Artificial Intelligence (AI) is the latest technology to be leveraged across all industries and domains. AI continues to make its way into domains previously reserved for human skills and has been enabling significantly dominant results. On a similar note, AI is being leveraged in the field of software testing also to ease the automation testing process and deliver more quality outcomes.

Content 1.  What is AI-based Testing? 2.  How has AI evolved in software testing? 3.  Some of the benefits of leveraging AI in software testing 4.  What are the 4 key AI-driven testing approaches? 5.  What are the 4 main categories of AI-driven testing tools?   5.1  Differential tools   5.2  Visual AI testing tools   5.3  Declarative tools   5.4  Self-healing tools 6.  Conclusion

According to the World Quality Report 2020-2021, it is stated that the interest in AI-based testing is increasing, and approximately 90% express that their organizations are focusing on significant growth in the areas of AI testing and testing of AI. Additionally, around 80% of respondents plan to augment the number of AI-based trials and proofs of concept.

Typically, the application of AI in software testing will make the entire testing process faster, clearer, easier, and budgeted. Therefore, AI-based testing will provide a strategic platform where software testers can leverage AI and take the testing process to a new level and thus deliver more quality results to businesses.

Test automation tools have been supporting the continuous testing process for many years.  But, now the need for more effective tools and techniques such as embedding AI in software and QA testing has become a mandate for delivering quality software and for ensuring superior customer experience.

What is AI-based Testing?

AI-based testing is a software testing technique in which AI and Machine Learning (ML) algorithms are used to effectively test a software product. The objective of AI-based testing is to make the testing process smarter and highly effective. With the inclusion of AI and ML in testing, logical reasoning and problem-solving methods can be applied to improve the overall testing process.  Further, in this testing method, AI testing tools are used to execute the tests that use data and algorithms to design and perform the tests without any human intervention.

How has AI evolved in software testing?

The paradigm of software testing has evolved significantly over the past two decades. Right from manual testing to automation testing, where selenium is considered to be one of the finest test automation tools, the testing journey has been quite encouraging. However, in today’s fast-paced IT world, the domain of software testing has to come up with innovative and well-researched testing methodologies. For this purpose, the dawn of AI-based testing has come up and has been proving very impactful.

AI algorithms can completely mimic human intelligence, and ML allows computers to learn automatically without any human intervention. Interestingly, AI and ML involve the development of unique and specific algorithms that can access data, learn from that data by extracting patterns to make decisions, and these predictions are to be used in software testing effectively.

Moreover, enterprises are rushing towards tools that can leverage AI and ML algorithms and can be used for testing the software effectively. It has also been seen that businesses get many benefits from AI-based testing as it will enable faster and continuous testing, complete automation without any human intervention, and also enables quicker ROI.

Some of  the benefits of leveraging AI in software testing

Visual validation –

AI has pattern recognition and image recognition capabilities that together help to detect visual bugs by performing visual testing on applications. It helps to make sure that all the visual elements are engaging and can function properly. AI can recognize dynamic UI controls irrespective of their size, shape and analyses them at a pixel level.

Improved accuracy –

In the manual testing method, the chances of human-prone errors are high, especially in situations of repetitive tasks. Automation testing helps in removing these human-prone errors. But, with the advent of AI in automation testing, repetitive tasks are handled more effectively and the results are recorded more accurately. Thus, AI helps in removing the minute chances of errors and improves the overall accuracy of tests.

Better test coverage –

AI in testing increases the test coverage as it can check the file contents, data tables, memories, and internal program states seamlessly. It also helps to determine if the program works as expected and delivers effective test coverage.

Saves time, money, and efforts –

Software tests need to be repeated whenever there is an instance of change being made in the source code. Manually this becomes time-consuming and takes a lot of effort from testers. But, with AI-driven tests, repetitive tasks are handled properly, quickly, and efficiently.

Faster time-to-market –

AI-driven tests support continuous testing, and thus products are released faster which helps businesses go early-to-market.

Reduces defects –

AI in testing helps in early and fast bug identification, which ultimately reduces the defects and makes the product bug-free, and reliable for end-users.

What are the 4 key AI-driven testing approaches?

Differential testing – Differences are classified and application versions over each build are compared in this type of testing.

Visual testing – Visual testing is a software testing technique in which the look and feel of an application are tested by leveraging image-based learning and screen comparisons.

Declarative testing – It aims at specifying the intent of the test in a natural or domain-specific language. The system decides how to perform the test

Self-healing automation –Element selection in tests is auto-corrected when the UI changes.

What are the 4 main categories of AI-driven testing tools?

Differential tools

These tools leverage AI and ML algorithms to identify code-related issues, security vulnerabilities, regressions, etc. This is achieved through code scanning, unit test automation, etc. Some of the tools under this category are:

1. Launchable:

Launchable is based on an ML algorithm that predicts the likelihood of failure for each test based on past runs and whenever the source code changes under test. This tool lets the user record the test suite so that tests that are likely to fail are run first. One can choose this tool to run a dynamic subset of tests that are likely to fail, thereby reducing a long-running test suite to a few minutes.

