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.

Suggested Read

AI in Cloud Computing – Blend of Two Growing Technologies

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.

Suggested Read

AI in Cloud Computing – Blend of Two Growing Technologies

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 Elite QASolutions. 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.

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What is ad hoc testing?

Ad hoc testing is one software testing that can help with quality assurance. It is an informal testing method to find software defects and problems quickly. The ad hoc software testing method does not follow formal procedures but relies on testers’ experience and intuition. Think of ad hoc testing as a spur-of-the-moment testing service that finds software defects before release.

Ad hoc testing can be performed at any stage of the development cycle and testing process. However, it is usually done after all other tests have been completed. It is a type of testing that allows testers to explore software in an unstructured way, making it suitable for software development projects with tight deadlines.

Key benefits of ad hoc testing

Here are the key benefits of ad hoc testing:

It is cost and time efficient

Ad hoc software testing is a quick method that helps developers find program defects quickly. This means software projects can be released faster and at a lower cost.

It is flexible

As stated earlier, ad hoc software testing and methods do not follow formal procedures. This means software testers can use their confidence with ‘error-guessing’ to find defects.

It is useful for software projects with tight deadlines

Ad hoc testing can be performed quickly and efficiently, making it an excellent option for software development projects with tight timelines.

It uncovers bugs that aren’t identified with other formal software testing methods

Ad hoc software testing can uncover bugs not found during other tests. This means developers can release the program with fewer or no defects.

It enables developers and testers to work together

Ad hoc testing allow software teams to work together(buddy testing). As there are no written test cases, testers and developers can share ideas, discuss defects and collaborate efficiently on resolving issues.

No planning or documentation required

Ad hoc software testing do not require developers to plan software tests or documentation. This saves them time and effort, allowing projects to be completed faster.

Disadvantages of ad hoc testing

While ad hoc testing can be beneficial in certain software development situations, there are some drawbacks to using this method. Here are some of the disadvantages of ad hoc software testing:

It is not suitable for complex software projects

Ad hoc software testing are not suitable for complex software development projects. This software testing method makes it difficult to track defects, making software projects with complex software requirements difficult to manage.

It can be difficult to reproduce software defects

Since ad hoc software testing do not have written test cases, it can be difficult to reproduce software defects. This means software teams may be unable to track defects and solutions efficiently.

It requires experienced software testers

Ad hoc software testing require software testers with extensive experience. Thus, finding such talents can be challenging for organizations.

It has little to low accountability

Since software teams do not have written software tests, developers and testers may be less accountable for defects in the program. This can make software projects difficult to manage.

Types of ad hoc testing methods

There are three popular ad hoc software testing methods used in software development projects:

Monkey testing

This software testing method involves testers randomly inputting data into the software application and attempting to break the software.

Buddy testing

This software testing method involves developers and testers working together to test software applications. Both can provide valuable feedback to software teams.

Pair testing

This ad hoc testing method involves two testers working together on a single program. While one software tester executes the testing, the other records and takes notes on the defects.

Some might confuse pair testing and buddy testing; however, they are two distinct software testing methods. One needs someone from the development and testing teams present for buddy testing. For pair testing, one would only need two people on the QA team, but they should ideally have different levels of expertise.

When to avoid ad hoc testing

While ad hoc software testing can be useful in software development projects, they should be avoided in certain scenarios. Here are some scenarios where ad hoc software testing should be avoided:

Testers don’t have the expertise

Ad hoc software testing require software testers with in-depth knowledge and testing experience. Thus, software teams should avoid using ad hoc software testing if the testers do not have the necessary expertise.

UI testing

If the software is being tested to check user navigation and usability, software teams should avoid ad hoc software testing. UI software testing requires detailed software tests and documentation, making ad hoc software testing unsuitable for this software testing task.

High-risk software

One should avoid ad hoc software testing for projects involving high-risk software development, such as for programs developed for medical devices and mission-critical operations. Such software projects require structured tests, making ad hoc testing unsuitable.

