Artificial Intelligence (AI) has been taking the world by storm for a couple of years now, especially since the global recession of physical market capabilities during the pandemic. Traditionally, testing was carried out through manual efforts which made the entire process not only prone to errors but vulnerable to slow development. Some of the widely recognized manual testing issues are listed below: 

• Time Constraints: Testing software involves voluminous processes, as well as repetitive. Without AI-powered automation, these tests would take a much longer time to be developed and deployed.  
• Human Error: Human-made tests have a higher possibility of error. Even a slight mistake can cause the whole testing process to produce nil results.
• Costs and Resources: Naturally, there is considerable inflation in the usage of resources and costs due to the time-consuming manual testing processes and leveraging complementary tools for the same. 

This is where AI has made a star-entrance and enhanced the efficiency of the whole testing process. One of the major buzz-cases is the test automation. Machine Learning (ML) has served as a counterpart for AI-generated automated test cases. This intelligent technology can learn and adapt to optimize the testing processes, maintain and analyze large pools of data, and automate repetitive and voluminous tasks to produce faster and more accurate results while minimizing errors considerably.  

This write-up aims to highlight the vitality of AI testing towards software testing and how it will provide supreme assistance in the coming years.

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Importance of AI for Software Testing

The future remains uncertain but technologies like AI/ML have already offered us a light at the end of the tunnel. Smart technologies like AI, ML, and NLP (Natural Language Processing) are renowned to be intelligent additions to the testing tools and processes while much more still remains to be discovered in the future. Here are some Need-to-Know’s of AI integration in software testing processes:  

• Regarding test automation services, the utilization of intelligent technology is essentially associated with object application recognition and classification fixated at all user interfaces. Under this, tools are created while utilizing the categorized controls recognized through the above classification. Moreover, testers can pre-train these controls that are usually available in out-of-the-box arrangements.
• As a rule of the process, testing is a means to confirm results: hence, it is necessary to acquire surplus test data. As a modern concept, Google DeepMind has developed an AI program that deploys deep reinforcement learning to play video games on its own. This program is efficient enough to generate sufficient test data for data acquisition. 
• In the upcoming years, cognitive technologies are likely to monitor testers involved in exploratory testing contained within the testing sites. This also includes assessing and spotting test applications built through human involvement. Consequently, business users will be attracted to actively implement their testing processes while increasing the chance for customers to entirely automate their test cases. 
• Smart risk automation ensures that the right tests are being imposed for optimum coverage while delivering on-time results. With AI as a core component of the test build, deployment, and analytics, it enables testers to permanently rid the systems of bugs and related components more efficiently.

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AI Testing & Related Advantages  

Finally, we mention some of the prominent benefits of AI testing which makes it a bonus inclusion for software testing:

1. Fastens Manual Testing

It is a well aware fact that countless test lines are coded even by the most skilled application development companies.  Manual testing is not that efficient in maintaining speedy processing even after all efforts are put in place. Above all, functional testing tends to be expensive, thereby, consuming additional time and money.

Incorporating AI in testing not only facilitates accelerating the testing process, but it also takes care of writing scripts and analyzing huge chunks of data making these voluminous tasks less time-consuming and more accurate. AI capabilities extend to sorting via log files, increasing programming accuracy and cutting time losses.

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2. Added Assistance to Testers and Developers

Shared test automation can assist developers in identifying critical bottlenecks and bugs in the system before the QA phase begins. In other words, as soon as source code variations are fed to the system, tests will be executed automatically to check for any inconsistencies in the code, making it easier to recognize failure in the early stages of development. 

3. Accelerated Time-to-Market

Every time there is an alteration in the code, be it due to upgrades, new regulatory guidelines, or changes in software functionality, software tests are run to ensure smooth implementation and usability. If run manually, these repetitive testing processes prove time-consuming and highly expensive. Test automations are a respite to these setbacks since they can run these repetitive tests ensuring continuous delivery without any added costs, and reduced time-to-market. 

4. Advanced Test Coverage

As AI is merged with automated testing, it works to enhance the overall depth and scope of tests. Automated software testing can easily access memory, file contents, data tables, etc., for determining the working status of the software. On the whole, test automation is capable of carrying out 1000+ test cases related to each test run offering coverage that isn’t doable with manual tests.

5. Scriptless Coding (Low-Code/ No-Code)

As the technological world advances, innovations like scriptless coding are gaining traction. This allows applications to be built with minimal to no coding requirements. The coding environment is becoming more accessible to people with little to no coding skills. However, testing these applications built through minimal coding is a complex task for users who have no prior knowledge of programming. This is where low-code/ no-code (LCNC) test automation comes in handy. 

LCNC test automation is a set of tools that require minimal to no prior coding knowledge. With a user-friendly interface, these testing platforms are accessible to anyone who wishes to test their LCNC applications.

Final Thoughts 

AI in software testing has the potential to transform all testing processes and aid in delivering high-quality software. As more technological advancements pour in, innovations are likely to accelerate the way companies do business. According to a recent study, the test automation market is expected to grow at the rate of 15% CAGR as more companies embrace the new-age tech. This is your cue to look through these paradigm shifts and bring about a new perspective to your testing capabilities.  

We at ImpactQA, are a well-seasoned team of experts that can help you embark on this AI-powered journey of limitless software testing possibilities. If you have any further concerns, connect with us and our dedicated team of experts will assist you at every step.

The post What is the Impact of Artificial Intelligence on Software Testing? appeared first on ImpactQA.

Unlike traditional testing approaches reliant on human efforts, often susceptible to sluggishness, high costs, and occasional errors, AI strides in with a solution. AI in test automation swiftly crafts test cases, deciphers complex code patterns, and predicts potential challenges before they snowball into major stumbling blocks. This acceleration not only amplifies pace but liberates human testers to delve into the intricate and innovative facets of their role, ultimately driving a substantial elevation in software quality standards.  

How AI and Low-Code are Changing the Dynamics of Test Automation

The software testing market has undergone a gradual evolution, transitioning from traditional manual testing to a semi-automated approach and ultimately toward full automation using specialized tools. More recently, there has been a growing demand for automation methods that don’t require coding, instead utilizing bots empowered by AI and ML technologies. Specifically, AI-based software test automation has emerged as a highly sought-after approach in recent years.

