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.

Cross Browser Testing- “IMPORTANCE” & “TYPES”

Cross browser testing is very critical and essential part of Software testing. Nowadays there are different types of browsers, devices, and operating system. Being a Specialist QA & Software Testing Company we had to ensure that the websites & products function across all mediums.

Buying and maintaining every single Operating system, device, and Browser is nearly impossible and not economically efficient. So there are tools which help us to test websites across these systems. Cross Browser testing is a type of Non-functional testing which helps us ensure that the web application works properly across the different browsers.

Need:

Browser testing diagram

In the diagram, you can observe that in IE, the login box of Twitter is not showing curve in every corner, but we are able to see that in Chrome Browser.

Most Common Reason behind Cross Browser Issues:

Device Offerings:

There are numbers of iOS and Android devices available nowadays by different manufacturers. So, the tool we use offers a wide range of devices.

Device Testing:

Testing with real devices vs. emulators gives you more accurate results. The perfect cross-browser testing tool will also allow us to use the application with natural touchscreen devices (Swipe, tap, zoom, and scroll).

Locally Testing:

The Cross Browser testing tool can also help us to test local builds. The ability to test development or staging environments is significant so that we can find bugs before they are released to users.

Automated Browser Testing:

Some tools support automated testing in addition to manual testing. Selenium testers can write Scripts to test their app or website & then run these scripts against multiple browsers and mobile device platforms within the tools.

Let’s take a Glance on different types of Cross Browser Testing tools

Cross Browser Testing can be the biggest pain for Tester. Thanks to different Cross-browser testing tools which help in minimizing the testing efforts.

1. BrowserStack:

BrowserStack test with real browsers on real machines which enable developer and tester to test websites and mobile applications. With this, you can conduct live testing and automated testing on mobile devices and browsers. The core feature seems to be the introduction to the test-development environment where you can easily test all you can without setup & configuration. Another trait that separates it is the native experience, where you can enjoy testing through the browsers with the aid of DevTools.

2. Sauce Labs

Sauce Labs is a leading cloud-based all-in-one framework for testing your websites and mobile applications across all browsers and devices. It mainly focuses on continuous testing so that you can get feedback on your changes rapidly throughout the development process. With Sauce Lab, you can list the currently run tests in sequential order, with information about the testing platform, runtime, build and whether they failed or passed.

3. CrossBrowser Testing:

CrossBrowser testing provides interactive browser compatibility testing over the desktop and mobile browsers. Their core features include live testing (manual testing), automated testing, and visual testing (screenshots). This makes a great platform for developers, QA, and designers to collaborate on testing projects.

There are loads of cross-browser testing tools available like Browsershots, Lambda test, Browserling, IE tab, Multi browser, BrowserEmAll, NetRendere, Litmus, etc. that help you to test the web application and mobile application (Android and iOS) for cross browser compatibility.

At ImpactQA, we have implemented Cross Browser Testing in several projects and clienteles are reaping the benefits listed above. For any questions or comments, please reach out to us at services@impactqa.com

The Vagabond of IT -Docker

In a world that relies heavily on the SOA and Micro services based applications, we often end up developing and testing an application in an environment that may or may not be the latest environment, thus causing issues for certain users who may not be using the same hardware/software configuration machines. The solution to that was tedious and required a lot of capital and manpower investment. To overcome this, the concept of Vagrant was developed. But that had its own limitations. Vagrant acts as a simulator of the live production environment and simulates the hardware and software configuration of an actual system. But like they say, no software is a perfect software, it had its own limitations.

Why not Vagrant?
Although Vagrant brought a revolution in the field of IT and was helpful in aiding the cause of continuous development, it limited the user to run their products on a single VM at a time. This meant that if a single VM had been converted to behave like a single deployment environment, to run the same product on a different configuration would require a separate VM. This would lead to over utilization of the system’s resources since each VM would need their own resources to run on a single machine.

How is Docker different?
Utilizing the concept of turning a VM into a deployment server from Vagrant, Docker took it to the next level. Docker is a tool that helps us virtualize the OS, filtering out the unwanted components and drivers and leaving us with a packet that not only contains our code but the entire OS. This process of creating packets is called as containerization and these packets are called as containers.

Using Docker, we can run multiple containers, each holding the same code but different configurations at the same time. Multiple instances/containers can be run on the same machine so long as the machine does not run out of resources.

How does Docker work?
Docker allows segregation of applications and OS in a lightweight package called a container. The container can be customized to reflect the behavior of the production server and with the help of scaling, the exact behavior of the production server can be reflected. This eliminates the issue that is commonly faced by the Developers, testers and Infra team where the code works on one of the environment and not on others or the one where the code works on one dev’s machine but not on others. If the container runs on one of the systems, it is bound to work on other systems that use Docker Engine.