Ad hoc testing best practices

When software teams use ad hoc software testing, they should adhere to certain best practices. Here are some best practices that software teams should consider:

• Have experienced testers carry out the test
• Make sure that the test is carried out under the right testing conditions
• Keep a checklist of software tests to perform and have notes on the process carried out during the testing

Conclusion

This article attempted to provide the readers with a comprehensive overview of ad hoc software testing. Ad hoc software testing is a useful software testing method that software teams should consider. However, software teams should be aware of the situations where ad hoc software testing services are inappropriate and adhere to the best practices.

If you are looking for a software testing company to carry out software testing services for your software project, then you should consider using Elite QASolutions’s software testing services. Their software testers have more than ten years of testing experience and can help you effectively test your software application. Contact Elite QASolutions today to find out more about their software testing services!

The post Ad Hoc Testing: Everything You Need To Know appeared first on ImpactQA.

What Do You Understand About Edge Computing?

Edge computing is a distributed/network computing strategy in which processing occurs near the point where data is collected and processed rather than on a centralized server or in the cloud. With this new architecture, you can access sensors that collect data and edge servers that securely analyze data in real time and on-site. You may also connect additional devices to the network, such as mobile phones, computers, etc.  

Data processing at the edge aids in the quick application of analytics and the deployment of AI capabilities, which is interesting to know. Furthermore, edge computing allows for seamless data transmission with no latency and lower network bandwidth and storage needs.  

  The usefulness of edge computing has been realized at the global level. The below-mentioned statistics say it all.  

• According to Gartner’s report, by 2022, 50% of enterprise-generated data will be generated and processed outside of centralized cloud data centers via edge computing.  
• Based on IDC’s Worldwide Edge Expenditure Guide, enterprise & service provider spending on software, hardware, and services around edge solutions will expand at a rapid pace through 2025, reaching around $274 billion.  

Edge Computing & Relationship with Cloud

Many question whether edge computing is a subset of cloud computing or vice versa. The fact is that edge computing works in tandem with the cloud to provide a customizable solution built based on data collecting and analysis specific to each enterprise. 

The edge is appropriate in terms of specific workloads for handling real-time data collecting and processing. Simultaneously, the cloud aids in the provision of a centralized site for large-scale analytics. They work together to provide real-time insights on performance and enable projects like machine learning. 

Why is Edge Computing Gaining Popularity?

• Supports the automation of all business processes
• Motivates operational capabilities  
• Presents a less costly strategy to achieve scalability
• Better performance & security across all devices
• Lowers bandwidth to offer low transmission cost  

Use Cases of Edge Computing

The implementation of edge computing can be seen in our day-to-day lives. Here are a couple of use cases that can prove helpful in understanding the value of this computing paradigm.

A. Edge Computing in Smart Homes

Smart homes have become the talk of the town, with several cities in the United States already benefiting from intelligent automation to make their lives as comfortable as possible. IoT devices are the primary elements of the smart home and are responsible for data collection and analysis.

The collected information is typically transmitted to a specialized remote server for storage and processing. However, the existing configuration may have several flaws, such as security, latency, etc. Edge computing decreases roundtrip time and simplifies processing and storage by allowing sensitive information to be analyzed at the edge. In addition, your voice-based assistance devices, such as Google Home, will reply to your orders more quickly, thanks to the presence of edge computing.  

B. Edge Computing in Fitness tracker

Consider the case of a fitness tracker. Its primary purpose is to keep track of the user’s heartbeat. When edge computing is used, it may be converted into a far more complex information-delivery device, such as providing data when the user’s heartbeat stops. It’s a life-saving breakthrough since the gadget can instantly contact an EMT service, which may provide rapid medical aid. Because edge computing produces low latency, the delay caused is minimal, allowing the EMT service to respond quickly.

