Increasing Importance of Quality Engineering in Software Testing

How can a company win? One of the key criteria is to ensure good quality of its products and services. But the traditional testing and QC paradigm is not enough in the context of emerging technologies. It has proven to be inefficient: if some shortcomings are revealed, the product may have to be redesigned, requiring additional expenses and extra time. That is why something new is being executed in business — quality engineering solutions. Quality Engineering (QE) is the series of procedures by which software quality is analyzed and improved throughout the application or software development lifecycle. It differs from traditional Quality Assurance in that it prevents defects as well as discovers them.

The QE approach implies that every single stage of the product/ software development cycle is under a scrupulous test of quality engineers. Furthermore, the quality maintenance is offered long after the product is delivered. The execution o such strategy in manufacturing or software development procedures guarantees the sufficiency of the output from the very start reduces imperfections, flaws, and reduces potential losses. In other words, quality engineering is the analysis, development, management, and maintenance of diverse systems compliant with high standards.

What are the rewards of Quality Engineering?

With Quality Engineering, the core benefit for your application development cycle is that you are actually making all the proposed advantages of DevOps and Agile more real. Also the teamwork between developers and testers is more real, more in line with the agile ethos. It is also integrated with Test Management solutions so that the outcomes appear on the dashboard instantly, without a human trigger. With shortened release cycles, time to ensure Quality also reduces considerably. Testers have to be involved at the start of the cycle as they will be setting up the testing environment and framework which will be relied upon for all future sprints. Done right, Quality Engineering offers a great deal more speed in testing. It mainly relies more on Test Automation than manual testing. It is hard to imagine a Quality Engineering function that doesn’t have Test Automation at its center. Yet again, done right, it creates more flexibility and speed for the whole development cycle. It is not considered just functional and non-functional testing, but every single layer and integration that can and should be tested.

In current Digital era, a Quality Engineer should have experience in programming and be supposed to be able to write software as the situations demands. While the Software Development team focuses on constantly upgrading the application, the Quality Engineering team main responsibilities are:

  • Setting up new parameters and standards
  • Optimization of test cases, & improving automation efficiency
  • Identification of drawbacks
  • Generating a plan for improvement
  • Plan execution using different tools and methods
  • Assessing & implementing new technologies and tools
  • Following up to make sure that issues have been solved
  • Creating tailor automation solutions to address application specific use cases
  • Create frameworks & accelerators that help scale QE across manifold channels, Enterprise wide.

Quality engineering is driven by emerging technologies like AI (artificial intelligence), Big Data analytics and IoT. Automation is the driving force behind turning the traditional testing into an effectual quality support model.

Bottom line

Performance of the application/ software is of paramount importance. Every outage, crash, drawbacks, and even slowing down of the app or processing/ working on a client request has the potential to impact revenue directly. It is the responsibility of QE team to not only identify such issues, but also work on identifying/removing the root cause of such problems. This demands a sound understanding of app architecture, monitoring tools, several enterprise sub systems that are catering to the app etc. Overall, Quality Engineering team provides substantial insights about the root cause or issue and solved it in the fastest possible manner.

Cloud Computing Trends to Craft in 2019

Ever since its inception in 2000, the Cloud computing has been a buzz topic in the business world and has also proved itself to be the cherry on the cake in terms of digital data processing & storage. Cloud computing is now no longer just a tool, it has advanced as a scalable service offering and a delivery platform in the computing services field. Organizations these days have shifted their focal point towards discovering the appropriate procedures to handle and deal with cloud computing to accomplish their business goals. Along with several value-added services, cloud technologies have emerged from personal cloud storage to a genuine and secure data storage network for the enterprise regardless of its size. So, what’s ahead to 2019 and all important future years? Let us take a look at fresh trending cloud computing attributes of 2019.

Cloud Computing Trends 2019

In the near future specialists foretell that the cloud technologies are more likely to be a utility; this is because approximately every single app that runs on any platform will necessitate storage. In addition, with the advent of IoT, the current era in cloud storage is expected to emerge in the upcoming year. So, let’s take a brief look at “New Cloud Computing Trends in 2019.”

  • Trend of Cloud Storage Capacity

    Get into Cloud Computing if you need Hassle free Storage. Cloud will become an inevitable division of management and operation for almost all enterprises. Software as a Service (SaaS) has already created its trademark in the year 2017 and is now all set to open a flexible and financially smart door for companies and consumers to try early cloud services.

  • Blend of IoT, and Artificial Intelligence with Cloud

    In 2017, the introduction of IoT with its Artificial Intelligence was the talk of the town, and it emerged as the buzzing topic for the business application enthusiasts. The constant progression of IoT with real-time analytics and cloud computing has taken Artificial Intelligence to a new level and has even made it likely for IoT to make things easier and simpler technological interactions at different levels. With IoT spearheading at a fast pace, it is no doubt that it will be at the forefront of technology innovations in 2019 too.

