1. The Role of AI in Quality Assurance
2. Challenges of Implementing AI in Quality Assurance
3. Benefits of AI-Led Quality Assurance
4. AI Testing Tools for Quality Assurance
5. Conclusion
6. How can TestingXperts help transform QA using AI

Artificial Intelligence (AI) is drawing substantial financial investments and genuine enthusiasm from prominent entrepreneurs because of its promising advantages. Companies are eagerly adopting AI in quality assurance to streamline their operations and lower expenses. AI facilitates the efficient sharing of data, forecasts customer behavior, offers product recommendations, identifies fraudulent activities, tailors marketing to specific demographics, and delivers valuable customer support.

If you’re still uncertain about the practicality of AI, here are some astonishing statistics:

• 63% of companies acknowledge that the pressure to cut costs will drive them to adopt AI.

• 61% of business leaders assert that AI assists them in identifying business opportunities they might otherwise overlook.

The Role of AI in Quality Assurance

To evaluate software and its functionalities, it is essential to develop a test suite and generate test data. Nevertheless, this procedure is time-consuming. On average, when employing manual testing methods, it accounts for more than one-third (approximately 35%) of the total time expended during a testing cycle.

In addition to this, manual testing is susceptible to human errors, which can lead to increased expenses and consume more time and resources – something no business is eager to invest in.

Additionally, as software becomes more intricate, the number of tests naturally grows, rendering it progressively harder to maintain a test suite and ensure sufficient code coverage.

In such situations, leveraging artificial intelligence for quality control can expedite the mobile app testing procedure and surmount all the aforementioned challenges. The key areas where AI enhances testing processes in quality assurance encompass:

• Comprehensive supervision of API testing.

• Automation of testing operations.

• Determining the optimal timing and methodology for executing a test script.

• Utilizing AI-based spidering tools for more effective data analysis.

Here are a few limitations of manual testing and How AI can change that:

Challenges of Implementing AI in Quality Assurance

Implementing AI in Quality Assurance (QA) introduces a range of complex challenges that organizations must address to ensure the successful integration of artificial intelligence into their quality control processes. These challenges include:

Data Quality:

QA teams must grapple with the need for high-quality training data. Ensuring that the data used to train AI models is accurate, representative, and free from biases is a crucial initial step.

Integration Complexity:

Integrating AI into existing QA systems can be a daunting task. Ensuring compatibility, data flow, and proper synchronization with legacy processes is essential for a smooth transition.

Regulatory Compliance:

Depending on the industry, there might be specific regulations and data privacy laws that must be adhered to when implementing AI in QA. Ensuring compliance with these regulations is a critical concern.

User Acceptance:

Convincing QA teams and stakeholders of the benefits and reliability of AI-driven processes is essential. Education, training, and effective communication are required to gain trust and acceptance.

Benefits of AI-Led Quality Assurance

The utilization of Artificial Intelligence (AI) in software testing brings forth distinctive advantages that can significantly transform the quality assurance process. Let’s explore these benefits in detail, particularly focusing on how AI contributes in transforming Quality Assurance:

Speedy Timelines:

Efficient Test Execution:

AI-driven test automation can execute a vast number of test cases at unprecedented speeds, significantly reducing the time required for testing. Tasks that might take human testers days or weeks can be accomplished in a matter of hours.

Faster Regression Testing:

AI streamlines regression testing by automatically re-running test cases to check for unintended side effects. This ensures that new code changes don’t break existing functionality, enabling faster release cycles.

Well-Researched Build Releases:

Predictive Analysis:

AI can analyze historical testing data and software performance metrics to predict potential defects. By identifying areas of the software that are more likely to contain issues, teams can focus their testing efforts on high-risk areas, leading to more well-informed build releases.

Continuous Monitoring:

Real-time monitoring by AI can detect anomalies and performance issues in production, helping in pinpointing areas that require immediate attention before a new build release.

Going Beyond the Limits of Manual Testing:

Comprehensive Test Coverage:

AI can perform extensive and exhaustive testing, covering a multitude of scenarios and edge cases that manual testing may overlook. This thorough test coverage ensures that software is rigorously examined.