C. Edge Computing in Smart Tunnels  

The development of a smart tunnel in Spain illustrates how edge computing can be combined with traffic safety measures. The smart Cereixal tunnel in Galicia uses edge computing and 5G technology. Data is collected and analyzed using cameras, sensors, and connected vehicles.  

The supervisors can remotely monitor what is happening inside the tunnel. Drivers entering the tunnel are also informed of potential delays, the presence of pedestrians, and any emergency vehicles requiring a quick passage. At the exit, drivers can also get information about weather conditions and traffic jams.   

Advantages of Edge Computing

• Better Speed: Edge computing offers improved speed and lower latency to permit faster data processing  
• Improved Scalability: It delivers a cost-effective path to scalability and adaptability, thereby permitting businesses to enhance their computing abilities  
• Security: Since a large amount of raw data is processed close to secure edge devices, it ensures data privacy and security when transmitting data across international borders.  
• Low Bandwidth Usage: Edge computing enables IoT apps to use less bandwidth, allowing them to function normally even when connectivity is limited

Disadvantages of Edge Computing

• Abide Regulations: For a business to stay on track, data is king. When the data/information is gathered at the edge server, it becomes imperative to check for compliances & regulations thoroughly.  
• Network Susceptibility: The networked architecture of edge computing increases known attacks. Such a system is susceptible to security flaws and malware infiltration.  
• Balancing Bandwidth: As more data is secured at the edge, more computers are required. As a result, the bandwidth must be increased. Therefore, the implementation of edge computing necessitates the balancing of network bandwidth.  
• Technical Issues: Since these Data Centers lack the comprehensive infrastructure of a typical Core Data Center, they must work around some technical challenges.   

How is QA Crucial in Edge Computing?

The tiered architecture of edge computing involves a lot of interdependent components. It includes:  

• Edge software
• Edge hardware
• Edge gateway
• Interconnection
• Cloud
• Data syncing  

The value of Quality Assurance (QA) may be evident during the authentication of the functionality for each of the components mentioned above and further testing to ensure that they operate in harmony as part of an integrated structure.  

 Some of the testing activities which are performed on edge components are: 

• Unit testing
• API testing
• Performance testing
• Database testing
• Integration testing
• Security testing

With ImpactQA, you can achieve quick QA and software testing service solutions for your edge systems. Our experts are dedicated to ensuring customer satisfaction and managing projects with meticulous analysis and reporting.  

The post Edge Computing: Implementation, Advantages, and Disadvantages appeared first on ImpactQA.

As technology spreads worldwide, existing tech hubs have developed an upgraded platform called the ‘Super App.’ Although the term was coined by Mike Lazaridis (Founder of Blackberry) in 2010, its impact in the real world has recently increased.

Lazaridis outlined it as a “closed ecosystem of two or more apps” that people can use to meet the demand for integrated services without switching between multiple software apps. In this mobile-first era, it can be described as a seamless experience.

Why Super Apps?

A recent survey found that 67% of all consumers (173 million people) need a super app to manage their digital functions. Such customers prefer a single platform that performs multiple functions, eliminating the need for many individual apps and online websites.

According to Gartner, a super app is comparable to a Swiss army knife that can be used whenever necessary and covers various components. Super apps are currently resonating worldwide as the smartphone generation advances the mobile-first experience. This factor directly expands the window in which super apps can demonstrate their ‘ease of use’ to a wide range of digital consumers.

Gartner also forecasts that by 2027, close to 50% of the global population will depend on numerous super apps as part of their everyday lifestyle. As a result, you will receive a perfectly balanced experience across messaging, payments, and workflow tasks on a single platform.

Reasons Why Supper Apps Gaining Popularity

Below are some of the prime reasons super apps are accelerating their foothold in the global digital consumer market.

Convenience as a Factor

The convenience factor, which is consistently ranked as a top priority for people, is mainly responsible for the success of super apps. Furthermore, the problem of storage is also solved because one software app can perform multiple tasks rather than having to download numerous apps.

