5 Great Ways to Achieve Complete Automation with AI and ML

5 Great Ways to Achieve Complete Automation with AI and ML

Automation in the testing domain has evolved a lot when it comes to Artificial Intelligence and Machine Learning specifically. Real-life examples including self-driven cars, bots, and the famous Amazon-owned product, Alexa are some of the basic examples of how AL and ML have influenced our lives and day to day activities. Talking about the competitive product line being released every other day, with updated application software and devices making users lives easier than ever, emphasis on the demand for product quality for users. Customers are becoming intolerant to product defect with the numbers of alternatives available to them to switch in the market. The below-mentioned statistics are true when talking about the loyalty a customer can portray for a particular product or service for a company.

 

91% of non-complainers just leave and 13% of them tell 15 more people about their bad experience for a product

 

This kind of cut-throat competition, thought healthy, is naturally leaving an impact on the quality assurance operations of any industry. The QA processes for any QA Testing company is becoming more and more complex abiding by the increasing complexity in software structures. Adding to the hustle is the demand for quality products with increased speed in delivery. All this combined direct towards having an apt end to end testing solution for any organization. Given the limited time frame, creating dedicated test cases and writing scripts from scratch becomes a challenge while covering all crucial test scenarios.

 

A single bad experience on a website makes users 88% less likely to visit the website again

 

Situations like these have given opportunities to major QA companies to leverage the potential of artificial intelligence and machine learning to achieve high test automation with increased speed and better quality and efficiency. Such technologies can help you cover high-risk test scenarios and achieve complete test coverage in the given stipulated timeframe. Analysts are continuously aiming towards reducing test automation as much as they can and replacing them with the new age test automation technologies.

Limited-time period for companies to deliver software projects becomes a challenge for software testing teams. Project delivery cycles is one such example that needs to incorporate and leverage the features of test automation with AI and ML to eliminate such challenges.

Test automation using AI is the new buzz in town that’s forcing companies to use it as an integral part of their development and testing process throughout.

Also Read: 5 Ways AI is Shaping the Future of Software Testing

 

Addressing Challenges in Test Automation Through AI and ML

As mentioned, the best testing results can be derived by infusing smart and intelligent test automation tools to address pain points in traditional test automation. Now let’s talk about how smart test automation techniques using AI and ML can help project teams to reduce the testing effort and improve test coverage.

 

1. Self-Healing for Test Automation

 

ImpactQA - Self Healing of test Automation
Self Healing Process

 

The self-healing technique in test automation solves major issues that involve test script maintenance where automation scripts break at every stage of change in object property including name, ID, CSS etc. This is where dynamic location strategy comes into the picture. Here programs automatically detect these changes and fix them dynamically without human intervention. This changes the overall approach to test automation to a great extent as it allows teams to utilize the shift-left approach in agile testing methodology that makes the process more efficient with increased productivity and faster delivery. Small examples include how the UI identifier in the test case is automatically rectified whenever any change is made in the object identifiers in the HTML page by your developer. The AI engine locates these elements despite the changes in the attribute and then modifies it according to the changes made in the source code. This self-healing technique saves a lot of time invested by developers in identifying the changes and updating them simultaneously in the UI.

Below mentioned is the end-to-end process flow of the self-healing technique which is handled by artificial intelligence-based test platforms.

As per this process flow, the moment an AI engine figures out that the project test may break because the object property has been changed, it extracts the entire DOM and studies the properties. It runs the test cases effortlessly without anyone getting to know that any such changes have been made using dynamic location strategy.

 

2. Auto Generation of Test Scripts

 

ImpactQA - Auto generation of test scripts
Auto Test Script Generation Process

 

Developing automation test scripts is a tiring task that involves using highly skilled programming languages such as Java, Python, Ruby etc. This entire project requires a lot of initial effort, time and skilled resources. Alternatively using automation scripts for the development reduces this testing script generation process to almost 50%. Additionally, infusing AI and Machine learning techniques into this process eases out the test script designing process as a whole.

