An Overview of UiPath FORWORD VI

The FORWARD VI conference is an exciting platform where industry pioneers meet to discuss the concepts and tactics influencing the direction of automation. It is an opportunity for the business executives to learn about the UiPath vision for the future of AI and how automation powered by AI is transforming industries.

The conference promises to offer deep insights into the latest trends, cutting-edge technologies, and real-world applications of RPA. Attendees get the opportunity to engage with visionary speakers, participate in interactive sessions, witness live demonstrations, and connect with like-minded professionals, all contributing to a vibrant atmosphere of learning and growth. It’s a gathering of automation aficionados, a celebration of innovation that shares UiPath’s commitment to driving the RPA revolution forward.

UiPath Test Suite and its significance in the world of RPA and test automation

UiPath Test Suite is a transformative tool in the sphere of Robotic Process Automation (RPA) and test automation. It represents a powerhouse of tools designed to elevate the testing process within the RPA landscape. This suite offers a unified platform for orchestrating, managing, and executing end-to-end testing seamlessly integrated with RPA workflows.

Its significance lies in its ability to deliver comprehensive testing capabilities, ensuring RPA solutions operate flawlessly across diverse scenarios. From functional validation to regression testing, UiPath Test Suite empowers developers and testers to scrutinize every facet of automation, driving efficiency, reliability, and precision. By promoting reusability and collaboration while providing robust reporting and analytics, it not only optimizes the testing workflow but also paves the way for a new era of accelerated, high-quality RPA implementations. UiPath Test Suite speaks of UiPath’s commitment to excellence, assisting in a future where RPA operates at the pinnacle of efficiency and dependability.

RPA and its Importance in Modern Software Testing

Robotic Process Automation (RPA) stands at the forefront of a technological revolution, redefining how we approach modern software testing. Imagine software robots mimicking human actions to navigate applications, execute repetitive tasks, and validate functionalities seamlessly.

RPA has emerged as a revolutionary, allowing testing teams to automate monotonous, rule-based tasks, and significantly enhance efficiency, accuracy, and speed in the testing process. By automating routine tasks, testers can shift their focus to higher-value activities like crafting comprehensive test strategies, identifying critical issues, and ensuring a superior user experience. Thus, RPA’s importance in modern software testing cannot be overstated—it’s the catalyst for a shift towards smarter, more efficient testing methodologies, ultimately delivering superior software products to users worldwide.

The Power of Integration: RPA and UiPath Test Suite

The integration of RPA and UiPath Test Suite represents a revolutionary leap in the world of test automation, elevating efficiency, precision, and scalability to unprecedented levels. When RPAs mimick human actions and automate repetitive tasks seamlessly merged with the rich testing capabilities of UiPath Test Suite, the result is dynamic. It can execute end-to-end testing with unmatched finesse. RPA, with its ability to replicate human interactions, ensures a true simulation of user behavior, while the comprehensive functionalities of UiPath Test Suite enable rigorous testing across diverse scenarios and platforms. This symbiosis allows for not only accelerated test case execution but also the validation of complex business processes, delivering a comprehensive and robust approach to software testing.

Furthermore, this integration optimizes the entire testing workflow by focusing on reusability and collaboration. Automation scripts and test cases created within UiPath Test Suite can be efficiently integrated into RPA workflows, saving time and effort by avoiding redundant work. The collaborative environment ensures seamless communication and coordination between development and testing teams, promoting an agile and iterative approach. Additionally, the consolidated reporting and analytics capabilities provide clear insights into testing results, enabling informed decision-making and continuous improvement. The amalgamation of RPA and UiPath Test Suite is the epitome of a synergy that not only amplifies the efficiency of test automation processes but also sets new standards for testing precision and excellence.

How UiPath’s features and capabilities are suitable for implementing RPA-based test automation

UiPath, as a leading RPA (Robotic Process Automation) platform, offers a robust set of features and capabilities that make it highly suitable for implementing RPA-based test automation. Here’s a detailed discussion on how UiPath’s offerings align with and enhance RPA in the context of test automation:

Intuitive Interface and Ease of Use: UiPath provides an intuitive and user-friendly interface, enabling testers and automation engineers to easily design, develop, and maintain automated test cases. Its low-code/no-code capabilities ensure that both technical and non-technical users can create automation scripts efficiently.