Scalability:

AI-powered testing is highly scalable, accommodating the increasing complexity and size of modern software projects. It can adapt to new technologies, larger datasets, and evolving testing requirements, something that manual testing often struggles to achieve.

Effortless Test Planning:

Intelligent Test Case Generation:

AI algorithms can autonomously generate test cases, making the test planning process more efficient. These generated test cases often cover critical paths, boundary conditions, and high-impact scenarios, saving time and effort in test case creation.

Test Prioritization:

AI can prioritize test cases based on their criticality and potential impact, simplifying test planning by ensuring that the most important tests receive primary attention.

AI Testing Tools for Quality Assurance

AI testing tools for quality assurance have gained popularity in recent years for enhancing testing efficiency and accuracy.

Applitools:

• Applitools offers Visual AI testing, a technology that uses AI and machine learning to automatically detect and analyze visual differences in the user interface of applications. It’s particularly useful for web and mobile app testing.

• It can identify visual defects such as layout issues, rendering problems, and functional bugs.

• Applitools provides a visual validation platform that integrates with various test automation frameworks and tools, making it easy to add visual testing to existing test suites.

Test Sigma:

• Test Sigma is an AI-driven test automation platform that aims to simplify test creation and maintenance.

• It uses natural language processing and AI to enable testers and non-technical users to create automated test scripts.

• The platform offers features like self-healing tests, easy maintenance, and cross-browser testing.

SauceLabs:

• Sauce Labs is a cloud-based platform for automated testing, including both web and mobile application testing.

•While it’s not exclusively an AI testing tool, it offers AI-powered testing capabilities to improve test execution and analysis.

•It provides a wide range of real devices and virtual environments for running tests and supports various testing frameworks.

TestCraft:

• TestCraft is a cloud-based test automation platform that uses AI to generate and maintain test scripts.

• Its AI engine identifies changes in the application under test and adjusts test scripts accordingly to ensure they remain functional.

• TestCraft supports web and mobile app testing and offers integration with various CI/CD tools.

These tools leverage AI and machine learning to enhance the efficiency and accuracy of quality assurance processes. When choosing the right tool for your specific needs, consider factors such as the type of applications you’re testing (web, mobile, desktop), the level of technical expertise in your team, and the integration capabilities with your existing testing framework or tools. It’s also essential to evaluate factors like cost, scalability, and support to determine the best fit for your organization’s requirements.

Conclusion

The integration of Artificial Intelligence into Quality Assurance is ushering in a new era of efficiency, accuracy, and effectiveness in software testing and product quality control. AI-driven solutions are revolutionizing the way businesses approach quality assurance, from automating repetitive and time-consuming testing tasks to enabling predictive defect identification and improving performance and security testing. With AI, organizations can streamline their testing processes, ensure consistent quality, and ultimately deliver higher-quality products to their customers. This transformation not only enhances product reliability but also reduces costs, accelerates time-to-market, and equips businesses with the agility needed to thrive in an increasingly competitive technological landscape. AI’s impact on quality assurance is a testament to the ongoing evolution of technology in the pursuit of excellence and customer satisfaction.

How can TestingXperts help transform QA using AI

TestingXperts, as a specialized software testing and quality assurance company, offers comprehensive AI-driven solutions to facilitate the transformation of your quality assurance processes. They can leverage AI to enhance the efficiency, accuracy, and agility of your testing initiatives. By employing AI-powered test automation, TestingXperts can help you reduce manual testing efforts, accelerate test execution, and enhance test coverage. Their expertise in developing self-healing test scripts and frameworks ensures that your tests remain robust even as your applications evolve, reducing maintenance overhead. Additionally, they can optimize test data management through AI, ensuring the availability of diverse and realistic data sets for thorough testing. This not only improves the quality of your tests but also contributes to data privacy compliance with AI-driven anonymization techniques.

