part of the business software landscape for a long time. Ever since their foray into the world of business, vendors are incessantly evolving them, so they are more powerful, robust, simpler to use, and affordable.

The 5 Major ERP trends that we should consider in 2019:

1.Competition from Disruptors- The Enterprise resource planning behemoths that have conquered the industry are encountering stiff competition from new, often Software-as-a-Service (SaaS)-only startups & the proliferation of fresh trends threatening to upset how enterprises collect and process data, and also operate. Renowned companies like FinancialForce (already having more than 1,300 Enterprise resource planning customers) and Kenandy are creating solutions based on the Salesforce App Cloud to make them alluring to users of the popular CRM and sales automation tool. On the disruption side, data visualization, big data, and artificial intelligence (AI) top the list of newest technologies that threaten to alter the way Enterprise resource planning systems are built and used. Enterprises looking to update their Enterprise resource planning systems in the year 2019 will need to become aware of to how their new prospects handle such trends. Database performance will be the core performance indicator (KPI) for Enterprise resource planning in 2019.

2.Enterprise resource planning, SaaS, & Hybrid ERP- Enterprise resource planning apps are stored on your servers, which mean you are responsible for long-term hardware maintenance, hardware costs and data backup and recovery. SaaS-based applications are stored on cloud-based servers, which are much less costly, very fast to upgrade and scale, and don’t take up clunky servers. Hybrid ERP systems are becoming famous in some sections as long-time Enterprise resource planning customers enjoy the ability to move certain Enterprise resource planning functions to the cloud while sustaining tight, on-premises control over other facets, particularly those most vulnerable to compliance regulation.

3.Focused on Social Media and Digital Marketing- These days, Enterprise resource planning is specifically focused more and more on functions than marketing, but those modules will need to become social media-savvy by the year 2019. Future Enterprise resource planning systems will need to be able to integrate direct marketing & data gathering links across manifold social media channels to remain on the top list and highly competitive.

4.The Internet of Things (IoT) is going to stay- As more and more products and devices become connected to the internet, more data can be instantly funneled into the Enterprise resource planning system, and that’s an advantage that can’t be ignored. This trend offers better oversight over things like the supply chain and appliance performance, and it also gives overall data pool for good decision making.

5.ERP for the Subsidiary- As more Enterprise resource planning systems are being delivered via the cloud, it is becoming far easier to deploy such SaaS-based tools incrementally. Rather than replacing ERP whole-hog, big giant companies are selecting one slice of the business and plugging in SaaS Enterprise resource planning on a trial basis. This approach lets businesses observe SaaS Enterprise resource planning performance to evaluate how it might fit into the existing on-premises Enterprise resource planning implementation—or whether it should replace on-premises Enterprise resource planning throughout the whole enterprise.

How AI Adoption Actually Bang and Turn QA Expectations?

Software testing industry is becoming extensive with every passing day. With the sudden boost in the technology challenges, apps are growing in complication which creates an incessant need for effective software testing. Software testing is the premeditated way where an app can be observed under definite conditions and where software testers can detect the risks involved in software implementation. Testing is an imperative process that guarantees customer satisfaction within an app and assists to safeguard against potential failures that may prove to be detrimental down the line. It is a planned procedure where the app is reviewed and analyzed under definite conditions to understand the overall risks and threshold involved in its implementation.

According to the State of Testing Survey 2017, the future is about the automated test as 62% of respondents believe it will boost in the next few years. According to the similar report, we can also expect testers spend extra resources and time on testing mobile & hybrid apps, with the time used on actual development shrinking. When it comes to automation tests, Artificial Intelligence is being extensively used in object app categorization for all user interfaces. Here, recognized controls are classified when you create tools & software testers can pre-train controls that are generally seen in out of the box setups. Once the hierarchy of controls is observed, software testers can create a technical map such that the Artificial Intelligence is looking at the HUI (Graphical User Interface) for obtaining labels for the diverse controls.

Artificial Intelligence in Software Testing

Simply said, Artificial Intelligence is the science behind computers performing significant tasks that are traditionally performed by individuals. It provides machines with the critical skill to process information about its conditions & learns to adapt to the changes and modifications. Machines are learning more rapidly than ever given the technology advancements. The computer is fed with a high amount of data to adapt as per a series of inputs so that it can recognize patterns and logic and as a result make an effectual connection between similar input & output pairs. It is all the way through machine learning that websites like Amazon and Netflix are able to bring targeted advertisements to their customers by using vast amounts of data based on the web pages the user searches. Machine Learning has to turn out a long way with people using it for driving cars without a human. Artificial Intelligence has become quite popular in the field of healthcare also as it is used to recognize high-end flaws and loopholes. As Artificial Intelligence begins to progress, technology experts around the world are finding means to leverage its potential in the software testing field as well. It is merely a matter of sometimes when training apps to be familiar with issues for agile and more efficient testing becomes a reality.

AI is one of the best choices for developers looking for speedy deployments with insufficient infrastructure. It is better to leave the arduous or strenuous work to the Artificial Intelligence-powered automation which leaves only 18-20% of the test work to human ingenious and cognitive ability. It would guarantee more fail-safe results as hand-crafted testing doesn’t just require long human hours but is also susceptible to imprecision’s and inconsistencies. Quite the reverse, Artificial Intelligence bots-based testing requires very less maintenance and is skilled enough to find out new trails through the product on their own. As industries realize the compensation of Artificial Intelligence and Machine Learning, developers are considering it to drive automation, facilitate decision making, and also perk up efficiency in the area of software testing. Machine learning and AI are unquestionably becoming vital components in Quality Assurance and software testing as well. By adding Artificial Intelligence to test creation, execution, and data analysis, expert testers can rapidly identify controls, spot links between defects as well as other components, and eliminate the requirements to continually update test cases by manual means.