Advanced AI and ML Capabilities: UiPath incorporates AI (Artificial Intelligence) and ML (Machine Learning) capabilities, which can be leveraged for smarter test automation. AI-powered features can assist in dynamic element identification, data validation, and predictive analysis, enhancing the overall test automation process.

Versatile Automation: UiPath is highly versatile and supports automation across a wide array of applications, technologies, and platforms. This flexibility is crucial in the context of test automation, where applications may use diverse technologies, and the automation tool needs to adapt accordingly.

Scalability and Performance: UiPath offers scalability, enabling the automation of a large number of test cases concurrently. This scalability is crucial for handling extensive test suites and ensuring timely execution of tests.

Robust Integration Capabilities: UiPath offers seamless integration with various testing tools, frameworks, and CI/CD (Continuous Integration/Continuous Deployment) pipelines. This allows for a smooth integration of RPA-based test automation into the existing testing ecosystem, enhancing efficiency and collaboration.

TestingXperts: A Key Player in RPA-Based Test Automation

TestingXperts has been accorded the recognition of a “Leader” in NelsonHall’s NEAT Vendor Evaluation for Quality Engineering in 2023, specifically in the domain of RPA-based test automation. With a sharp focus on RPA-based test automation, TestingXperts empowers its clients to accelerate their time-to-market, significantly lower testing costs, and elevate software quality.

UiPath Test Suite combines leading RPA technology with state-of-the-art test automation capabilities to deliver:

• A single unified enterprise automation platform to achieve higher automation rates and faster time to market

• A test automation solution built on the familiar UiPath paradigm – Intelligent and comprehensive reporting and analytics

• A low-code test automation platform that does not require testers with deep coding skills

• A solution that leverages licensing investment across RPA and test

• The easy development of end-to-end tests across applications

• Ability to enable risk-based testing when combined with UiPath Process Mining

• Ability to develop and implement automation from a holistic perspective: Robotic Process Automation + Test Automation = Automation

• Significant artifact reuse – what is developed for testing can be reused for RPA thereby turning QA into a profit center

TestingXperts’ UiPath Test Suite Test Centre of Excellence (TCoE)

TestingXperts, Gold and USN certified partner of UiPath has developed Test Centre of Excellence in collaboration with UiPath. Our shared goal of improving test automation for a quicker time to market and more business value is the foundation of our endeavor. We’ve created assets and accelerators within the TCoE that strengthen UiPath Test Suite’s integration with our client’s environment. These accelerators are made to simplify the implementation procedure and guarantee a perfect fit with the client’s environment. For our clients, the CoE has been a pillar in allowing effective, dependable, and agile automation operations.

Our collaboration with UiPath has already yielded impressive results, showcasing the expertise of this cutting-edge technology. By leveraging UiPath’s RPA capabilities, TestingXperts has successfully enhanced automation initiatives for clients, resulting in up to 70% faster time-to-market and up to 96% reduced regression test coverage. The implementation of UiPath technology has propelled our clients towards a future where efficiency, accuracy, and innovation converge harmoniously.

How TestingXperts create a difference with its extensive UiPath capabilities?

Here are a few differentiators to set Tx apart:

• Early adopter and preeminent partner for Test Suite

• NextGen QA Company with statistical approach to AI/ RPAtesting

• DevOps ready framework using UiPath Test Suite to accelerate deployment

• Working closely with UiPath Test Suite team to create out of the box solutions for automation using UiPath

• 100+ trained/certified employees

What TestingXperts is Planning to Deliver at UiPath FORWARD VI?

As a proud participant, TestingXperts is primed to showcase its deep understanding and proficiency in leveraging UiPath’s transformative RPA solutions. Engage with our CEO, Manish Gupta, COO, Joe Underwood, as they share invaluable insights on how the amalgamation of AI and Automation can reshape businesses.

• Explore our enriching discussions, focusing on the latest trends, challenges, and opportunities in the RPA landscape.

• Witness firsthand how TestingXperts, in partnership with UiPath, is revolutionizing test automation through our Tx-UiPath Test Suite Center of Excellence (CoE).

• Discover the future of automation and how we can empower your organization to innovate and excel using the power of RPA and AI.

Don’t miss this exciting opportunity to connect, learn, and envision a future where automation is not just a tool, but a strategic imperative. TestingXperts is excitedly looking forward to meeting you there.

The post Explore the Capabilities of UiPath Test Suite at UiPath FORWARD VI first appeared on TestingXperts.