One of the prominent uses of AI in test automation is reducing manual effort. The integration of AI in automation testing services has enabled automated test cases to simulate human-like interactions with software. AI-driven tools have the capability to script, execute, and analyze tests automatically, significantly minimizing the need for manual intervention. This not only accelerates the testing process but also reduces the possibility of human error. AI algorithms can also predict the maintenance needs of the application, ensuring a proactive approach to fixing potential issues before they escalate.

For example – Google leverages machine learning to optimize and advance test automation processes. This involves generating test cases using past data, predicting test failures for efficient resource allocation, enabling test scripts to update themselves according to code alterations, and identifying anomalies in test outcomes to signal possible problems. In summary, machine learning optimizes and enriches Google’s test automation workflows, elevating efficiency, coverage, and software excellence, all while minimizing manual intervention.

Low-code test automation, on the other hand, has recently gained significant popularity due to its ability to simplify and accelerate the test automation process. Low-code platforms provide an intuitive graphical interface, enabling testers to design, build, and implement automated test cases with minimal coding. This democratizes test automation, making it accessible to a wider audience, including those with limited coding expertise. According to Gartner‘s projections, by the year 2025, approximately 70% of newly developed enterprise applications will be created using no-code or low-code technologies.

The low-code approach allows for faster test case creation and modification. Testers can drag and drop pre-built automation components, configure them, and link them to create test cases. This reduces the learning curve and speeds up the entire testing cycle. Low-code test automation also promotes collaboration among cross-functional teams, providing a common platform for testers, developers, and business analysts to work together seamlessly.

The global low-code platform market is anticipated to produce revenue amounting to $187.0 billion by the year 2030, with an expected compound annual growth rate (CAGR) of 31.1% over the forecast period from 2020 to 2030.

Benefits of AI in Software Test Automation

1. Enables Visual Validation in UI Testing

AI can be employed for UI testing by utilizing image recognition techniques to navigate through applications and visually verify UI elements, enabling the creation of UI tests. AI tools also employ exploratory testing methods to uncover application UI bugs. Screenshots are generated for subsequent verification by QA engineers, encompassing visual aspects such as color, size, and layout.

2. Predicts Defects Early in the Development Cycle

AI aids in the early and rapid identification of bugs, decreasing the occurrence of defects and ensuring a bug-free and reliable product for end-users.

3. Supports Self-Repairing Capabilities

Many automation tools with self-healing capabilities utilize AI and ML to automatically adapt to UI or application environment changes. Typically, AI tools identify issues and remediate them without human intervention, employing self-healing techniques.

4. Streamlines Regression Testing

AI-powered tools and engines expedite test creation, enabling the execution of thousands of tests in a single operation and significantly reducing overall test maintenance. These tests seamlessly function across various browsers, including mobile browsers.

5. Guarantees Smooth API Testing Execution

AI algorithms comprehend patterns and relationships within different API calls, effectively categorizing them based on scenarios. They analyze existing tests to learn about API relationships, subsequently utilizing this knowledge to comprehend API changes and create new scenario-based tests.

6. Enhances the Security Testing Procedure

AI expedites the security testing process by swiftly identifying cybersecurity issues within the software. It extracts information from previously recorded data and analyzes real-time weaknesses in the system. AI thereby assists businesses in fortifying the security and privacy of their systems, applications, data, and networks, empowering testers to create more robust penetration tests.

Conclusion

AI and low code have revolutionized test automation by addressing the challenges of traditional automation testing services. With AI’s proficiency in minimizing manual tasks, expanding test coverage, and forecasting maintenance requirements, the testing process has seen a substantial boost in both efficiency and precision. Simultaneously, low-code platforms are democratizing test automation, rendering it available to a wider array of users and speeding up the testing cycle. 

If you are looking for an ideal solution for integrating AI and low-code approaches into software testing, then ImpactQA is the right place. Our test automation services involve a user-friendly interface that caters to both tech-savvy individuals and those with limited technical expertise, making automation of the testing process easily accessible. Moreover, we seamlessly integrate with various testing frameworks and a wide array of other tools – so whatever your team needs, ImpactQA has got you covered! 

The post How AI and Low-Code are Revolutionizing Test Automation? appeared first on ImpactQA.

The storm of generative AI systems such as ChatGPT, DALL-E, and Google’s Bard is transforming entire industries. According to the MarketAndMarket report, the generative AI market is expected to grow from $11.3 billion in 2023 to $51.8 billion in 2028. 

This type of AI has the potential to revolutionize how we create and consume content. In this article, we’ll explore what generative AI is, how it works, the types of generative AI models, and its impact in the real world. 

What is Generative AI? 

Generative AI refers to a type of machine learning model that creates new data from scratch. Unlike traditional AI models that use existing data to make predictions, generative models can create entirely new content that has never existed before. Generative models work by learning the underlying patterns and structures within a dataset. Once it learns these patterns, it can then use them to create new data that is similar in style and content to the original dataset. 

How Generative AI Works 

Generative AI models work by utilizing a training dataset to create a model. The model then generates new data that is similar to the original dataset. The key to generative AI is the ability to generate new data that hasn’t been seen before, while also maintaining the structure and patterns of the original dataset. 

To create these new data, generative models use a range of techniques. One popular technique is Variational Autoencoders (VAEs). VAEs work by encoding the input data into a lower-dimensional space, where it’s easier to model the underlying patterns. The model can then generate new data by sampling from the distribution learned by the model. 

Another popular technique is Generative Adversarial Networks (GANs). GANs are made up of two neural networks that collaborate in order to generate fresh data. The first network generates samples, while the second network evaluates those samples and provides feedback to the generator. The feedback helps the generator to improve and generate more realistic data. 

Autoregressive models are another type of generative model. These models generate new data one element at a time, using a probability distribution to determine the most likely value for each element given the values of the preceding elements. 