Better Consumer Loyalty

The usability of a specific app is only determined when the user needs a particular service offered. However, if you use the app frequently, whether for placing a food order or purchasing goods, the usability factor improves, and the likelihood of uninstalling it decreases. As a result, a super app that does everything without switching between apps has a high usage rate that directly translates into better consumer loyalty.

Data Protection

Super apps lessen the chance that user data will be compromised, in addition to offering the convenience of a single platform login. Every time a new app is downloaded, the user is required to input personal information, increasing the risk of data access by hackers. For instance, ten of these apps would require ten different logins. However, if just one super app can take the place of all ten of those, the personal data is restricted on that one platform.

Example of Popular Super Apps

Hugo

Hugo began operating in Central America in 2017 as a food delivery and micro logistics business. Hugo quickly increased the quality of its services to satisfy customers over three years, expanding to six countries in Central America. The company currently provides a wide range of services, such as food delivery, ride-hailing, payment, grocery delivery, and on-demand services.

Rapi

Rappi was founded in 2015 and has expanded to over 250 cities in nine regions, including Mexico, Brazil, and Argentina. This super app currently has over 10 million active users per month. Rappi has grown into a billion-dollar company focused on rapidly delivering food, medicines, and clothing. Over time, the company’s business verticals have expanded to include a wide range of financial services and travel and hotel stays.

Grab

The business got its start in Malaysia as a taxi service. The brand name was changed in 2013 to be known as Grab finally. The company currently provides various services to over 187 billion users across eight different countries via a single application. You can use Grab to access everything from food delivery to courier services, ridesharing, payments, and hotel reservations.

Maintaining Quality of Supper Apps

Currently, the development of a super app is restricted to a specific technological niche. To increase value delivery, businesses must leverage advanced technology proficiency. Simply put, a super app must meet a user’s daily needs while balancing ease of use and seamless performance. The UI/UX are essential factors that must be considered for a digital consumer to return to your super app regularly.

Global industries are evolving, as are their needs for a unified app to cater to a growing consumer population. Moreover, the adoption of super apps carries a ‘flawless’ label, necessitating quality assurance. Therefore, it calls for the need for quality Engineering (QE) practices must be implemented in addition to a strong development team.

Each super app should be evaluated based on its functionality, speed, and system performance to enhance user experience. The best fit for improving user experience in the form of better retention and value is a good mix of intelligent process automation and RPA (Robotic process automation) practices.

Final Say…

Concentrating on the various features and components when creating a super app is essential so that it functions as a collection of unique min-apps. Maintaining user experience and data protection is necessary for the success of such a collaborative platform. The best way to do it is to work with a technology provider with upgraded expertise in the tools and methodologies used to develop a super app. Software testing and quality assurance procedures are only possible with a proficient team of software engineers.

Get in touch with Elite QASolutions, your trusted software testing services partner, to help you with a full-proof super app that supports your business goals. Contact us today to share your requirements!

The post Know Why Super Apps are on Emerging Rise? appeared first on ImpactQA.

Thanks to OTT platforms, online media consumption is now more popular than traditional cable TV. Once the only video content provider, Cable TV is now available on OTT. However, OTT platforms’ performance is judged not just by the content they offer but also by how seamless and smooth the user experience is on the platform. The success of any OTT platform heavily depends on its content delivery system, which is determined by how the platform is designed. Quality engineering and testing must be prioritized to achieve a highly efficient design. Keep reading to learn how this can be done.  

Role and Importance of OTT Platform Testing  

Consumers are using different OTT platforms to watch the content. They take several media subscriptions at a time. Today there are more than 110 media providers in the market. Every day, competition increases, so every provider wants to differentiate themselves.   