Artificial Intelligence (AI) was just a buzzword a few years back, but now it has become an indispensable part of human lives. AI is present almost everywhere, from smartphones, smart devices (Alexa, Siri, etc.), smart apps (Cortana, Google Assistant, etc.) to self-driving cars. With AI used across all fields such as Healthcare, Banking, FinTech, Insurance, etc., undoubtedly, this technology has its demanding presence even in the software testing field, which is on the rise today. Next-Gen technology is used in test automation to make the testing process smoother, faster, and more reliable.

Content 1. An overview of AI-based software testing 2. How can AI help overcome test automation challenges? 3. How is AI revolutionizing software test automation? 4. AI-based software test automation methods 5. Leading AI tools for automation testing 6. Conclusion

An overview of AI-based software testing

The software testing market has slowly changed from the initial manual testing to semi-automation, and then towards automation testing using tools. Further codeless automation, automation using bots leveraging AI & ML technologies, and specifically AI-based software test automation is in more demand in recent years.

AI-based software testing is an advanced software testing technique in which next-gen technologies like AI, Machine Learning (ML), and Deep Learning (DL) are used to test the software effectively. AI and ML apply problem-solving and reasoning techniques to automate and improve the testing process. AI-based testing can be performed effectively by leveraging tools that use data and algorithms to design and perform the tests without any human intervention. This advanced testing ensures better test coverage, test accuracy, faster time to market, and provides a significant reduction in overall testing costs.

AI is embedded into automated software testing processes making tests smarter, more reliable, and efficient. However, businesses continue to face many challenges associated with conventional automation testing methods and specifically, AI can be leveraged to overcome such challenges.

How can AI help overcome test automation challenges?

1. Finding the right set of people:

One major challenge associated with automation testing is finding the right team with the right skill set for scripting.

Solution: Businesses can leverage AI-based test automation tools that provide testers with a semi or fully codeless scripting platform, and can thus overcome the challenge of finding the right team and skill set.

2. Time consumed in performing repetitive tasks:

Every time a new test automation project comes up, no matter how reusable the components are, teams end up writing a lot of similar code again, which consumes a lot of time.

Solution: To overcome this challenge, AI can be utilized to generate test scripts quickly and automatically. AI tools can be trained based on the previous project inputs and outputs to create test scripts automatically for similar projects.

3. Flaky tests:

Testing teams spend hours determining if the failed test is due to application issues or poorly written test cases. These sorts of test failures are known as flaky tests, due to which a release is unnecessarily kept on hold, resulting in the delay of the software delivery.

Solution: AI can help teams overcome the challenge of flaky tests by creating more robust test cases and finding patterns in random test failures to speed up the process.

4. Frequent script updates due to UI changes:

In order to provide a seamless User Experience (UX), businesses tend to modify the app User Interface (UI). Even though the change may be minor or unnoticeable, it could fail the test scripts while performing some actions on the page.

Solution: AI and ML algorithm-based tools can be trained to determine minor changes in the code or bugs in the application. These tools can then take further actions accordingly and save human efforts in script updates for such minor changes.

5. Determining appropriate test size and test cases:

Every time a change is made in the application, regression tests are to be conducted. But, in a fast-paced environment, it is logically not feasible to run the entire regression suite of test cases for every change. Thus, determining the right size and set of test cases to be run for a particular change has always been challenging for the testing teams.

Solution: AI can help testers build algorithms that predict the right size and set the regression test suite for the given change based on different parameters.

6. Maintaining the test suites/test scripts:

As the size of an application becomes larger, it becomes challenging to maintain a large number of test scripts.

Solution: AI can be used to overcome this challenge as AI tools can be used to maintain and extract the required test scripts as per testing requirements.

Thus, AI helps overcome the challenges of traditional test automation and is said to bring a revolution in the field of test automation.

How is AI revolutionizing software test automation?

1. Helps automate the unit testing process:

AI can be used for static analysis of the software to decide which code area is not covered under unit tests. The AI-based software testing tools can use this information to generate unit tests for uncovered code. These tools can generate unit tests and even update the tests whenever the source code changes.

2. Enables visual validation of UI testing process:

AI can be used for UI testing as it involves the usage of image recognition techniques to navigate through the application. It can verify UI objects and elements visually to create UI tests. AI tools also use exploratory testing techniques to find bugs in the application’s UI. The AI tool generates the screenshot for later verification by QA engineers. It also verifies the other visual aspects of the system under test like color, size, layout, etc.

