How does AI make your testing processes smarter?

 

 AI-based automation testing helps the testing processes to achieve a slew of benefits. These include automated building and maintenance of test suites, predictive self-healing, visual validation, and cloud-based virtualization.

The software development lifecycle is becoming more complex and tech-driven to meet the requirements of the quick release of quality software applications. Since software testing is a critical component of the whole process, it needs to be made faster, smarter, and better. The best way to achieve the same is to employ a smart AI testing strategy wherein QA specialists can move towards precision testing on a continuous basis. By leveraging machine learning, problem-solving, and reasoning, AI-based automation testing can reduce the quantum of tedious and mundane tasks in the SDLC. The key role of AI-driven testing is in reducing the direct involvement of QA specialists and developers in conducting routine activities. Normal test frameworks cannot analyze and decide the type of tests to be conducted, leaving it to the discretion of the testers.

It results in the testers running all test cases or a few of the scheduled ones thereby leaving testing at a sub-optimal level. An AI testing framework, on the other hand, can analyze metrics such as test status, code coverage, and changes, among others to decide on the type of tests to be run. In other words, Artificial Intelligence can carry out decision making without the help of the developer or tester. The best examples of using AI in digital technology are the smart chatbots or voice recognition software like Siri (Apple) and Alexa (Amazon). These two are routinely used by people to browse the web and look for information, find directions, book appointments, and manage other activities. Siri and Alexa leverage machine learning algorithms to analyze past data patterns to predict customers’ requests, understand their habits and preferences, and make recommendations. Alexa can even connect with the smart thermostat called Nest to automatically adjust the room temperature by using the behavioral data of the homeowner.

How can AI-based testing impact the overall testing scenario
The need to provide the best quality products quickly into the hands of the consumers has driven software enterprises to adopt new tools and practices. Robotics Process Automation (RPA) using AI is shaping business priorities to achieve new thresholds of quality, speed, and cost-effectiveness. As test automation remains an underutilized practice in most enterprises, AI-based automation testing can truly fulfill the testing requirements delineated in Agile and DevOps methodologies.

Building test suites: AI in software testing can simulate the behavioral patterns of users based on geography and devices, and use them as inputs to build smart test suites. Further, it can analyze data from the social media interactions of users, mine defects, and make intelligent decisions on test coverage and optimization of test suites, rapidly and consistently. 

Maintenance of test tools: The task of maintaining test cases is tedious because of the complex architecture and workflows of the test cases. Also, maintaining and executing automated testing tools still require manual intervention to some extent, which can be eliminated through AI automation testing. The time and effort saved by the testers can be put to good use by focusing more on exploratory testing.

Predictive self-healing: Changes made to software applications are a recurring feature, which often results in the breaking of UI tests as objects are not found. This is a major challenge faced by QA specialists who not only have to conduct tests but maintain the test suites and object repositories as well. An AI testing framework can dynamically update the test suites when the software application undergoes changes. Hence, QA specialists can cut down their time and effort on maintaining the test suites.

Visual validation: A robust AI testing strategy involving pattern and image recognition can detect visual bugs in the software application. This is done by ensuring the visual elements appear and function as expected. In fact, AI can identify and analyze the dynamic UI controls on a pixel level.

Cloud-based virtualization: Any software application needs to be tested on a plethora of devices, their screen sizes and resolution, operating systems and their versions, and browsers and their versions, among others. These have led to increasing the complexity of test environments wherein testing teams have to filter and execute UI testing. No wonder, managing such test environments has become cumbersome, time-consuming, and expensive. The Artificial Intelligence led testing can analyze historical test results and user data to help teams virtualize the specific test environments on the cloud.

Conclusion
With the rising complexities of software applications, device platforms, and test environments, AI test automation is the way forward for enterprises to stay competitive. It helps QA specialists to achieve quality control, stability, maintainability, and scalability of the test suites.

How Software Testing can be benefited from the use of AI

 

 

Enterprises operating in the digital realm can achieve success if they ensure their software applications are updated as per the latest trends and customer preferences. This forces the IT teams to be faster and agile, and adopt measures to align their work culture to meet the demands of the business. No wonder they are adopting Agile and DevOps methodologies to speed up software development and delivery besides fostering collaboration. Agile and DevOps have quality assurance embedded in them leading to continuous testing of the code build.
 
Continuous testing ensures that any change in code is implemented instantaneously and effectively. It also places iterative QA across the SDLC thereby accelerating the build quality. However, continuous testing in the omnichannel environment can be riddled with issues such as a lack of visibility of requirements across processes, siloed automation, and a high volume of tests, among others. To address these issues and drive quality at speed across the SDLC, it is important to adopt an AI-based testing approach.
 
How can AI facilitate continuous testing?
 
Artificial Intelligence can remove manual intervention and smoothen the process of continuous testing. In AI testing, QA teams can initiate the test cycle to identify and fix bugs based on insights drawn from past events and historical data sets. AI-driven testing ensures that only a robust code is moved across the SDLC thereby foregrounding quality as the centrepiece of software development. Even though the testing activities are automated in continuous testing, the code needs to be signed off manually to move across quality gates based on the test results. This siloed automation can disrupt the flow of code in the SDLC. However, with AI test automation, the code checking at quality gates is done automatically and code progression is managed by analyzing the test results. This makes the workflow fully automated. By triggering QA processes with artificial intelligence, the testing teams can achieve the following:
 
Automation of quality gates: Given that the AI algorithm can determine the course of code progression based on historical data, the testing teams can allow/entrust the AI testing services with facilitating code progression. Conversely, the AI engine can prevent code progression if it finds a high probability of outage or disruption to be caused by the presence of bugs.
 
Identification of root causes: AI-based testing can analyze the patterns and correlations in a build to identify the root causes of defects. Thereafter, the AI engine can orchestrate remedial tests to reduce the margin of errors in the build.
 
Focus on brand assurance: With an AI testing framework, test automation is made all-pervasive and intelligent. This allows the QA teams to glean insights from historical data, business acumen, and customer inputs, thereby shifting the QA focus to brand assurance and competitiveness.
 
Predictive analysis: AI-based testing can analyze the existing customer data to understand how the user behavior and needs will evolve in future. These insights will give the software development and QA teams to come up with suitable features and functionalities to be ahead of the curve.
 
API test evaluation: The performance of a software application depends to a large extent on the quality of interactions between various modules connected with databases and servers, among others. By evaluating the performance of the APIs, the testers can validate if the connection between various modules in the application is stable. They also check if the requests are processed accurately and the customers will get the correct outcome post a specific interaction. Artificial intelligence led testing can analyze the functionality of the APIs and large volumes of data to detect risks and create precise test cases.
 
Scriptless test automation: Even though test automation has revolutionized the QA process, the writing of test scripts to automate various test processes can be a challenge. Besides, the scripts need to be maintained as well for further usage thereby escalating the cost. However, an AI testing framework can create test cases automatically leaving the QA specialists to focus on other areas of software testing. Often developers writing test cases do not have the time to explore additional possibilities for the test cases. Consequently, they may end up writing test cases they are familiar with rather than writing the ones that can generate the least redundant data. AI-driven testing can run a minimal number of tests to understand the kind of impact (positive or negative) a code change can entail. With limited manual intervention, the artificial intelligence led testing can ensure no redundant data and bottlenecks are there in the application.
 
Conclusion
 
Artificial Intelligence can open up new avenues for testing software applications. In a day and age where the speed and quality of applications go hand-in-hand to generate superior user experiences, reduce costs, and improve profitability, AI-based testing has become an enabler for enterprises to stay competitive.

Article Source:
https://medium.com/