2. Google OSS-Fuzz:

It is a fuzz testing tool that aims to make common open-source software more secure, stable, and reliable. This tool combines modern fuzzing techniques with scalable, and distributed execution. This tool supports C/C++, Rust, Go, and Python code

Visual AI testing tools

With the ever-growing number of platforms that vary in screen sizes and have different configurations, it has become a tedious task for test engineers and developers to effectively test the UI layer. Also, the UI layer experiences constant changes from time-to-time as businesses wish to provide a better user experience. Therefore, today there is a dire need for visual AI testing tools that effectively test all variations of these UI layers. Some of the tools that fall under this category are:

1. Applitools:

This is an AI-powered visual testing and monitoring platform. This has been named a next-generation test automation platform powered by Visual AI. The major features include Applitools Eyes which helps to increase test coverage and reduce maintenance. The Ultrafast grid helps with cross-browser and cross-device testing and accelerates functional and visual testing by 30 times. This Applitools platform integrates with all modern test frameworks and works with many existing testing tools like Selenium, Appium, Cypress, etc.

2. Percy by BrowserStack:

It is an all-in-one visual review platform that comes with amazing features such as pixel-by-pixel diffs, responsive diffs, and snapshot stabilization. This tool allows cross-browser rendering, high-speed rendering, and has parallelization capabilities. Percy helps teams automate visual testing. This Browserstack tool is used to typically capture screenshots and compare them against the baselines and display visual changes. It increases the visual coverage and helps teams to deploy code changes with confidence.

Declarative tools

These tools aim to enhance test automation productivity and stability. These tools leverage AI and ML and have significant abilities related to Robotic Process Automation ( RPA), Natural Language Processing (NLP), Model-based Test Automation (MBTA), and Autonomous Testing Methods (AT). The main aim of these methods is to eliminate tedious, error-prone, repetitive tasks through smart automation. Some of the tools that fall under this category are:

1. Tricentis:

This is an AI-driven, next-gen automation testing tool that allows Agile and DevOps teams to rapidly achieve test automation goals. It allows teams to go beyond continuous testing with AI. It allows automating end-to-end testing of software applications. This tool combines multiple aspects of software testing (test case design, test automation, test data design and generation, and analytics) to test GUIs and APIs from a business perspective.

2. UiPath Test Suite:

This is the latest Test Suite that can be used to automate and centralize the testing process and helps to launch resilient robots and ensures high-quality of every automation. The UiPath Test Suite consists of UiPath Studio Pro, UiPath Test Manager, and UiPath Orchestrator. Thus, UiPath test Suite can be used to automate tests in UiPath Studio Pro with drag and drop interfaces, helps to manage tests with UiPath Test Manager, and also helps to execute tests witn UiPath Orchestrator. Therefore, UiPath Test Suite is helping businesses with a 360 degree testing and is helping RPA developers to build more, test better, and fix never.

Self-healing tools

In automation tests, the problem of flakiness, reliability, and maintenance issues persist, and this is one of the main reasons why AI and ML have been introduced in test automation. To overcome these problems, self-healing tools have been developed that are mostly based on a record and playback mechanism, wherein the main ML engine resides in the self-healing of the recorded scripts. Some of the tools that fall under this category are:

1. Mabl:

It is the leading intelligent test automation platform built for CI/CD. Mabl crawls your app screens and begins to run default tests that are common for most applications. It also uses ML algorithms to improve test execution and defect detection. 

2. Testim:

This tool uses AI and ML algorithms to automate testing to its full extent. AI is used to speed up the authoring, execution, and maintenance of the tests. Testim includes self-maintenance of automated tests that are ML-based. This results in the fast authoring of stable automated tests.

Conclusion

Today as technology is advancing, more and more businesses are adopting agile and DevOps practices. But, with the adoption of these methodologies, the need for robust tools that support continuous testing and continuous release are also rising. This is where AI-based test automation tools come into the picture. With the advent of AI in software testing, businesses are now able to achieve faster tests and reliable products.

AI-driven test automation tools not only support DevOps practices but also bring in human-like decision-making abilities which ultimately helps in releasing high-quality software in less time. Leverage next-gen AI-based testing services by next-gen testing services provider to get faster and quality releases with more efficiency and accuracy.

The post AI in Software Testing – Benefits, Approaches, Tools to Look in 2023 first appeared on TestingXperts.

Software testing industry is becoming extensive with each passing day. Applications these days have started interacting seamlessly with other applications through APIs. With the sudden increase in the technology challenges, applications are growing in complexity, which creates an incessant need for effective software testing.