Apart from eliminating the above mentioned issue, docker also helps in increasing the speed of delivery of the code. This is done by Docker’s ability to create a continuous delivery pipeline. This is aided by the fact that Docker isolates the process at the OS level, rather than the conventional VMs that do this on the Hardware level.

Conclusion
In a world where 91%*(*Data collected from https://techbeacon.com/survey-agile-new-norm) organizations either follow or are leaning towards following the Agile methodology, it is the need of the hour that organizations shift away from the conventional Development methods and adopt methods that can aid the quick creation, testing, and deployment of the Applications. These techniques are both applicable for the On-Premise and Cloud-based applications. One has to select the product based on the organization’s needs and the capital that they can spend.

Security Testing “THREATS” and “METHODOLOGIES”

There are varied types of security threats that the application or software is prone through that may cost your business enterprise, if not identified. With the progression in technology attackers bang-up some of the innovative and fresh ways to break into the security mechanisms of a system. Hence, it is vital for the testers to be aware of the several kinds of security threats and determine solutions to tackle them.

Here are some of the most common security threats that the expert testers discover during the security testing process:

Threat 1- SQL Injection

Such security attack happens when the hacker inserts degrading SQL statements into the entry field for execution. The aftereffects of SQL injection is sinful as it leads to leakage of classified info from the server database. It can be easily prevented by thoroughly checking the several input fields like comments, text boxes, etc.

Threat 2-URL manipulation

It is the procedure where hackers make the alteration to the URL query string to access information. Applications that use HTTP GET ways to pass critical info between client and server are normally prone to such sort of attack. As such, the tester must change the parameters to find if the server accepts it.

Threat 3-Privilege elevation

In this kind of attack, the hackers can use his/her existing account to increase the privileges to the top levels than what he/she deserves. If the hacker becomes the success in doing so, he/she will use the benefit for running the code and the system will eventually give in.

Threat 4-Data manipulation

It is a type of security attack which includes hackers gaining access to site or application data and makes the modification to it for their own benefits or to humiliate the owner of the website/ app. The hacker does this by approaching the HTML pages of the site.

Threat 5-Unauthorized data access

This is the well-known security attacks where the hacker gains access to data by unauthorized means which comprises:

  • Make use of data-fetching operations to gain access
  • Gaining access to data by evaluating the other’s access
  • Gaining access to reusable client authentication info by keeping track of the success of others.

Threat 6-Identity spoofing

In Identity spoofing security attack, the hackers use the credentials of a valid user or device in order to attack the network hosts, for data theft and to gain the benefits over access controls. IT- infrastructure as well as network-level mitigation are requisite to prevent such attacks.

Threat 7-Denial of Service

It is a major security risk or attack and the attacker aims at making a network or system resource unavailable to the valid users. When software or applications are prone to such threats, the application or the complete system may end up being unusable.

Threat 8-Cross-site scripting (XSS)

Cross-site scripting (XSS) risk allows attackers to insert client-side script in website pages and manipulate them into clicking the URL. After clicking on the URL is done by the users, the code automatically changes the way the website behaves and gives access to the attacker to steal critical information and other personal data.

Some of the security testing tools available for web applications:

  • BeEF (Browser Exploitation Framework)
  • BFBTester – Brute Force Binary Tester
  • Brakeman
  • Vega
  • Google Nogotofail
  • CROSS (Codenomicon Robust Open Source Software) program
  • Ettercap
  • Flawfinder
  • Gendarme
  • Knock Subdomain Scan
  • ZED Attack Proxy (ZAP), etc.

Security Testing: Critical Concepts & Methodologies as follows:

1. Understanding Context: Understanding the rules concerning security compliance and rules of the company and its impact on the use.

2. Pick out the Kind of Security Testing: The testers would then pick out the kind of security testing, after determining the security vulnerabilities and weaknesses lists in the application.

3. Testers will carry out Threat Modelling: The experience and adept testers would then carry out threat modelling, for creating a Threat Profile.

4. Next Step is to create a Test Plan: Creating a test plan to perform security testing after determining the list of vulnerabilities and potential threats.

5. Build a Traceability matrix: A traceability matrix would be created for every single identified risk or vulnerability.

6. Determination and Selection of Security Tool: Selection of a tool to be used for the testing.

7. Execute the Test Case Execution: Testers would then implement the test case after which they would detect the defects.

8. Preparing Test Case: Testers would then create the test case for the security testing.

9. Reports: The final step would be the submission of the final complete report of the security testing, which highlights the list of identified threats, flaws, and weaknesses.

In actual practice combination of several techniques may be used to have a comprehensive assessment of the complete security aspect. At ImpactQA, we provides customized security testing that aid Enterprises deal with immediate security threats to their business operations.