3. Eases regression testing significantly:

The AI-powered tools and AI-powered engines help to create tests faster, run thousands of tests in one go, and significantly reduce overall test maintenance. These tests work seamlessly across browsers and even mobile browsers.

4. Supports self-healing:

Many self-healing automation tools use AI and ML to automatically update and adjust changes in the UI or environment of the application. Typically, the AI tools detect the issues and remediate them without any human intervention through the self-healing method.

5. Delivers early defect predictions:

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

6. Smoothens the continuous testing process:

AI smoothens the continuous testing process by eliminating the chances of manual intervention. It can detect issues or defects in the testing process and trigger remedial measures to ensure continuity of the testing process. This ensures that only the bug-free code progresses from one stage to the next stage and helps to improve the quality across the software development lifecycle.

7. Ensures seamless execution of API testing process:

AI algorithms understand the patterns and relationships in different API calls and group them effectively based on the scenario. It understands the existing tests to learn about the relationship between APIs, and then uses this information to understand changes in APIs and finally creates new scenario-based tests.

8. Improves the security testing process:

AI can improve the security testing process by quickly identifying the cyber-security issues present in the software. It can extract information from the previously recorded data and analyze the system’s weaknesses in real-time. This way, AI can help businesses strengthen the security and privacy of their systems, application, data, and networks. It also allows testers to create more robust penetration tests.

Thus, it is evident that AI is used in all spheres of the automation testing process and ensures faster testing outcomes more efficiently without any human intervention.

AI-based software test automation methods

AI-based testing:

This testing method is an advanced software testing technique in which AI, ML, and DL technologies are integrated with the automation testing process to improve the software’s quality and speed up software delivery. Some of the common methods are:

Regression suite automation:

Regression testing consumes a lot of time and the tester’s efforts. AI-enabled regression suite automation intelligently runs automated tests based on the changes in the code each time. The main aim of this test is to reduce the regression test cycle time by identifying and executing the right set of test cases that must be executed.

Defect analysis and prediction:

It involves the usage of ML and NLP algorithms that helps with the effective identification of software defects. The main aim of this test is to ensure the early identification of defects and help businesses go to market early.

Self-healing automation:

AI-enabled self-healing process involves automatic healing of test automation script breakages that could occur due to object or other property changes. The main aim of this test is to ensure lesser manual intervention and a faster self-healing process.

Leading AI tools for automation testing

UiPath Test Suite:

UiPath Test Suite combines the world’s leading Robotic Process Automation (RPA) technology with best-of-breed testing capabilities to accelerate scaling through proactive testing, cross-enterprise collaboration, and a consistent approach to create and deploy automation. UiPath Test Suite benefits practitioners who create automation and stakeholders who depend on automation, including transformation leaders, business users, customers, and partners. UiPath Test Suite consists of Test Manager, Studio Pro, Orchestrator, and Robots, and other Test Studio Components. Moreover, it fosters collaboration through reusable artifacts and has a user-friendly interface.

Sofy.Ai:

It is an AI-powered, no-code quality automation platform that leverages the power of AI to test mobile apps on a variety of android and iOS devices. It can be used to perform manual, automated, and RPA testing of apps. Uniquely, this no-code testing platform simplifies testing, saves testing time, increases productivity, and shortens the testing life cycle. This intelligent automation testing tool simplifies manual, automated, and robotic testing in a significant way.

Tricentis Tosca:

It is an enterprise software testing tool used for end-to-end automation testing of software applications. This tool comes with amazing features like Vision AI, model-based test automation, risk-based test optimization, etc. The Vision AI feature of this tool leverages the power of AI to automate and customize tests. It uses deep convolutional neural networks combined with advanced heuristics to deliver stable, self-healing, platform-agnostic UI automation. Vision AI, together with Tricentis Tosca, can be used to automate even the hardest-to-access UI systems.

Appvance IQ:

It is an AI-driven autonomous continuous testing system that can be used for test creation and execution. This tool allows the generation of autonomous tests and codeless test creation and can be used to execute functional, performance, and security testing. This tool supports continuous testing in a DevOps environment and can be easily integrated with Jenkins, TeamCity, CircleCI, Jira, and other CI/CD tools.

Functionize:

It is a cloud-based automation testing tool that is used for functional and non-functional testing (load and performance testing). This tool uses AI and ML algorithms to speed up test creation and maintenance. This AI tool uses a plain English language and NLP to create a functional test. Also, this tool can execute thousands of tests in a minute from all desktops and mobile browsers.