Types of Generative AI Models 

Now that you know how generative AI works, let’s dive into the several types of generative AI models. These include: 

Variational Autoencoders (VAEs): VAEs are a type of generative model that works by encoding the input data into a lower-dimensional space. The model can then generate new data by sampling from the distribution learned by the model. 

Generative Adversarial Networks (GANs): GANs are a type of generative model that consists of two neural networks. One network generates samples, while the other network evaluates those samples and provides feedback to the generator. 

Autoregressive Models: Autoregressive models generate new data one element at a time, using a probability distribution to determine the most likely value for each element given the values of the preceding elements. 

Flow-Based Models: Flow-based models are a type of generative model that uses invertible functions to transform a simple distribution into a complex distribution that can generate new data. 

Impact of Generative AI in the Real World 

Generative AI has shown the capability to transform the face of various industries. One of the most significant areas where generative AI is making an impact is in art and music. Generative models can create unique pieces of art and music that have never existed before. For example, AIVA, an AI-based composer, creates unique pieces of music using a deep neural network. 

Generative models are also making an impact in the advertising and marketing industry. Businesses can use generative models to create unique content for their websites, social media accounts, and other marketing materials. This allows businesses to automate their content creation process, saving time and effort. 

Conclusion 

Business leaders must develop a clear and compelling generative AI strategy now in order to acquire a competitive advantage. Generative AI will reduce time to market while improving digital product quality, efficiency, and accessibility. It is a good moment for software engineering executives to make rapid changes in order to adopt generative AI and realize its competitive benefits. 

The post Generative AI: The Next Big Thing appeared first on ImpactQA.

What are AI Fraud Detection Systems?

AI fraud detection systems are software or tools that use AI technology to detect fraudulent activities. They can help companies identify suspicious activities, such as unusual purchases or suspicious payments. One can also use AI systems to scan for fraudulent transactions, such as those from stolen credit cards.

AI fraud detection systems can be used by businesses of all sizes, from small startups to large corporations. AI software can help companies identify suspicious activity quickly and accurately, saving them time and money in the long run.

What are the Benefits of Using AI for Fraud Detection?

Using AI for fraud detection has many benefits. Some of the key benefits of AI fraud detection include the following:

• Efficient than manual methods

AI fraud detection systems are more efficient than manual methods. An AI-driven program can detect behavior patterns that may be too difficult for manual detection. AI systems can also reduce the time it takes to detect fraud, making it easier for companies to identify suspicious activity quickly. Moreover, such systems can trigger alerts in case of any slightest deviation from an established process, which can help companies prevent malpractice.

• More accurate

AI fraud detection systems are more accurate than manual methods. AI can detect subtle behavior patterns and make more precise decisions than a human analyst. AI systems can also help reduce false positives, as AI can accurately distinguish between fraudulent and non-fraudulent activities.

• Cost-effective

AI systems can help companies save money by reducing the time and resources needed to detect fraudulent activities. AI systems can also help businesses reduce costs by helping to prevent fraudulent activities from happening in the first place.

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Disadvantages of Using AI for Fraud Detection

While AI-based fraud detection systems can benefit businesses of all sizes, there are also some potential drawbacks.

• Not always accurate

AI systems may not always be 100% accurate, and there is always the potential for false positives or false negatives.

• Bias

AI systems may also be prone to bias, depending on how they are trained and the data they are given. This can lead to inaccurate results or even discrimination against certain groups.

• Security risks

AI systems may also be vulnerable to security threats, such as malicious software or hackers. Companies should be sure to protect their AI systems from potential security risks.

• No human understanding

Finally, one of the major drawbacks of using AI for fraud detection is that AI systems lack human intuition, psychology, and understanding, which is hard to beat in some cases.

How to use AI for Fraud Detection

One can break down the process of using AI for fraud detection into five major steps. They are:

• Providing input data

The first step is to provide input data to the AI system. This data should include information about past fraudulent activities and other relevant details. This includes transaction values, customer profiles, geographical locations, and more.

• Rule setting

The next step is to set up rules for the AI system. This involves defining parameters the AI system will use to identify suspicious activities.

• Rule reviewing and activation

Once the rules have been set up, the next step is to review them and activate the system. During this stage, the rules should be tested and adjusted as needed.

• Training the model

The fourth step is training the AI model. There are various AI models, such as supervised and unsupervised learning. Companies should choose the best model for their needs and train it using labeled data.

• Testing and deploying the model

Finally, companies should test their AI model to ensure it works correctly. Once the model has been tested, it can then be deployed in a live environment for fraud detection.

Examples of Using AI for Fraud Detection Purposes

Businesses can use AI in various scenarios and settings to detect fraudulent activities. Some common examples include:

• Financial transactions monitoring

An organization can use AI to monitor financial transactions for suspicious activities. This includes identifying patterns of activity that may indicate fraud, such as unusual spending patterns or rapid changes in account balance.

• Identity verification

Organizations, especially financial companies, can also use AI to verify customer identities by confirming government-issued IDs or scanning fingerprints and faces.

• Transaction anomaly detection

Banks and financial institutions can use AI to detect unusual or suspicious transactions, such as those involving large sums of money or those made from unfamiliar locations. This can help companies identify potential fraud in real time.

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Need for Testing AI Software Used in Fraud Detection Systems

AI testing is essential to developing any AI-based system, including fraud detection. AI testing helps ensure that your software is functioning properly and accurately detecting any suspicious activity. This is a critical step in ensuring that your fraud detection system is reliable and accurate.

AI testing should include automation and manual tests to ensure that the system works as expected. Manual tests should consist of scenarios that involve varying input data and conditions, while automated tests can be used to test the system’s accuracy and responsiveness. It is also important to test the system’s security measures to protect it from malicious attacks.

Conclusion

Overall, AI can be a powerful tool for fraud detection. Still, ensuring that the software is appropriately tested and secure before deploying it in a live environment is crucial. Companies should also take steps to protect their AI systems from potential security risks. With the proper implementation, AI can be an invaluable tool for fraud detection.

If you are looking for AI testing services to ensure your fraud detection system is working as intended, look no further than ImpactQA. Its experienced team of developers and testers can help you at every stage of the software development cycle, ensuring that your system is reliable and secure. Contact us today to learn more about our services.