  • Security Becomes Critical

    As we all are aware of the fact that technology has two ways to it. In one side it will give us real power to change the world while in the flip side it always remains prone to the security breach. The advantages of Cloud are trumpeted around the globe and for most enterprises; it has become the simpler decision to embrace Cloud. With the introduction of the GDPR – General Data Protection Regulation, the stress on security concerns has gone even higher. As early as 2019, small enterprises could find themselves threatened with the possibilities of data-theft and an inability to sustain with the General Data Protection Regulation’s rules.

  • Quantum Computing – Closer Look

    We have been discussing quantum computing for decades but in recent times. In reality, as the race for cloud supremacy heats up, we are getting pretty close to realize the dream. All the leading tech giant companies are working around the clock for building the first quantum computer. If they attain that, we will soon make better financial models, solve intricate medical issues, and even we have human-like communications with artificial intelligence. Last November, we took a glance at the fastest available quantum computing platform globally with IBM’s 20-qubit cloud computer. The year 2019 also promises to deliver a whole lot more within the quantum computing space that can modernize cloud services and solutions.

  • Hybrid Based Cloud Services Are on the Hype

    The major complaint about the business owners have when moving their hosting & computing services to the cloud is that it is really difficult. The hybrid cloud can be a blend of on-premise, third-party private cloud, and public cloud services. This system allows workloads to move flawlessly from private to public clouds with no difficulty. With the implementation of the use of a hybrid cloud, enterprises can enjoy a higher degree of flexibility & a range of data deployment options.

Impact of cloud technologies has been global, yet fewer than half companies use public cloud platform. However, the popularity of cloud computing technology is on hype, and 2019 is poised to adopt new cloud technologies and help it achieve new heights.

Why I talk about Context-Driven Testing?

As we know sometimes Testing Practices and Testing Techniques become very rigid and imitation based. So there must be some way by which we can easily shift our testing practices, techniques and even definition as per the circumstances or requirements. This is exactly Context-Driven Testing.

There can be different circumstances with every project we are going to deal with-

●    Requirements can be documented or not.
●    Enough time vs fighting schedules.
●    Tools Availability.
●    Clients Requirements.
●    Selection of best process for the project.
●    Trained employees’ vs untrained employees.
●    Time zone issues between the Development and Testing teams.

The Testing team working on the Context-driven testing are going to select their testing objectives, techniques, and deliverables (contains test documentation); also find out the details of the specific situation, the wishes of the stakeholders, etc.

The utmost priority of it is about doing the best with what we are having in our pocket. In Spite of applying “Principal practices and industrial testing standards”, we can accept each and every different practice or even different definition which can work best under different circumstances.

Basic principles of Context-driven testing –

●    The actual return of any practice is directly dependent on its context.
●    In context, there are good practices but not best practices.
●    How people are working together is very much important.
●    Over time, project unfolds in many ways, which are often not predictable.
●    The product is a kind of solution, if the problem doesn’t solve, the product doesn’t work.
●    Good Software testing is challenging deep thinking and intensive reasoning task.
●    Can we do the correct work at the correct time to do productive testing of our products via appropriate judgment, skill, and unified work?

Some testers may favor life-cycle models and organizational models. Let’s consider the V-model; it is a kind of disjunction between Testing group and Development group, here the testing team demand for all code along with detailed specifications. Context-driven testing has no room for this kind of philosophy. Also, agile development is related to a particular set of values that belong to only one kind of context. Context-driven testing is far broader than that. Testers get what they get, and they know how to cope with what comes their way. More importantly, a tester is basically a customer advocate. Testers should try their best to understand the customer position and make the best case when they feel it isn’t being addressed.

So the final call will be before ensuing Context-Driven testing, we should ask our self –

●    Do we value more in individuals rather than their interactions over processes or tools?
●    Do we value more in seeing working software over documentation?
●    Do we value more in responding to change over following the plan?

Expert testers can better explore how the product should work from a user’s point of view, and identify and address barriers that prevent users from fully adopting or accepting the product.

Context Driven Testing is not for every organization, and it’s not a replacement for other forms of testing.

Page Object Model and Implementation in Selenium

In testing department of today’s IT sector, Automation has a significant role. IT companies are leaning towards automation testing because there are endless advantages of automating an application. For programming language, automation gives flexibility. There are distinct types of frameworks that companies use for automating their applications. Some of which are mentioned below, and one of which is Page Object Model also popular as POM.