Contents 1. Importance of Artificial Intelligence 2. Why do we need Artificial Intelligence in Software Testing? 3. How Artificial Intelligence benefits software testers? 4. What are the AI tools for software testing? 5. In Conclusion

Software testing is the only premeditated way where an application can be observed under certain conditions and where testers can recognize the risks involved in the software implementation. Testing, on the other hand, is gradually transitioning to greater automation to ensure maximum precision and accuracy in the journey towards digital transformation. In an attempt to make the application foolproof, the world is turning towards Artificial Intelligence (AI). This implies that instead of manual testing and human intervention, we are moving towards a situation where machines will be slowly taking over.

Importance of Artificial Intelligence

Simply put, Artificial intelligence (AI)is the science behind computers performing tasks that are traditionally performed by humans. AI provides machines with the skill to process information about its conditions and learns to adapt to the transformations with problem solving techniques. Machines are learning faster than ever given the technology advancements. The computer is fed with a huge amount of data to adapt as per a set of inputs so that it can identify patterns and logic and as a result make an effective connection between similar input and output pairs. It is through machine learning that websites like Amazon and Netflix are able to deliver targeted ads to their customers by using huge amounts of data based on the web pages the customer searches. Personal assistants like Google Assistant, Alex, and others use AI technology extensively to deliver results.

Machine Learning has come a long way with people using it to drive cars without a human. Artificial Intelligence (AI) has become quite famous in the field of healthcare also as it is used to identify cancerous tumors. As Artificial Intelligence begins to progress, technology experts across the globe are finding ways to leverage its potential in the software testing field as well. It is only a matter of sometimes when training applications to be familiar with problems for agile and more efficient testing becomes a reality.

Why do we need Artificial Intelligence in Software Testing?

Software testing is usually a time-consuming process. Artificial Intelligence ai is the best choice for developers looking for faster deployments with insufficient infrastructure. It is best to leave the arduous work to the Artificial Intelligence-powered automation which leaves only 20% of the testing work to human ingenious and cognitive ability. It would ensure more fail-safe results as hand-crafted testing does not only require extensive human hours but is also susceptible to imprecisions and discrepancies.

How Artificial Intelligence benefits software testers?

Reduces the test-cycle time:

With the AI methodology, it is easier for a tester to understand which test cases are required to be executed in each test cycle. AI is efficient in gathering data from the previous test executions made for the application. This helps AI to easily identify when any changes are made in the code. Also, it automatically states the test suites as per the application risk. The application risks are defined by considering several factors such as the most used parts of the application, tests having the risk of failure or complexity, etc. This way of finding defects at earliest and running the test cases that can get affected at the start of a test cycle helps to receive faster feedback from delivery teams and ensures to find solutions when any defects are identified.

Helps to find bugs faster:

Unlike the traditional test methods, the AI methodology is an expert to find the defects faster in applications. In the previous methods, error detection was at the later period and this resulted in several drawbacks for the app before its launch. AI helps in automating the processes, it automatically corrects the code and assigns bug tracking to the QA teams. This effectively benefits the teams to track the errors at the earliest and achieve a bug-free app before production.

Encourages Script Execution:

There is no need to perform a test script automation when using AI because test scripts are executed automatically by the AI. Also, it enhances test cases to be more stable so that there won’t be any change experienced when the locators get changed.

Improves the speed of Test Coverage:

AI is efficient for increasing the scope and depth of the test cases. AI monitors the memory, file contents, internal programs, and data tables of the app to verify the functionality is as needed or not. Also, with the use of the AI-powered test automation tools, thousands of test cases can be executed in one test run. This ability of AI effectively helps in speeding up the test coverage.

Reduces Costs Requirements:

Bugs or the errors in the application can become expensive when they are not resolved at the earliest. With the help of AI, instant feedback can help to find and resolve the errors at the earliest and this significantly helps in cost savings. In addition to this, AI is even more efficient to sort files, detect errors and scan code at a lesser time. This reduces the effort of manual work and human errors and thus, yields more accurate results at a lesser cost. 

Improves Accuracy:

A well-experienced tester can even make mistakes while carrying out repetitive tasks in manual testing. This is the reason automation came into effect to reduce the scope of repetitive tasks. When automation is powered with AI, the repetitive tasks are performed by the AI and the testing teams are provided with the scope to perform exploratory activities and find automation solutions. Thus, the time consumption is reduced and the accuracy for achieving quality results increases. 

What are the AI tools for software testing?

– Functionize

– EggPlant AI

– AppVance

– Testim

– Applitools

– TestCraft

– Test.ai

In Conclusion

Digital Transformation is becoming impossible without the implementation of new and emerging technologies. The consumer market has become dynamic, organizations that do not leverage these technologies will end up way behind in the race. Businesses need to experiment and innovate to make their product success with the end-user. This can only be possible when these technologies are tested extensively against the odds and under all conditions.

TestingXperts brings machine learning together with analytics to unleash the power of software testing and drive automation and innovation, consequently improving QA efficiencies. Our intelligent frameworks are based on industry best practices and standards, to efficiently evaluate clients’ testing maturity. Connect with our experts to understand and improve all QA focus areas across the delivery lifecycle.

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