The post Using Artificial Intelligence for Fraud Detection appeared first on ImpactQA.

In this new-age era, where launches and releases are happening every week and upgrades almost every other day, Artificial Intelligence (AI) is like the power elixir. The presence of an AI-managed testing stage can distinguish changed controls more adequately than a human. Moreover, with stable and nonstop algorithm updates, even a small percentage of changes can be watched. 

Automated testing has enabled Artificial Intelligence (AI) to be used extensively in categorizing item applications for each UI. In this context, recognized controls are characterized by the ability to train pre-trained commands typically observed in out-of-the-box setups. When the chain of controls is observed, experienced AI software testers can create a technical map to the point where the artificial intelligence looks at the graphical user interface (GUI) to obtain labels for each control.

This blog will talk about the various advantages of AI in software testing or AI testing and how it streamlines the advanced use of different software testing tools.

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Benefits of AI in Software Testing

• Surpass the Need for Manual Testing

Most QA/Software testing departments would struggle to implement a controlled web application test with 1,000+ clients. However, with AI software testing, one can simulate tens, hundreds, or thousands of virtual client sets interacting with a product, system, or web-based application.

• Improved Accuracy

Human errors are a typical occurrence that can happen at any time. Even the most careful software tester will make mistakes when performing tedious manual testing. It is why AI automated testing comes in handy, completing the same process or stages flawlessly and ensuring detailed results. Testers who no longer have to deal with tedious manual testing methods have more time to create new automated software tests and deal with complex features.

• Enhance Overall Test Coverage

By expanding the scope and depth of testing using automated AI testing, you can improve software quality. Software testing with artificial intelligence (AI) can look into data tables, memory and file contents, and program states to determine whether the software is functioning as it should. Test automation can run 1000+ unique cases in a trial, delivering coverage that is unimaginable when it comes to manual tests.

• Supports Both Developers and Testers

Software developers and designers can use shared automated tests to catch troubles quickly before going to Quality Assurance. For example, tests can run automatically whenever source code changes are checked and immediately alert the group or the developer if the event does not succeed. Features like these spare the developers’ valuable time and increase their confidence.

• Saving Time for Faster Time-to-Market

Software testing is repeated every time source code is modified; thus, manually repeating those tests can be costly and time-consuming. On the other hand, AI automated testing can be applied repeatedly at little to no additional expense and quickly. As a result, software testing can be completed in hours rather than days, directly reducing costs.

• Conduct Visual Testing

Artificial intelligence can find visual faults in apps by conducting visual testing and making sure that all the visual aspects look and operate correctly. It is made possible via pattern recognition and picture recognition. Using pixel-level analysis, AI can detect dynamic UI controls regardless of size and shape.

• Automated API Test Creation

With the active use of API automation testing, organizations can collaborate to develop numerous test cases to guarantee API quality assurance. This makes it possible to determine how many third-party tools are operational. Since only a small number of services use hundreds of APIs, automation is essential. AI-based tools are created in a way that allows them to quickly assess whether an API is functioning or not after looking at a large amount of data.

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Artificial Intelligence Aiding Software Testing Tools

The smart removal of manual constraints can sum up the benefit of AI in software testing and development. Artificial intelligence is a game changer, boosting software test automation tools or streamlining the overall SDLC.

It is critical to comprehend the benefit of using this strategy for a CI/CD pipeline. As an illustration, think about how the test automation tools run the tests and deliver results for you. Many gadgets, however, don’t know which tests to execute, so they either perform all of them or slowly facilitate a predetermined selection.

In this case, an AI-guided bot’s work can assist in evaluating the tests’ current condition and tracking any new code additions and overall code coverage to choose the best tests to execute and implement.

More specifically, using AI can cater to explicit decision-making when it comes to software testing tools. Implementing AI-enhanced software can readily replace the work of software professionals.

Bottom Line

To reliably speed up the delivery of software products, businesses all over the world are embracing the power of AI in software testing. This is because AI can intelligently automate test operations. The emergence of AI software testing enables cutting-edge technological strategies to improve decision-making capabilities.

To keep up with the most recent revelations in the software industry, you must alter course with artificial intelligence. It can serve as the road map to your desired business milestone. Collaboration with professional AI testing services provides the best way to learn more about how artificial intelligence can be integrated into your software testing strategies.

The post Top 7 Advantages of Integrating AI in Software Testing appeared first on ImpactQA.

In a nutshell, combining AI and cloud computing creates a large network capable of storing massive quantities of data while also learning and improving.

According to MarketsAndMarkets, the cloud computing market size is estimated to reach $947 billion by 2026, double the size of the current market. While the AI market is expected to increase more than fivefold to $309 billion, as per the study. The combination of cloud computing with AI will enable users to not only store data, but also analyze and draw conclusions from it.

Advantages of AI in Cloud Computing

The incorporation of artificial intelligence and machine learning capabilities in the cloud has completely altered the cloud environment. Using machine learning algorithms, the cloud is evolving into an intelligent cloud that can accomplish a lot of useful work effectively. Here are some of the most significant advantages brought about by the combination of AI with cloud computing:

More Economical

The elimination of costs associated with on-site data centers, such as hardware and maintenance, is a significant benefit of cloud computing. With AI projects, those initial expenses can be prohibitive. With the help of the cloud, organizations can quickly access these technologies for a monthly subscription, making research and development costs more reasonable. Furthermore, AI systems can extract insights from data and analyze it without human participation.

Boost Productivity

Software management, developing production, and testing environment are a few of the tasks for which the algorithms based on AI need significant time and effort. It gets eliminated by using a centrally managed hybrid cloud, or a public cloud, allowing IT employees to focus on routine activities.

Impactful Analytical Data

Analyzing a vast data set for evaluating customer trends and patterns is accomplished effectively with AI. It compares past data to the most recent data, providing IT teams with well-informed, data-backed information.

Furthermore, AI systems can do data analysis quickly, allowing businesses to respond to client requests and issues immediately and efficiently. AI capabilities provide observations and vital advice, resulting in faster and more accurate results.