●    Page Object Model (POM)
●    Hybrid
●    Data Driven
●    Keyword Driven

POM is a type of framework which is very easy to understand and easy to implement while making architecture of any automation process. It basically enhances the test maintenance and reduces the possibility of duplication of code, which is very concerned thing in test automation. In other words POM is a structured base object repository design.

In POM we create a page class for each corresponding web page in the application. Now, the page class that we have created contains all the web-elements of that page and also that methods that we will perform on those web-elements, so the name that we give to a method should be according to its functionality, for example- for a log-in page, the name of the method can be login() which only has few elements like user-name, password, log-in button, forget password link, etc. and methods like passing strings in the field and clicking the buttons.

For making a robust and easy to maintain framework we use POM with data driven by collating excel with POM. The best combination would be POM with data driven through excels and run test cases through TestNG

Below mentioned the flowchart will make it clearer to understand:

Flow Chart

Implementation Example in Selenium 

  1. Create a new package file as Practice; we will be creating different packages for Page Objects, Utilities, Test Data, Test Cases and Modular actions. It is always recommended to use this structure, as it is easy to understand, simple to use and easy to maintain.
  2. Create a new class file and refer the name to the actual page from the test object. In our case it is Home Screen and Login Screen.
  3. Create a static method for each element in Home Screen. Each method will have an argument (driver) and returns a value (element).

package Practice;

   import org.openqa.selenium.By;

    import org.openqa.selenium.WebDriver;

    import org.openqa.selenium.WebElement;

public class Home_Screen {

    private static WebElement element = null;

public static WebElement MyAccount(WebDriver driver){

    element = driver.findElement(By.id(“id”));

    return element;

    }

public static WebElement LogOut(WebDriver driver){

    element = driver.findElement(By.id(“logout”));

return element;

    }

}

4.Reason of passing driver as argument selenium is able to locate the element on the browser (driver). Element is returned so that action can be performed on it.
5.Method is declared as public static so that it can be called in any other method without creating instance of the class.
6.Follow same rule for creating another class LogIn Screen.

package Practice;

import org.openqa.selenium.*;

import org.openqa.selenium.WebDriver;

import org.openqa.selenium.WebElement;

public class LogIn_Screen {

        private static WebElement element = null;

    public static WebElement UserName(WebDriver driver){

         element = driver.findElement(By.id(“id”));

         return element;

         }

     public static WebElement Password(WebDriver driver){

         element = driver.findElement(By.id(“id”));

         return element;

         }

     public static WebElement LogIn(WebDriver driver){

         element = driver.findElement(By.id(“id”));

         return element;

         }

}

7. Now create a new class which will be our test case, let’s say we are creating it in package called Framework by name POM.

package Framework;

import java.util.concurrent.TimeUnit;

import org.openqa.selenium.WebDriver;

import org.openqa.selenium.firefox.FirefoxDriver;

// Import package pageObject.*

import pageObjects.Home_Screen;

import pageObjects.LogIn_Screen;

public class POM{

private static WebDriver driver = null;

public static void main(String[] args) {

driver = new FirefoxDriver();

driver.manage().timeouts().implicitlyWait(10, TimeUnit.SECONDS);

driver.get(“http://www.store.demoqa.com”);

// Use page Object library now

Home_Screen.MyAccount(driver).click();

LogIn_Screen.UserName(driver).sendKeys(“testuser_1”);

LogIn_Screen.Password(driver).sendKeys(“Test@123”);

LogIn_Screen.LogIn(driver).click();

System.out.println(” Login Successfully, now it is the time to Log Off buddy.”)

Home_Screen.LogOut(driver).click();

driver.quit();

}

}

8.You will notice that once you type HomeScreen in your test script and the moment you press dot, all the methods in the Home Page will display. We can expose methods in order to reduce duplicated code. We are able to call these method multiple times. This will ensure a better maintainable test code, because we only have to make adjustments and improvements in one particular place.

**Implementation reference is taken from: toolsqa.com.

Artificial Intelligence Permeation in Testing

Software Testing is the process that ensures the customer is satisfied with the application and provides defect-free software. It allows testing an application under some conditions where maximum threshold and risks are involved in implementation. Testing ensures the quality, output and market efficiency of the software. Here comes the Artificial Intelligence (AI), which reduces manual effort and allows machines to write and execute test codes.

About Artificial Intelligence

AI allows the machine to read and process information at a very high-speed. They intelligently react to the environment changes and can learn things at a speedy pace. Some algorithms are applied that allow machines to analyze and identify data logically. It is a probabilistic approach applied to test the application. ‘AI Robots’ are introduced that performs testing with minimal human inputs. This will improve testing efficiency and decrease failure rates.