Parasoft:

It is an automated testing tool that can be used to automate the tests at every stage of the software development cycle, starting from code analysis to UI Testing. It is a test suite with various components, and it has recently introduced AI and ML into software test automation to help organizations adopt static analysis.  Parasoft’s AI solution can be used to detect defects early and to ensure compliance with a variety of functional safety, security, and coding standards. It reduces the burden of teams and ensures serious design issues are addressed first.

Applitools:

It is an AI-powered visual testing and monitoring platform that is easy to set up and integrates with all modern test frameworks. This tool supports all major browser versions and allows to check the functionality and appearance of the app on various devices like mobile phones, tablets, laptops, or external monitors.

Conclusion

AI in software test automation has become an important trend and has a tangible reality as AI holds the potential to take software testing to the next level. Undoubtedly it helps testers to generate more tests and ensures the speed and reliability of automated tests. But integrating AI into software test automation needs professional assistance as it is a complex process. Businesses can leverage AI testing from a Next-Gen QA and independent software testing services provider for high-quality software and faster time-to-market.

The post How is AI Revolutionizing Software Test Automation? first appeared on TestingXperts.

Content 1.  AI – Its Significance in Healthcare 2.  Benefits of using AI in the Healthcare Domain 3.  Some of the prominent AI Healthcare applications 4.  9 Significant use cases of AI in healthcare” 5.  Conclusion

AI – Its Significance in Healthcare

Artificial Intelligence (AI), Machine Learning (ML), and its related technologies are today used in various business applications across industries of healthcare, pharma, automobile, banking, finance, insurance, etc. These AI applications not only deliver great results for businesses by simplifying business processes but also improves the human experience as a whole. Specifically, though AI is being leveraged across many industries, its dominance is more prominent in the healthcare sector.

Since the pandemic, the healthcare sector has been expanding in leaps and bounds with more demand for quality healthcare. Undoubtedly, though AI is touching every sphere of human life, the way it is transforming healthcare is truly phenomenal. Analysts believe that AI is poised to become a transformational force in the coming years for the healthcare domain.

According to MarketsAndMarkets, “The global AI in healthcare market size is expected to grow from USD 4.9B in 2020 and reach USD 45.2B by 2026 and is projected to grow at a CAGR of 44.9% during the forecast period.” The major factors driving the AI in healthcare market growth are the increasing volume of healthcare data, growing complexities of datasets, the intensifying need to reduce towering healthcare costs, the eventual need to deliver better patient outcomes, etc.

According to reports by Frost & Sullivan, the healthcare AI market is likely to experience a CAGR of 40% by 2021, and it has the potential to change healthcare outcomes by 30-40% and cut treatment costs in half.

As outlined by many leading research analysts, there has been more complexity and rise of data in healthcare which essentially demands the use of AI within this field. There are various ways in which AI is being employed by different healthcare providers and various life science companies which have been cited vividly in an article on the topic ‘Potential for Artificial Intelligence in Healthcare’ published by Future Healthcare Journal. Many of these publications state that the usage of AI in healthcare has been increasing in recent times and also state many advantages of leveraging AI in healthcare.

Benefits of using AI in the Healthcare Domain

1. Better patient outcomes:

Leveraging AI helps to bring-in better treatment methods and helps improve patient outcomes and their survival rates. There are various AI applications used to detect and analyze huge volumes of data and make effective predictions through ML algorithms. These predictions continue to better the patient health predictions with the AI’s new computing power. Typically, all these predictions have been helping healthcare providers to deliver better patient outcomes.

2. Simplify healthcare with better data-driven decisions:

ML algorithms have the potential to provide data-driven clinical support decisions to both the doctors, physicians, and also to hospital staff. Evidently, this continues to simplify healthcare as AI uses algorithms and data to give automated insights that can be best used by healthcare providers to derive real-time decisions.

3. Reduce healthcare costs:

It has been stated that AI in healthcare helps identify inefficiencies within the systems and streamlines administrative processes and ensures healthcare cost reduction.

4. Screen diagnostic reports on par with radiologists:

Some of the AI imaging tools can be used to screen chest x-rays for signs of tuberculosis and they provide more accurate readings. Moreover, in places with low resource areas, many AI tools can be deployed to reduce the need for trained diagnostic radiologists and can be used to screen patient radiology reports.