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Automation Intelligence

Integrating AI right into the cloud ecosystem can help in automating repetitive processes and simplify work. AI tools are used in a hybrid cloud system to monitor, manage, and self-heal individual public and private cloud components.

Advance Data Management

We can clearly see that AI plays a core role in data processing, management, and structuring. Using reliable real-time data, we can improve marketing, customer service, and supply chain data management. AI solutions simplify the process of ingesting, modifying, and managing data.

Downsides of AI in Cloud Computing

While the benefits are substantial, it is critical to have a thorough grasp of the subject before making a judgment. As with every coin, there are two sides to every coin, and merging AI with cloud computing can have certain drawbacks. For example, deploying AI may drastically reduce costs, which is true, but there is a catch. AI is a complicated technology, and businesses will require well-trained people to make the most use of such cutting-edge technology. Which will eventually cost more money in terms of providing enough training and knowledge.

Here are some challenges that companies can face while using artificial intelligence in a cloud computing environment:

Connectivity Crisis

The constant flow of internet access is imperative for cloud-based machine learning systems to function smoothly. IT departments use the internet to deliver raw data to cloud services and recover processed data. Poor internet connectivity can limit the benefits of cloud-based machine learning algorithms.

While cloud computing is faster than traditional computing, there is a significant delay between sending data to the cloud and obtaining results. It is a primary issue when utilizing machine learning algorithms for cloud servers since prediction speed is a widespread concern.

Data Privacy Concerns

SaaS technology’s pay-as-you-go model enables thousands of businesses globally to make sense of data, identify efficiencies in daily procedures, develop new products, and even grow into other verticals.

As a result, companies run their customer, vendor, and market data through cloud applications with little or no understanding of the public cloud’s security dangers. When AI processes data provided into a SaaS service in a public cloud setting, it exponentially compounds these hazards. When the processes and perimeters for AI algorithms are not established in a clear manner, the chances of sensitive data getting exposed to a security breach increases.

To avoid such circumstances, enterprises need to focus on creating privacy policies and protecting all the sensitive data while utilizing AI in a cloud computing environment.

Conclusion

AI has already made a dominating start in the tech world and is present in every industry. On the other hand, almost every technology now includes cloud backup services. As a result, the demand for cloud computing services appears to be increasing in the next years.

Although the usage of AI and cloud computing together is in its initial phases and few of the leading companies take charge of investing huge amounts in AI-based cloud testing services. We need to understand that both are growing technology that has a lot of potentials to grow and evolve more in the coming years.

The post AI in Cloud Computing – Blend of Two Growing Technologies appeared first on ImpactQA.

Artificial intelligence and machine learning algorithms have become more widely used technology in high-stake industries like healthcare, finance, and automobile manufacturing. As a result, the implementation of AI/ML has sporadically grown in these industry-specific applications.

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AI has roots everywhere, which is why it is critical to test these AI/ML-driven apps in order to achieve higher operational efficiency, product iteration, and data security. It is crucial to focus on the problems, critical areas, and significant factors involved in effectively secured testing of AI/ML-based systems.

Critical Areas to Consider While Testing AI-based Systems

Data is the new code for AI-based systems. As a result, in order to have an effective operating system, these solutions must be validated for any changes in input data. This is somehow comparable to the traditional testing approach, in which any modifications to the code result in testing the improved code.

There are a few things to take into account when reviewing AI-based solutions:

Curation of semi-automated training data sets: The semi-automated tailored training data sets incorporate the input data and desired output data. Annotating data sources and features, which is a critical component for migration and deletion, requires static data dependency analysis.

Developing the test data sets: To verify the efficacy of trained models, test data sets are rationally designed to test all potential permutations and combinations. The model is refined throughout training as the number of observations and data variety grows.

Developing test suites for system validation: System validation test suites are generated using the test data sets and algorithms. For instance, in a test case, an AI/ML implemented healthcare system designed to predict a patient outcome based on clinical information should also include patient demography, medical therapy, risk profiling of patient’s disease as well as other required data for the test case.

Reporting the test results: Since ML-based algorithm validation produces range-based precision (confidence ratings) rather than anticipated benefits, test results must be expressed statistically. Testers must define and specify assurance criteria within a relevant interval for each iteration.

Challenges Involved while Testing AI & ML Systems

Proper Training Data: It is estimated that almost 80 percent of scientists’ time goes on creating training datasets for ML models. As these systems highly depend upon labeled data.

Hard to Determine: AI and machine learning systems frequently exhibit disparate actions in response to the same information. They are more likely non-deterministic.

Bias: The training data are often distributed from a single source which can lead to bias.

Ability to Explain: When it comes to extracting certain attributes, the challenge level is enormous. Finding out what led a system to incorrectly detect a picture of a coupe as a sedan, for example, may be impossible.

Continuous Testing: Once a traditional system is tested and validated, it doesn’t require further testing unless a modification has been done to the system. Whereas, the AI/ML-based system, on the other hand, constantly learn, adapt, and train with the new inputs.

Key Aspects of AI & ML System Testing

Curation and Validation of Data

The performance of an AI system is determined by the richness of training data, which includes factors such as bias and variety. Understanding diverse accents are difficult for car navigation systems and phone voice assistants. Like the accent of a Japanese person and an Australian individual can be completely different and difficult to interpret by an AI/ML system. This means that data training is essential for AI systems to get accurate input.

Extensive Performance and Security Testing

QA for AI systems, like any other software platform, causes extensive performance and security testing, along with regulatory compliance testing. Without adequate AI testing, unique security issues such as chatbot manipulation and utilizing speech recordings to mislead voice recognition software are becoming a widespread practice.

Performing Algorithm Testing

Algorithms are the core of an AI system that can process huge chunks of data and provide great insight. The key benefits of this method are model validation, learnability (a great example would be e-commerce sites like Amazon), algorithm efficiency, and real-world sensor detection.

A reliable AI testing approach should thoroughly investigate model validation, efficient learnability, and algorithm efficacy. Any errors in the algorithm may have far-reaching consequences in the future.