OTT testing involves testing data, voice, video content delivery quality, and user experience through software performance engineering. Behind one application of OTT, several application components, network, and infrastructure setups are linked together. Hence, different types of testing are also involved in testing OTT platforms. These applications are tested for the following capabilities:  

• User-friendly and interactive interface  
• Compatibility with several devices/browsers  
• Content selection  
• Feature and functionality testing of the app
• Personalized recommendations for the content  

OTT platforms should be thoroughly tested to offer excellent user experience and deliver high and good quality content through performance engineering. High-quality video streaming is also an essential requirement. These systems are usually tested for the following capabilities:  

• Playback Quality: Content delivery should not take high streaming time
• Consistent Delivery: There should be stable delivery of content  
• Compatibility: The OTT applications should be compatible with almost every device.   
• User Accounts Handling: A subscription can usually be accessed by more than one user on different devices. Therefore, OTT users should be able to set up multiple individual accounts with personalized recommendations and watchlists.
• Offline Accessibility: Sometimes, due to network connectivity problems, users cannot access data online. For this, OTT gives the feature to download and view content later. The apps can therefore be tested for offline mode performance as well.  
• Smart Recommendations: The OTT applications should be monitored to track the users’ watch history. It helps recommend suitable content to them and increase their interest.  

While most viewers use TV, laptops, or mobile phones to consume video content, recent surveys state the use of mobile phones has been rapidly increasing. It makes it crucial that OTT applications are tested for mobile platforms. The next section states developers’ challenges while testing OTT apps for mobile phones.  

Challenges in OTT Testing  

Today, most content is delivered through streaming rather than rentals or satellite TV. Therefore, providing quality service to users through OTT platforms is essential. In addition, OTT testing ensures that all features of the app work perfectly and ensure a seamless user experience.  

Listed below are some common challenges faced by OTT testers:  

Cross-Device Compatibility

Ideally, OTT platforms should work with all internet-enabled devices, including computers, smart TVs, smartphones, tablets, and more. However, testing OTT application on several devices is a big challenge because the tester may need access to all devices and operating systems.  

Reduced Response Time in Different Networks 

Internet speed is crucial to run an OTT service uninterruptedly. If the data transfer rate is low, buffering takes longer, which can potentially compel users to switch platforms. Therefore, it is essential to test OTT platform performance on various networks.  

Traffic Overloads 

Famous events and streaming shows may cause high traffic, and traffic overload can lead to several glitches, resulting in negative feedback from customers. Moreover, the traffic load is usually unpredictable, which makes it challenging for testers to take precautions beforehand.   

The testers may face these and some other challenges. But application testing is an essential step and can be overcome as well. So here are some ways to overcome those challenges we will discuss in the next section.

How to Overcome Testing Challenges?  

Now that we know the challenges faced while testing OTT platforms let us look at how these can be managed or done away with.  

Automated Testing

Most QA issues can be handled and resolved through automated OTT testing. Automated testing helps in the following ways:  

• Maximize the test coverage  
• Scale the testing process without hiring additional testers  
• Faster debugging in comparison to manual testing  

Cross-Device and Browser Testing

It involves testing the application for different browsers as well as devices. Ensuring the OTT application works across other platforms eases the rendering process.

Load Testing

OTT testers must also test the load limit of the applications. This way, they can prevent the application from crashing during times of high demand. In addition, load testing helps maintain the stability of the application.

Apart from this, several tests like functional testing, UI/UX testing, security testing, performance testing, and integration testing can be carried out to test OTT applications. 

Wrapping Up…

The rise of OTT platforms has been significant, and so is the demand for quality check before they are made available to global users.  It is ideal to have a trusted reliable software testing company with OTT platform expertize to ensure app quality. Contact Elite QASolutions, a leading quality engineering expert that has expanded its service offerings in the media and entertainment industry. 

The post Eliminate OTT Platform Flaws with Quality Engineering appeared first on ImpactQA.