Benefits of Artificial Intelligence in Testing

  • Improved Quality: As testing is performed automatically, with assured security, the quality will be improved. It increases the market efficiency of the applications.
  • Effective: AI theories and algorithm focuses on reliable testing methods. It ends up reducing manual effort and intensive costs.
  • Timely Feedback: Since AI testing is automated it provides quick feedback on the application. It also reports the application’s efficiency.
  • Improves Trace-ability: AI checks the error by going through the code itself and leaves no error unattended. It resolves all issue and then proceeds forward.
  • Integrated Platform: Entire AI process is based on the embedded and integrated platform to run tests. Due to this, the website is launched easily by developers.

AI Robots take less time and capable to find testing paths on their own. They can be easily maintained. The bots are trained to process the data input and performs an action intelligently, like Android Auto Assistant. These bots are strengthened with time as the AI algorithms are continuously monitored to study behavior and input patterns.

Artificial Intelligence and Automated Tool

Intellectual decisions are bases for Artificial Intelligence automation when regression testing is performed. These are built on the algorithms with data and examples. The basis of AI Automation is the system’s intellectual decision-making ability. The information gathered by AI shows the application behavior, its stability, defect area, failure pattern and so on. The Artificial Intelligence System correlates the data information with existing test suites and also auto generates the test cases or test code by following the user story acceptance criteria. It supports code-less test automation on mobile or web applications. Artificial Intelligence mainly focuses on test management.

The following inputs are required to generate the tests automatically:


  1. Relevant data for the application
  2. Test results data for pass or failed use cases
  3. Requests and their valid data, that are to be run on production as well as the test environment
  4. Builds a version control monitor (can be SVN or others)
  5. Historical data, on which AI works

The names of the tools which are available to automate testing with Artificial Intelligence are: Testim, Appvance, Functionize, Endtest, Appitools.

AI Changing Test Automation

The machine learning AI is more of the statistics-based. It has transformed the way the test automation is performed. ML algorithms recognize all the patterns to predict the trends followed by an application.

Real-time examples that are following machine learning algorithms to embed AI:

  • Smartphones are using voice recognition software (e.g.-SIRI) that allow human interactions to do some action.
  • While online shopping, like in Amazon, list of recommendations to buy comes up as per previous experience is followed by Machine learning algorithms.
  • Visual Validation Automation Testing: This is an image-based testing technique done by visual validation tool. Like Applitools, it can find the differences that may be skipped by testers. This is required to verify if UI is as expected to the application users. It ensures that the UI element has correct position, shape, size, and apt color. It also tests if any UI element is not hidden and do not overlap too. For this, testers create ML test which detects visual bugs and validate the correctness. The AI system keeps screen-shots virtually in its mind to determine the state of the application.
  • Provides more reliable Selenium Automated Test: Let’s take a scenario where there are frequent changes (e.g.: changing the ID of Web element) to the application and Selenium tests fail as the element is not found. AI tools adjust to these changes automatically using Machine Learning algorithms and find locators, rather than doing changes on selector or path for the element. These AI tools start to learn about an application and understand the relationships among parts of DOM. It doesn’t break the tests and keeps track of the changes throughout time. This makes automated tests reliable and easy to maintain.
  • API Testing with AI: Some automation tools are available to remove complexity from the API testing. AI helps to simplify the process of API Testing. ‘Smart API Test Generator’ is a plugin embedded in chrome which helps to convert manual UI tests to automated API tests using artificial intelligence. It helps in building the comprehensive strategy to perform API testing. This tool identifies API calls, and then observes pattern followed and finally analyze the relationships among them. This process flow generates API testing scenarios.
  • Running more of precise test cases: When there is some change in code, it takes much effort to analyze the minimum number of tests to be run. For this, AI & Machine Learning is used to tell the precise number of tests to run. Also, AI is significant when the tester is unable to finish test-failure triage before the next build is released. Here, Machine Learning algorithm forms the ‘fingerprint’ of failed test cases in correlation with debug logs and system. It further predicts all the duplicate failures. This makes precision testing grow.

Effect of AI testing for Continuous Delivery

Testing allows developers to figure out if the application is working as expected in the real world. AI automation testing tools increase feasibility to identify the gaps in the application. AI enables testers to view wider issues, rather than just looking into repetitive ones. It offers the testers to customize the tests with combined information. These solutions are stored in metrics which tracks the success rate and perform execution cycles. The analytics collected is used to track issues in the software development cycle. Every industry is trying to learn and use AI supported apps that can automate tasks. Organizations may face multiple testing challenges when using ML and AI for testing application quality. AI algorithms optimize test suites, provide log analytics, traceability, and rapid impact analysis and get defect analytics.