Smart Systems Integration Testing

When testing artificial intelligence systems, it is important to keep in mind that AI computers are designed to connect to other systems and solve problems in a much bigger context. During AI testing, a full assessment of the AI system, including its many connection points, is required for seamless, functioning integrations.

Conclusion

When deploying an AI/ML model into production, the number of factors that must be examined vary dramatically from standard software testing approaches. AI/ML-based systems must be updated on a regular basis in order to focus on the data that is fed into the system and the predictive outcomes that are generated.

As more and more businesses start implementing AI in their systems and applications, the testing approaches and procedures will automatically evolve and will ultimately reach the stage of maturity and standardization of traditional testing models.

ImpactQA provides effective and efficient testing services for AI/ML applications utilizing the most up-to-date methods and technologies, resulting in faster deployment, reduced test case redundancy, and shorter time-to-market. Please contact us if you have any AI/ML testing needs.

The post Guide to Successful AI/ML System Testing appeared first on ImpactQA.

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Artificial Intelligence Permeation in Testing

According to research conducted by the Institute of Medicine of the National Academies of Science, Engineering, and Medicine (NASEM), diagnostic errors cause around 10% of patient deaths and account for almost 17% of hospital problems. Although NASEM said that these challenges are not caused by professional ignorance, but rather by a combination of variables such as ineffective communication between patients and organizations, human mistakes, and others. 

 AI implementation and testing in the medical sector can aid in illness diagnosis and treatment by evaluating massive amounts of treatment and patient data (previous medical data, doctor reports, etc.) and offering relevant assistance and recommendations to clinicians. As a result, the total treatment procedure would be sped up and improved.  

Artificial Intelligence (AI) to Improve the Medical Sector

The use of AI in medical diagnostics can assist physicians to enhance medical therapies in a variety of ways. Physical burnout as a result of overwork is a severe problem that many medical professionals experience these days. It reduces medical practitioners’ overall performance, which leads to an increase in diagnostic inaccuracy. The most recent Medscape National Physician Burnout and Suicide Report 2020 data highlighted the risks of putting too much pressure on physicians, particularly those juggling families, retirement plans, and the intricacies of their employment.  

Furthermore, AI in medical diagnosis could help reduce the number of diagnostic errors made every year. Making use of the AI ability of deep learning professionals can increase the efficiency of disease detection. A recent study shows that an AI system has achieved the skill of tracking breast cancer similar to an average breast radiologist, demonstrating a 95 percent accuracy rate, published in the journal of the National Cancer Institute. In oncology, AI applications are being utilized to identify tumors. Pathologists use machine vision technologies to diagnose diseases in bodily fluids and tissues, and facial recognition helps match phenotypes with specific rare diseases. 

Other AI use in healthcare includes the development of new medications and more effective drug targeting to improve efficacy and prevent bad drug effects. Hundreds of startups are actively leveraging AI for drug discovery. For instance, Atomwise (a San Francisco-based startup) has recently joined a partnership knot of 1.5 billion USD with the giant Jiangsu Hansoh Pharmaceutical Group on working to design new cancer drugs. 

AI and Internet of Medical Things (IoMT) 

The application of artificial intelligence and the Internet of Medical Things (IoMT) in consumer health apps is another possible area where it can flourish its benefits. These solutions put medical IoT devices to gather healthcare records and AI-based medical apps to evaluate the data and provide modifications based on patients’ current lifestyles. The patient-centered approach of medical software developers has brought the in-house trend toward at-home health solutions.  

One of the potential implementations under consideration is a voice-based virtual nurse program. The main goal is to improve the hospital room experience and make the process of preparing patients to continue their rehabilitation at home easier. Virtual nurses also help to minimize patient anxiety, improve privacy, keep patients interested, and raise patient satisfaction with medical services. 

Challenges with AI in Medical Diagnosis

While implementation of Artificial Intelligence (AI) opens doors of many possibilities but at the same time it raises several challenges for the medical sector: 

Safety of Data

The desire for huge datasets encourages developers to acquire such information from a large number of patients. Some patients may be worried that this data collection would infringe on their privacy, and lawsuits have been brought as a result of data sharing between large health institutions and AI startups. AI may potentially compromise patient privacy in another way: deep learning AI can anticipate personal information about patients that were never even revealed to the algorithm. (In fact, this is often the goal of healthcare AI.) 

Data Availability 

Training AI systems need a massive quantity of data from many sources, including electronic health records (EHRs), medication records, symptom data, and consumer-generated information such as activity trackers or purchase histories. Health data, on the other hand, is usually problematic. Data is frequently spread across several platforms. Aside from the variances stated above, patients routinely change doctors and insurance carriers, resulting in data fragmentation across several systems and formats. This fragmentation increases the risk of inaccuracy, decreases dataset comprehensiveness, and boosts the cost of data acquisition, restricting the sorts of organizations that can construct successful healthcare AI. 

Error

A well-known primary concern is that AI systems will be, or can be, inaccurate on occasion, which might have a negative impact on the patient’s life or other healthcare difficulties. If an AI system prescribes the incorrect medication to a patient, fails to discover a tumor on a radiological test, or gives a hospital bed to one patient than another because it mistakenly predicted which patient would benefit more, the patient may experience damage. Despite this, countless injuries occur in the current healthcare system as a result of medical mistakes, even when AI is not involved.  

AI flaws can differ from one another due to at least two factors. For starters, patients and physicians may react differently to software-caused injuries than to human-caused ones. Second, suppose an AI system is extensively utilized; a single error can endanger thousands of lives. 

Conclusion

In the healthcare business, artificial intelligence has a bright future. Although AI medical diagnosis is not now commonly employed in most clinical settings, experts predict that widespread AI intervention is not far off. And, as we progress toward digitalization and integration of medical data, we will see a rise in the use of AI to assist us in making the best and most cost-effective answers for complicated topics.  

To discuss AI implementations in medical diagnosis, feel free to reach out to our expert team of testers. Contact us for a quick consultation!  

The post Artificial Intelligence (AI) and its Assistance in Medical Diagnosis appeared first on ImpactQA.