In a world where globalization is more common, ensuring that the software works the same for all types of audiences is crucial to its success. The testing team comes up with different testing methods to ensure that the software’s performance, quality, and functionality remain consistent for different audiences. Multilingual software should be linguistically and culturally appropriate when used in other countries. Localization testing services help identify gaps and defects concerning user interface, functionality, translation, features, and much more.

Understanding the Difference between Globalization and Localization Testing

Choosing the correct type of testing is crucial to reduce the time to market software products. Only a well-tested product will deliver features and benefits to the end users. The testing methodology chosen should take just a short time to execute. It should ensure that the gaps are adequately addressed before the product is released to the audience.

Scope

Globalization testing ensures that the software application runs properly in local or cultural environments. It is widely used for testing software that provides multilingual support. The testing ensures that the software works in every language and that different attributes deliver the same functionality in other languages. For example, Google supports searching in multiple languages; therefore, considered a global application.

Localization testing services ensure that the software performs well in specific cultural and geographical environments. It tests the software usage in a particular language and for a specific region. Apps only available in local languages and for a particular region will benefit significantly from localization software testing. For example, web services such as QQ are available only for the Chinese audience. It is a local application.

Language

Globalization testing tests application support for multiple different languages. Localization testing tests the software application for a specific language and its dialects, if applicable.

Currency

Different currency formats, number formats, and address formats supported by the software are tested in globalization testing. Localization software testing assesses support for a specific currency, mobile number format, and address format.

User Base

In globalization testing, the software is tested to ensure that it caters to the needs of a generic user base in the global scenario. Localization testing ensures that the software meets the goals and demands of a specific set of users in a particular culture.

Testing Environment

For globalization testing, the tester should set up multiple testing environments because the software is intended for use worldwide by different groups of people. Localization software testing is a targeted testing approach where the software features are tested for users from a specific locality. Ideally, a region-specific testing environment should be set up for practical testing.

Validation

The software must be validated for resource attributes and linguistic accuracy for every language supported for globalization testing. Adaptability and compatibility for each region should also be tested.

Compliance

Globalization testing tests the software for compliance with multiple regulations and rules for different countries. Localization software testing helps ensure that the compliance and regulations imposed by the local bodies are carefully met.

Bug Reporting

In globalization testing, formalized bug reporting is essentially included. Localization software testing doesn’t support bug reporting.

Time taken

As globalization testing is comprehensive, it takes more time. Localization testing has a limited scope, and thus, it takes less time.

Pros and Cons

Globalization and Localization Testing Challenges

The methodology for both globalization and localization testing services must be flawless to create a cost-effective and valuable product for the end users. The software functionality must be uniform across multiple languages when providing multilingual support. For localized applications, they should meet the expectations of the targeted audience.

The linguistic requirements for globalization testing are vast and specific. Depending on the functionality required by the software, linguistic experts may be needed for the accurate translation of software features. In the case of localization testing services, the target audience may sometimes be inaccessible. Knowing the preferences of the local audience require extensive research and collaboration with local agents. A thorough understanding of the value and culture of the region is essential.

To improve the universal usability of your software application without any difficulty, get in touch with an expert to perform localization testing services. Count on Elite QASolutions to take care of your needs for localization software testing.

The post Globalization Vs. Localization Testing appeared first on ImpactQA.

The use of accessibility testing tools is determined by a variety of factors, including the site’s budget and requirements. Here is a list of the top manual and automated accessibility testing tools for website accessibility testing in 2022:

AChecker   

• AChecker is a holistic testing tool used to assess the HTML content for a single web page.   
• You can test your web pages by uploading an HTML file, entering the URL, or pasting the source code straight into the tool.   
• AChecker offers the option to evaluate the website using different accessibility guidelines such as WCAG 1.0, WCAG 2.0, HTML Validator, BITV 1.0, and Section 508.  
• This tool can be both used online and offline simply via installation.  

WAVE   

• It is a community-built free tool designed to facilitate web accessibility testing by offering developers a visual representation of problems directly on their page.   
• WAVE is a browser-based tool that assesses a load of issues, ranging from color contrast to ARIA attributes, and is based on compliance standards from Section 508 and WCAG.   