5. Deliver customized treatment plans:

By leveraging AI & ML algorithms, doctors can deliver customized treatment plans after a thorough study of the AI patterns being produced. These patterns help deliver customized treatments based on analyzing various patterns of similar patient’s data

6. Reduce mortality rate:

There are certain AI applications being used by healthcare providers that have real-world use cases. These real-world use cases greatly help to make the diagnosis, treatment, and monitoring of patients more accurate, reliable, and also help reduce the patient mortality rates in certain cases

7. Enhance primary care and triage with chatbots:

Patients can use AI-powered chatbots to avoid trips to their doctor, as these bots provide instant answers to all patient health-related queries. These bots guide them with minimal medical instructions. These chatbots are available 24×7 and can deal with multiple patients at the same time

8. Reduce re-admission rates drastically:

AI tools are being leveraged to monitor respiratory rate, oxygen levels, pulse, blood pressure, and body temperature of patients. Eventually, it is helping to reduce the re-admission of patients effectively. According to a report, the readmission of patients cost US hospitals $40 billion annually. The Grady Hospital, the largest public hospital in Atlanta reduced readmission rates by 31% over a period of two years by adopting AI tools

Some of the prominent AI Healthcare applications

9 Significant use cases of AI in healthcare

1. Derive new treatments based on patterns:

Many AI apps are being used for early disease diagnosis, customized treatment protocol development, etc. Some of the AI-based apps analyze the relationships between treatment techniques used and the patient outcomes and help to derive new treatment processes by analyzing old patterns.

2. Cure neurological disorders:

Neurological diseases and trauma to the nervous system can take away some of the patient’s abilities such as speech, movement, and even patient’s interaction with their surrounding environments. Specifically, to treat such conditions, AI-based Brain Computer Interfaces (BCIs) are found helpful to cure such issues.

3. Discover new drugs:

Many healthcare and pharma companies are leveraging AI to help with the discovery of new drugs. AI solutions help to discover and improve the earlier lengthy timelines and processes that were used with any new drug discovery and in a way helping to get new drugs to the market faster.

4. Identify high-risk patients early:

By analyzing vast amounts of historic patient data, AI-based solutions can provide real-time support to doctors and clinicians to help identify high-risk patients faster than before and help to save them with better medical care.

5. Deliver better radiology predictions:

Radiological images obtained by MRI machines, CT scanners, and x-rays offer non-invasive visibility into the inner working patterns of the human body. There are various deep learning platforms that are being leveraged by healthcare doctors to analyze unstructured medical data of blood samples, radiology images, etc. and these can give doctors better insights into a patient’s real-time needs directly by using various AI tools.

6. Simplify Electronic Health Records (EHRs):

Leveraging AI in EHRs helps to create more intuitive user interfaces that simplify workflows. Especially, some of the AI solutions are being used to automate patient data entry routine processes that usually consume a lot of human effort and time, thus easing the overall process and is helping to simplify EHRs.

7. Prioritize clinician’s tasks:

AI applications are also helping to process routine requests from the inbox, like medication refills and various other result notifications.  These AI apps also help to prioritize tasks that truly require the clinician’s attention, said Adam Landman, MD, Vice President, and CIO at Brigham Health, making it easier for users to work through their to-do lists.

8. Improvise cancer treatment:

There are many types of cancer being identified early by leveraging various AI apps. AI apps can spot subtle patterns that are likely to be missed by humans and helps guide through better cancer diagnosis of different stages of cancer and the probable risk in some patients.

9. Detect harmful bacteria in blood:

AI-enhanced microscopes are being used to scan for harmful bacteria in patient’s blood samples. It becomes easy to identify and predict harmful bacteria in the blood with up to 95% accuracy by using these AI-based microscopes.

Conclusion

AI is the technology that is being leveraged by every industry segment to enhance their business outcomes and also to deliver a better human experience. The impact of AI in healthcare has been much more and wider than when compared to other industry domains.

Today, AI applications and especially bots are used in various treatment plans, diagnose diseases faster, protect patients from diseases, help them with customized treatment plans, etc. AI-powered solutions are predicted to play a leading role in how healthcare systems of the future operate and augment clinical resources.

These AI apps are likely to influence patient outcomes greatly in the coming years. But, the healthcare providers need to make sure their AI-enabled apps work seamlessly and continue to deliver a great patient experience. Hence, to get fully functional AI apps, and chatbots, leverage AI-based testing by next-gen testing service providers to get the desired high-performing AI apps.

The post Role of AI in The Healthcare Sector first appeared on TestingXperts.

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.