Furthermore, TestingXperts can assist in predicting potential defects through AI-based analysis of historical data and patterns, allowing your QA teams to proactively address vulnerabilities before they impact your software’s performance. Their AI-driven performance testing solutions simulate real-world user behavior and load patterns, providing valuable insights into system performance under varying conditions. With their expertise in AI-enhanced security testing, TestingXperts can identify security vulnerabilities and weaknesses, helping you safeguard your applications and data. Whether you need to optimize test cases, automate testing processes, or bolster your security measures, TestingXperts’ AI-driven solutions are tailored to elevate the quality assurance landscape of your organization, enabling you to deliver robust, secure, and high-quality software products to your customers.

The post How AI in Quality Assurance Enhances Efficiency and Improves Accuracy first appeared on TestingXperts.

Content 1. An overview of AI in DevOps 2. How is AI in DevOps revolutionizing product releases? 3. Impact of Leveraging AI in DevOps 4. How does AI-powered DevOps enable improved CX? 5. Best practices to implement AI in DevOps 6. Future trends of AI and DevOps in 2022 7. Conclusion

Improving the existing customer experience (CX) is a key driver behind digital transformation initiatives. For digital businesses, CX is one of the primary metrics for measuring the quality and efficiency of their products and services. Artificial Intelligence (AI) and Machine Learning (ML) are trending these days; similar is the case with the DevOps world. These next-gen technologies transform DevOps by automating tasks and making the product development life cycle more customer-centric. From decision-making process improvements to automated operations and code quality enhancements, the future of AI in DevOps looks promising for enabling a great CX.

An overview of AI in DevOps

Today organizations are focused on being data-driven to incorporate the capabilities of AI & ML to achieve their ambition. The usage of AI & ML is set for tremendous growth in multiple folds in almost all the fields across industries such as healthcare, BFSI, retail & eCommerce, EduTech, etc.

Since the development of AI aims to leverage data, the inclusion of ML and AI in DevOps enables businesses to witness the true potential of digital transformation. For DevOps organizations running on real-time data, AI & ML enables automatic integration of technology components, intelligent QA analysis, bug fixes before the execution of release pipelines, automated reporting, and more.

How is AI in DevOps revolutionizing product releases?

Improves data access:

DevOps environment generates a lot of data. The teams sometimes face issues accessing that data, but Artificial Intelligence helps compile and organize data from multiple sources. This data helps to analyse and give a good picture of trends.

Enhances application security:

AI and ML can help detect Distributed Denial of Service threats by using algorithms to differentiate normal as well as abnormal conditions.

Ensures efficient Software testing:

AI collects and identifies the pattern of software testing data generated via various types of testing performed in a DevOps environment. This data is used by the DevOps teams to identify the root cause of errors in coding practices and fix them to increase the process efficiency.

Prioritizes alerts:

The DevOps team receives alerts with no priority tags. This makes it quite difficult for the teams to manage these alerts. AI helps the DevOps teams to prioritize alerts based on key metrics such as behavior, source of the alert, etc.

Refines feedback loops:

The primary function of DevOps is to use monitoring tools and collect feedback from every stage. AI tools use features such as log files, performance metrics, etc., to provide and apply intelligent decisions.

Impact of Leveraging AI in DevOps

By streamlining and accelerating every phase of the SDLC, AI takes DevOps to a new level of accuracy, efficiency, and credibility. Here are the key impacts DevOps teams experience when leveraging AI into their existing DevOps processes:

Quality checking:

AI facilitates the creation of comprehensive test patterns according to the learning from previous releases and leads to optimized and continuous delivery.

Efficient production cycle:

AI efficiently analyzes resource utilization and similar patterns to detect any memory leaks that may lead to production issues.

Prompt addressing:

AI and ML play a crucial role in dealing with sudden alerts by continuously training the system to identify the anomaly.

Early detection:

AI detects the issues and immediately alerts the Ops team for prompt mitigation that leads to business continuity.

Business assessments:

While DevOps pays high regard to understand code release for achieving business goals, AI tools analyze the pattern-based functionality of the application through user metrics and alert the respective business teams in case of any issue.

How does AI-powered DevOps enable improved CX?