JAWS (Job Access with Speech)   

• It is the most popular Screen Reader tool for lost vision customers. Some of the good features of JAWS comprise two multi-lingual synthesizers, Viz. Vocalizer Expressive and Eloquence.   
• It works with Microsoft Office, IE, and Firefox and supports Windows with its touchscreen gesture.

Suggested Read

How to Conduct Accessibility Testing on Android Devices?

Dynomapper   

• Dynomapper is a Visual Sitemap Generator of 4 types Circle, Tree, Folder, and Default. It evaluates the HTML contents of the site and can generate a sitemap from any URL.   

• It imports XML files to generate the sitemap. It also provides content audit and inventory for filtering pages, images, and files.  

aXe 

• Like WAVE, aXe has extensions for Firefox & Chrome; however, the aXe extensions are more developer focused. The aXe extension is an incredible tool for developers. When you run the report on a page, you get a list of all flaws.   
• It works on all modern browsers and supports static fixtures, in-memory integration tests, and iframes of infinite depth. It is designed to work with whatever libraries, frameworks, environments & tools you have today.   

Siteimprove  

• Siteimprove is a highly effective website monitoring tool for accessibility and quality assurance.   
• The best thing about Siteimprove is that it offers a cohort of skilled accessibility testers who can deliver both manual and automated testing solutions.  
• Through a team of accessibility testers, Siteimprove addresses Office of Civil Rights (OCR) compliance and successfully resolves OCR complaints.  

• You also have access to a thorough audit report outlining corrective measures and new content planning.  

Equally AI  

• Equally AI is regarded as a top-tier automated web accessibility compliance tool that is well-known for assisting users in ensuring that their websites and apps comply with all legal requirements for web accessibility.   
• The tool can offer a pleasant and reliable experience for disabled users.   

• Equally AI provides a straightforward user interface that includes specific display options, making testing easy in different accessibility scenarios.   
• The tool’s mobility solution allows you or your users to test the accessibility of your website using only a keyboard.  
•  Equally AI is managed by a dedicated team of testers, who ensure this tool is used correctly.  

Google Lighthouse  

• Google Lighthouse is an open-source platform that automates website accessibility testing through Google Chrome.  
• It is available in Chrome Developer Tools and does not require additional extensions or configurations.  
• Lighthouse performs a variety of audits that cover a wide range of WCAG requirements, such as missing alt text, lower color contrast ratios, and so on.  
•  This tool rates your website’s accessibility after each round of auditing and provides recommendations for improving it.  

SortSite  

• SortSite is a popular one-click user experience testing tool for Windows, Mac OS X, and Linux.  
•  The tool is fully compatible with desktop, Internet Explorer, and mobile browsers.  
•  SortSite evaluates your website’s accessibility following existing accessibility standards such as WCAG 1.0 85, WCAG 2.0 110, and Section 508 15 US 47.  
•  This tool is helpful because it efficiently checks for HTTP error codes as well as script errors. Additionally, it validates CSS, HTML, and XHTML.  

UserWay  

• UserWay is an innovative accessibility compliance tool that employs artificial intelligence to guarantee that the website or application provides a wonderful user experience to those in need of accessibility assistance.   
• It can be quickly added to your website as a plugin to provide an aesthetically pleasing and user-friendly interface.  
•  By allowing you to move the widget and alter its size, position, or color, UserWay also lets you personalize the user interface.   

•  You can use UserWay to scan the websites and apps for violations of not complying with the legal requirements for accessibility.   

Wrapping Up  

The purpose of website accessibility testing is to identify and correct all compliance issues. The tools listed above help improve the overall usability of your website or application to assist disabled users. You can try looking for credible accessibility testing services that can assist you in resolving various website accessibility issues. Trust Elite QASolutions to take care of your project needs!