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

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

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

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

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

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

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5 Ways AI is Shaping the Future of Software Testing

Addressing Challenges in Test Automation Through AI and ML

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

1. Self-Healing for Test Automation

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

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

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

2. Auto Generation of Test Scripts

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

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

3. Utilize High Quantities of Test Data Effectively

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

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

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

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

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

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4. Image-Based Testing Using Automated Visual Validation Tools

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

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

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

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

5. Spidering AI

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

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

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

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

Keep going…

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

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

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

The post 5 Great Ways to Achieve Complete Automation with AI and ML appeared first on ImpactQA.

What is essential for planning a smart city? The discussed framework comprises wireless technologies such as interconnected devices and the cloud, thereby acquiring a significant share. To endorse such a concept the likes of the Internet of things (IoT) & Machine learning (ML) are preferred without any doubt. These technologies are capable of absorbing the various demands of urbanization through the delivery of innovative and smarter options aimed towards comfortable living.

“Smart Cities are those who manage their resources efficiently. Traffic, public services and disaster response should be operated intelligently in order to minimize costs, reduce carbon emissions and increase performance” –

Eduadro Paes

IoT applications’ presence is to manage and examine real-time data that collaborates with machine learning and works to assist municipalities, citizens, and organizations in upgrading the quality of living. According to Statista, It is estimated that the global share of people residing in urban areas will rise to 70% by 2050, as compared to 56% in 2020.

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IoT in Smart City

Transforming any city into a smart city requires the active deployment of IoT technologies. You can look around and pick up several practical examples across different industries like manufacturing, healthcare, and transportation. The extensiveness of the internet across global regions is the core of IoT applications. Apart from the reduced cost of connection, the introduction of better gadgets with sophisticated sensors and Wi-Fi connectivity contributes to smarter involvement of the Internet of Things.

AI/ML in Smart City

The availability of intelligent machines through Artificial Intelligence (AI) & Machine Learning (ML) has been crucial in escalating the concept of smart cities. You can now incorporate sharp computing programs merged with human intelligence to create a cyber-physical space that includes traffic sensors, video cameras, environment sensors, smart meters, etc. Data acquisition is carried out on a regular basis to frame actionable insights targeted towards intelligent city planning.

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Combined Applications of IoT & ML in Smart Cities

Intelligent Parking Systems

Since we are already familiar with IoT’s role across vehicle tracking platforms, its merger with ML can be used to support smart parking systems. These systems aim to spot vacant locations for a vehicle to be parked, especially in public places.

How does this technology function? An In-Ground vehicle detection sensor’s presence makes it possible as they are implanted within the pavement of various parking spaces. They are responsible for collecting data comprising time and duration for which space has been occupied by vehicles. Later, the data is transferred to the cloud to be processed and sent to the drivers searching for empty parking spaces.

Machine learning algorithms are also put in place to highlight peak hours that are possible with the comprehensive analysis of past trends and real-time data. The availability of an intelligent parking system is beneficial in reducing pointless congestion and fuel costs for people.

Public Safety

To uplift public safety within an urban city, you can actively use IoT technologies that are focused on providing real-time information via CCTV cameras and sensing tools. The comprehensive data collected using these systems help forecast potential criminal activities and implement safety measures.

For instance, the most striking arrangement of safety management is the deployment of the gunshot detection system. It involves connected microphones that are set up in different parts of the city. They collect sound patterns on a regular basis that is later passed onto the cloud platforms for processing. Machine learning is then introduced to filter out the sound of a gunshot. Interestingly, vital factors such as the gun’s location can also be estimated by calculating the sound’s speed reaching the sensor.  

Smart Traffic Administration

Transportation is one prime aspect that can achieve a smoother arrangement with the inclusion of smart traffic solutions. The aim is to achieve avoid vehicle coagulation and help citizens save time and fuel. This can be achieved by deploying various sensors that can record speed, location, and other important information related to your vehicle.  

 Some of the useful systems included in smart traffic management are:

• CCTV cameras
• Road-surface sensors
• Traffic management platform

All these systems together help collect real-time data, which is then analyzed using machine learning. Hence, the user is served with updates related to traffic congestion, road divergence, and other related info. Furthermore, historical data can also be examined with the help of machine learning to highlight peak rush hours beforehand.

Water Management Plan

Water supply and conservation are part of sustainable living for urban cities. The act of setting up a water management system that includes devices such as smart meters is proving helpful to track down water consumption. Furthermore, average consumption for each household, enterprise, or industry can be evaluated using ML technology. Future consumption stats can also be assessed with machine learning algorithms applied to past data.

A smart water management system is efficient in improving water distribution across a city without compromising water pressure and quality. At present, several cities across the globe have started the use of these smart meters to avert potential leaks across underground water pipelines.

Smart Street Lamps

The operation of street lamps can be conveniently managed with IoT devices playing their part. Smart cities are now getting decorated with sensor-fitted street lamps that are linked to cloud platforms. These sensors are responsible for collecting information related to

The sensors help gather relevant data on radiance, schedule of public transport, operational timing, etc. Real-time data availability is useful to analyze various scenarios when merged with historical data and machine learning. Hence, smart lighting solutions help control the brightness and power on/off for streetlights based on traffic volume, timings, and environmental conditions.

Conclusion

The world is heading towards a supremely computerized environment well channelized by modern age technologies such as IoT, machine learning, and artificial intelligence. As mentioned above, we have discussed the application and usage of these technologies for planning a smart city that offers relaxed and comfortable living. For the perfect implementation of new technologies and IoT devices, collaboration with digital transformation experts and software testing providers is a must. It is expected that in the near future the knowledge of such professionals will be applied to remodel multiple cities around the globe.

The post What is the Role of IoT & Machine Learning in Smart Cities? appeared first on ImpactQA.

Presently, artificial intelligence to supervise multiple IT functions, such as performance monitoring, service management, remediating blackouts, etc., is gaining popularity. Therefore, it is slowly becoming imperative to get hold of AIOps for ensuring fast-paced software delivery.