There are various testing tools available in the market, where selenium automation test scripts are built using manual test cases. The platform reads the test scripts and generates automation scripts automatically. The AI algorithms here use NLP or Natural Language Processing which are well trained to comprehend the intend of the user and mimic those actions on the web application. The good part is, that this entire action is delivered without the engineer having to write a single code by himself. This ultimately reduces the test script design time and effort by 80%. This entire concept is commonly referred to as Touchless testing.

 

3. Utilize High Quantities of Test Data Effectively

Many organizations that implement continuous testing with Agile and DevOps methodology opt for an end-to-end rigorous testing approach throughout their software development life cycle multiple times a day. This includes unit, API, functional, accessibility, integration and other testing types.

As the execution of these test cases come into the picture, the amount of test data that’s created grows significantly. More the data at stock, the harder it becomes for executives to make better decisions with accuracy. Machine learning identifies the key problem areas here, by visualizing the most unstable test cases and other sections to focus on, thereby making lives easier for developers.

Slicing, dicing and analysing test data becomes easier with AI and ML systems in the picture. It enables reading patterns, quantifying business risks and accelerating the overall decision-making process for any project in hand. A basic example can include identifying which continuous integration job to prioritize or spot which platform under test environment has more bugs than others.

With the absence of artificial intelligence or machine learning in the process, the entire script designing framework can be prone to errors, that’s mostly manual and highly time-consuming. With AI and ML analysts can utilize better features around

  • Test impact analysis
  • Security holes
  • Platform-specific defects
  • Test environment instabilities
  • Recurring patterns in test failures
  • Application element locators’ brittleness

 

Also read: How does Artificial Intelligence Benefit Software Testing?

 

4. Image-Based Testing Using Automated Visual Validation Tools

Leveraging the latest machine learning technologies in image-based testing using automated visual validation tools is becoming more and more popular amongst the testing community.

To simplify, visual testing, also referred to as user interface testing, in software development ensures that the UI of the web or mobile application they are building appears to the end-user as it was originally intended. It’s mostly mistaken with traditional or functional testing tools that were designed to assist developers with the functionality of the application through updated UI.

A majority of the test being conducted in this process are usually difficult to automate and ends up being a part of the manual testing process that’s technically ideal for AI and ML testing. Using ML-based visual validation tools enables testers to identify elements that could be easily skipped in the manual testing process.

This infusion of image-based testing can dynamically change the way companies deliver automation testing services in any system. Testing analysts can create machine learning tests that automatically detect all visual bugs in any software. This can help in validating the visual correctness of the application without the testing expert having to implicitly insert inputs into the system.

 

5. Spidering AI

The latest Artificial intelligence-based automation technique being used amongst developers today is using the spidering method to automatically write tests for your application. All you need is to point some of the newer AI/ML tools at your web application to initiate crawling.

Along the process of crawling, the tool collects data by taking screenshots, downloading HTML codes for every page, measuring load and so on as it continues to run the steps repeatedly. Ultimately, all this tool is doing is building a dataset and training your machine learning model for what the expected patterns and behaviour of your application are. As a result, the tool compares its current stage with all the previous patters it has observed. In case of deviations, the tool will flag that section as a potential bug in the testing process. Next, a human with the required domain knowledge still needs to go in and validate whether or not the issue being flagged is really a bug. So, although the ML tool takes care of the major bug detection process, a human would have to do the final verification before taking a call.

 

You Would Also Like: How AI Adoption Actually Bang and Turn QA Expectations?

 

Be A Domain Expert in AI and ML-Based Test Automation

To be able to achieve an expertise in leveraging artificial intelligence and machine learning in the testing domain requires you to have deep roots with the ML testing algorithms and come up with a strategic approach towards testing. Keeping this in mind, you need a testing team that knows how to break and analyse complex data structures into simplified representations to help you enhance your decision-making process and increase your overall project efficiency and effectiveness.

Keep going…

With AL and ML standing on the centre stage, it’s time for most of the companies to adopt these new technologies into their testing process and deliver better services with speed.

Get in touch with ImpactQA, a leading software testing and quality assurance company, to improve your QA efficiencies using artificial intelligence and machine learning and transform your technological landscape to a new level.

Have a new project in mind? Schedule a free consultation now!

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