Integrating AI in DevOps improves existing functions and processes and simultaneously provides DevOps teams with innovative resources to meet and even surpass user expectations. Here is how AI within DevOps enable improved CX:

Operational Benefits of AI in DevOps

Instant Dev and Ops cycles:

With AI, DevOps teams can provide superior CX by optimizing application performance through rapid Dev and Ops cycles. Insights on user behavior enable the DevOps teams to prioritize the performance updates for future application releases that enhances the user experience.

Test more, release better:

DevOps teams test, code, release, and monitor application behavior. By leveraging AI, DevOps teams can accelerate these steps as well as identify and fix bugs promptly, enabling an amazing CX.

Improve application security:

AI-based algorithms improve the way DevOps teams detect and fix the security threats of the application. With the help of AI, DevOps teams can effectively highlight false positives, suspicious activities, and more.

Growth benefits of AI in DevOps

Rapid CX enhancements:

AI reveals key insights that directly impact CX for DevOps teams and help them rapidly make informed decisions. By leveraging AI, teams analyze the insights and refine their existing CX strategy by providing real-time responses to customer inquiries.

Optimizes customer interactions:

Businesses leverage AI-based interactions into all the leading customer-facing channels that personalize the CX and provide them with prompt resolutions.

Process automation:

AI enables DevOps teams to automate behind-the-scenes processes like email marketing, social media promotions, targeted automated marketing promotions, SMS promotions, etc.

These benefits, however, may only be achieved in full length when DevOps teams follow the best practices of implementing AI in DevOps, as described below:

Best practices to implement AI in DevOps

Utilize advanced APIs:

The Dev team should gain experience with canned APIs like Azure and AWS that deliver robust AI capabilities without generating any self-developed models. This facilitates accessible development where the Dev teams can work on enhancing the models as per use cases and integrate add-ons to create advanced patterns.

Train with public data:

DevOps teams should leverage public data sets for the initial training of DevOps models. This data may not be an exact match with the project’s requirements but can effectively fill the gap to build up the project visibility.

Implement parallel pipelines:

As AI is still evolving, DevOps teams should run parallel pipelines to ensure that the entire system does not come to a halt in case anything breaks. DevOps teams should gradually implement AI to effectively inline the project’s progress with AI adoption into the DevOps ecosystem.

Deploy pre-trained models:

Pre-trained or documented models accelerate the adoption of AI capabilities of DevOps ecosystems. Past search patterns can be utilized as references to recognize inputs or user behavior and make intelligent business decisions.

Future trends of AI and DevOps in 2022

Data fabric:

Enables flexible integration of data sources across multiple users and platforms that make data available on-demand irrespective of the data location. Data fabric will rely on analytics to reduce data management efforts by 70%

Cybersecurity mesh:

Enables comprehensive security solutions that can quickly verify identity, context, and policy adherence across cloud and non-cloud environments.

Hyperautomation:

Enables rapid identification and automation of all possible business and IT processes. Hyperautomation will help businesses leverage AI-driven DevOps with scalability and business model disruption.

Decision intelligence:

Enables businesses to improve their decision-making abilities via AI, augmented analytics, and simulations.

Total experience:

Enables a business to comprehend various touchpoints such as experiences of employees, customers, and application user experience to accelerate UX optimization.

Conclusion

By using a systematic DevOps approach, digital businesses have been promoting close coordination between their Dev, Ops, and customer support teams for faster development, and quicker updates. However, as the next-gen technologies transform DevOps by automating tasks and making the product development life cycle more customer-centric, it is evident that the future of DevOps will be influenced incredibly by AI.

By leveraging AI, DevOps-driven businesses can execute data-driven and continuous development and enhance the overall CX. As the DevOps teams deep dive into the user behavior, they can target and enhance the essential UX areas. Integrating AI-powered capabilities into the existing DevOps ecosystem will improve the traditional processes and help businesses be at the forefront of cutting-edge innovation.

The post How Does AI in DevOps Enable Improved CX? 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.

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.

The post Artificial Intelligence and its Impact on the Software Testing Industry first appeared on TestingXperts.