Growing Demand for AIOps

The extensiveness of AIOps as part of the software testing plan is positively responsible at the global level. This is mainly due to this process’s usefulness and its ability to assist the quicker release of complex applications. Many organizations have greatly benefitted as AIOps can handle vast amounts of data without much worry.

Machine learning (ML) and Big Data are seen as the two primary components of AIOps. Therefore, this unites data from monitoring and further assists in automation tasks accomplished in a streamlined order. Numerous companies planning to remodel their testing approach have placed their trust in AIOps to receive useful insights. According to recent research updates, many large enterprises have exclusively preferred AIOps and sophisticated monitoring tools to examine applications and associated infrastructure. Such operations are a great benefit to the organization as it helps enhance overall business value.

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The global AIOps market is estimated to reach USD 237 billion by 2025. These statistics highlight impressive growth that is majorly supported by modern technologies such as machine learning, big data platform, and predictive analytics.

How can Testers Contribute to AIOps Operations?

For the implementation of AIOps, you need to gather a team of testing professionals having a clear idea about the components and steps involved. Similar to DevOps, AIOps also requires a significant cultural shift with the tester placed in an important position.

At the beginning of AIOps implementation, testers’ team needs to get familiar with the AI and ML terminologies. This step is essential and proves beneficial while imparting training. To understand the complete role of a professional tester in an AIOps environment, let us stress on the pre- and post-implementation stages for better insight.

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Pre-Implementation Stage of AIOps

AIOps is known to assist teams in smartly carrying out performance testing operations. Usually, performance testing is conducted during the staging environment but without the involvement of production-related data.

Before AIOps is actually implemented, the testing team ensures the scripting of performance testing scenarios. These are then reviewed in collaboration with business and development teams.

The latter approach by the testing team is to execute the load tests. According to professionals’ tests, it was revealed that the scripting failed while the server collapsed when it reached peak memory usage. At this moment, the utilization of system resources was reasonably high.  

While the testers analyze the script execution results and application log files, they can ask the IT support team for additional assistance. This includes a comprehensive analysis by the IT team focused on various alerts related to the APM monitoring system.

Post-Implementation Stage of AIOps

During this phase, the testers select performance testing scenarios by seeking help from the IT & infrastructure teams. This stage acts useful towards better decision-making carried out by the business & development teams.

After implementing AIOps, the IT support team gets to prepare curative automated scripts for handling the peak usage of various system resources. At the time of performance test execution, the testers are notified regarding dynamic errors, which are carefully resolved by the IT team’s scripts.

Hence, system resources are very well managed; therefore, the testing team can completely dedicate themselves to performance testing activities. It is essential to understand that even after implementing AIOps, the testers’ capabilities are utilized from time to time per system resources. 

The post AIOps Role of Testing Professionals for Successful Implementation appeared first on ImpactQA.

For the success of your software product, it is important to check on the effectiveness of the testing process put in place. The competition in the software testing arena is indeed cut-throat and requires a smarter approach. These days, the inclination is fairly higher towards artificial intelligence as several leading testing enterprises have understood its potential to simplify complex tasks in lesser time.

These days, AI and software testing together are viewed as a supreme set that brings in better control and precision. It has been concluded by industry experts that a software testing company abiding an AI structure is capable of offering better learning and methods of self-correction. Since AI is directly dependent on Machine Learning (ML), it enables you to equip machines to act without being openly programmed.  Since 2014, the number of companies providing artificial intelligence in software testing has significantly increased. This trend was brought in by a majority of start-up companies. But gradually several established and popular software testing enterprises have supported AI-driven testing for creating a global noise.

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Role of AI in Software Testing

According to testers, the usefulness of Artificial Intelligence for amalgamating human and machine-level testing efforts needs to be realized. Moreover, several other factors should be understood that directly support the implementation of AI in QA testing; such as:

• Quick Testing Approach

With modern testing methods gaining popularity, manual testing is seen to be slow and costly. As a solution, AI is actively deployed because it automates different test processes. Such an action supports continuous testing and also increases its pace and precision. Furthermore, AI tools assist you in recognizing the number of tests required for investigating the changed code.

• Visual UI Testing

The best part about AI-driven testing is its effectiveness to test the user interface and its various contents. Under normal circumstances, these tests can be difficult to automate and might require human involvement. However, the deployment of an AI-based tool for visualization, you can easily spot disparities within images that are nearly impossible to be caught by the human eye. In addition, AI testing also reduces the manual effort to update the DOM or Document Object Model.

• Precise Bug Detection

The detection of bugs using conventional methods might turn into a tricky task. However, AI and machine learning in software testing can quickly tract defects within seconds. The power of artificial intelligence allows you to assess major errors quite efficiently. As a matter of fact, AI tends to automate testing processes thereby auto-correcting the codes and even determining duplicate defects.

• Automating Test Scripts

The automation of test scripts with AI occurs automatically with the help of the AI algorithm. Moreover, AI converts test cases to become more reliable. Since AI is brought into the scenario, test scripts evolve to achieve self-learning abilities. It is analyzed that AI can expediently receive mock responses directly from the server.

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• Better Test Coverage

The importance of AI can be judged by increased test coverage that simultaneously improves the functioning of the software program. AI can easily examine files, data tables along with overall memory segments. On the other hand, AI-driven test automation supports the running of a thousand test cases under a single test run! This is extraordinary and therefore makes AI quite essential for software testing processes.

• Future of AI Software Testing

At present, one of the prime issues with software testing is the phase of over-engineering results that can lead to loss of time and effort. It has been observed that companies aim to manage quick releases with the help of automation, but later struggle to cope up with the significant backlog. With the help of artificial intelligence, you can eliminate the backlog and improve the level of concentration towards the designated tests.

In addition, there is a bright future in terms of AI-powered testing stages for identifying changed controls. Technology experts are working towards the introduction of stable updates with the algorithms to carry out the identification task with higher precision.

AI has fastened the pace of software testing and is expected to do more wonders in the near future. To know more about AI and its impact on software development & testing, do connect with experts at ImpactQA. These professionals will carefully update you about the benefits of AI on software quality, optimization, and other important facets.

The post How does Artificial Intelligence Benefit Software Testing? appeared first on ImpactQA.