Conclusion

The biggest “A-ha!” moment for us is when we realized that the complications we solve with AI are not deterministic. Machine learning gives software testers the opportunity to better understand their client’s needs and respond more rapidly than ever to their changing expectations. Besides, testers now also need to analyze more data and they are given lesser time to do that, while their margin of error and flaws decreases constantly. Tools like machine learning and predictive analytics offer the amazing means to address these challenges, either with in-house well-versed tester’s teams or, turning to QA outsourcing providers. Either way, this approach is set to fill the gaps of traditional techniques of testing and make the whole process effective and relevant to the customer’s needs.

All You Need to Know about the Internet of Things (IoT)

The era of modern technology began in 1973 with Motorola’s invention of mobile phones. Then the idea of ‘ network of networks ‘ came in 1983, and in 1990 it took a more recognizable form. Smart phones took the world to a whole new level somewhere in the early twenty-first century. Every day, the world’s constant increase in technical intensity is becoming smarter, after the smart mobile phones, we’ve made everything around us shrewd. With the Internet of Things, the latest developments in the technical era make things easy to access.

What is the Internet of Things?

Simply put, this is basically the concept of connecting any device to the Internet (or/and to each other) with an on & off switch. This includes everything from headphones, mobile phones, washing machines, coffee makers, lamps, wearable devices, and just about anything else you might think of. The Internet of Things (IoT) is the inter-networking of physical devices and network connectivity that allows these objects to collect and exchange information.

IoT evolved from the convergence of wireless technologies, micro-electromechanical (MEMS) systems, micro-services, and the Internet. The connection helped to tear down the silo walls between Information technology (IT) and operational technology (OT) making it possible to analyze unstructured machine-generated data for insights that drive improvements.

This all depends on your industry: in terms of IoT, manufacturing is perhaps the farthest forward, as it is useful to organize tools, machines, and people, and track where they are. Farmers also turned to connected sensors to monitor crops as well as cattle, hoping to boost their herds ‘ production, efficiency, and health.

The examples are endless, and all we can predict is that connected devices are likely to penetrate most businesses, just like computers and the web. When the efficiencies are with tools or plants, it’s easy to appreciate the potential benefit, but when it’s office workers who are being squeezed for more productivity, it might take a bit of a dystopian shade: imagine your security access card is used to track where you’re in the building, so your boss can totally spend how much time in the kitchen making tea.

How big is IoT?

HP conducted a small survey estimating the rise of connected devices over the years and surprising results. Are we moving towards a world that is fully automated?

These devices will bridge the gap between the physical & digital world to get a better life, society, and industries ‘ quality and productivity. Smart homes are the most awaited feature with IoT catching up, with brands already competing with smart appliances. Wearables are one more feature trending second on the internet. With the launch of Apple Watch and more devices to flow in, these connected devices are going to keep us hooked with the inter-connected world.

An Explosion of Connected Possibility
An Explosion of Connected Possibility

Industrial Internet of Things

The Industrial Internet of Things originally described the IoT (Internet of Things) as it is used in a number of industries, such as manufacturing, logistics, oil and gas, transportation, energy/ services, mining and metals, aviation, and other industries, and in use cases typical of these industries.

Industrial IoT in the above sense was mainly used to distinguish between use cases, actual use and specific technologies as leveraged primarily for manufacturing and, later, other industries on the one hand and IoT enterprise applications and consumer IoT applications on the other.

IOT and its Impact on Testing

So what impact on software testing will the Internet of Things have? What’s going to change? Just as introducing smartphones and mobile apps brought new concerns to testing (including touchscreen gestures, location awareness, and orientation concerns), testing smart devices on the Internet of Things will also require some recalibration. While tests running against computer software involve keyboard and mouse input, smart device testing must take into account the data received from the sensors of the device as well as user input by tapping and typing. It will be necessary to test “in the wild” will be required.

IOT Testing Types
IOT Testing Types

The Internet of Things could restructure the development of software across the board and release it into an intuitive operation in depth. The incredible growth in IoT customer demand is amazing software companies, with many predicting that the Internet of Things will permeate the industry by 2020. While companies were the largest initial IoT consumers, public enthusiasm for mobile computing devices has recently outstripped corporate interest. Consequently, the present challenge is to increase the scope of IoT testing.

Automated testing is essential for IoT testing to be designed, planned and implemented. IoT is set to be a reminder of when mobile phones and tablets started off in the early 2000s with a whole new set of development and testing requirements. IoT brings new software deployment development and testing requirements.

Looking at the innovative onslaught of the Internet of Things one sees mobility and diversity advancing technology. With test integration and automation, QA teams are well-set to design testing procedures to ensure further IoT technology expansion.

But, Is It Safe to Get Things Connected?

The facts say that many development teams for IoT applications do not follow industry best practices to design safe and secure embedded systems, putting all mobile applications and the entire IoT infrastructure at risk. As technology becomes more intertwined with the physical world, the effects of failures in security are escalating. Like a chess game – where simple rules can lead to nearly limitless possibilities – the complexity of IoT interconnections exceeds our ability to unravel them quickly.