The post Best Tools for Website Accessibility Testing in 2022 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.

Suggested Read

5 Ways AI is Shaping the Future of Software Testing

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.

Suggested Read

Guide to Successful AI/ML System Testing

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.

The global ERP software market will reach $100k million by 2025 (Statista). An ERP system can help your business:

1. By making it more productive
2. Streamlining the processes
3. Increasing visibility into the different departments
4. Removing bugs from your existing systems

Testing your ERP system for performance and load as part of the implementation is essential. It will help you know if your system is ready to go live.

Read on to learn about the benefits of stress tests, how they are used in real-world scenarios, and the process.

Importance of Stress Testing in ERP Systems

When implementing an ERP system for your business, do it in an isolated environment. As a business, there is limited scope to carry out ERP testing for extreme load situations. You may not be able to judge if your system can perform under varying conditions. The stress test will learn how your system endures the different variables.

Let’s discuss why stress testing is essential.

Effective Performance

It will identify how your system performs when the traffic increases. Furthermore, it helps answer questions like:

• Can the system work in abnormal conditions?
• What happens when the system fails?
• How long does it take for the system to recover?

A typical use case:

• Healthcare: The practitioners add all appointment-related data to the ERP. In case the system fails, they miss necessary appointments. They may not be able to add important information related to the patient to their records. It can cost them heavily going forward.
• Retail: The user will not get informed about the stockouts in real-time.

Test the Limit

Every ERP system is designed for a specific number of customers. When the numbers exceed, the system breaks down. By testing the limit, you will know:

• The maximum number of customers that can simultaneously work on ERP
• The limit for requests sent to the system.

When you know the limit, you can accordingly plan the operations so that the ERP system stays active and available.

A typical use case:

• Healthcare: Say a person is viewing the records while another is editing. At this point, if the limit of users has exceeded, then the algorithm shuts the data for one person. It offers limited accessibility, which can harm the experience.
• Manufacturing: When the system shuts down or offers limited access to one department, it hampers their work and productivity.

Test the Scenario

The stress test can help judge the system for different conditions. You will know if the system can handle 100 requests in a day or not. It will help us understand what kind of transactions the system can handle.

A Typical use case:

• Retail: Your ERP is used in PoS to manage different payment transactions in retail.
• Multiple users can try payment at the same time using different payment options.
• It can increase the load on the payment systems.
• The retail business should test the ERP for this scenario to ensure a smooth payment experience.

Practice Caution

You can define the preventive methods in case your system reaches the breakdown limit. You can conduct code cleaning or database structuring based on your test outcomes.

A Typical use case:

• Manufacturing: If two engineers are assigned to assess the machine performance and maintenance records manually, the data will differ owing to several factors. This can further impact the manufacturing processes, which can hamper the experience. Your system should be ready to embrace these issues and identify the solutions.

Ensure Security

With stress testing, you will know whether or not your system is vulnerable. It will help you tighten the security during implementation for the best outcomes.

A Typical use case:

• Retail: If your retail ERP system is not checked for external applications and data exchange, you may notice vulnerabilities in the system. Ensure you test for this scenario and mitigate the security risks.

The Stress Testing Requisites

As a software testing company, you should schedule and organize a thorough stress testing before going live with your ERP system. Here are the requisites to focus at during ERP testing.

1. Planning Document: Create a planning document that comprises every aspect of stress testing for the perfect outcome. The planning document should include:
2. Transparent Communication: Setup communication protocols for accessible communication
3. Similar Server Environment: The company should conduct the testing on a server similar to your production environment.
4. Network and System: Ensure the network strength is identical to the production environment
5. Tests for Scenarios: Plan the different scenarios and test your system for the same.

The Best-fit Methodology for Testing ERP Systems

A proper process and methodology should be in place when conducting a stress test of the ERP systems. You should adopt the best practices that can improve your test outcomes.

Define the Objectives

When planning stress testing for the ERP system, you should determine the test’s purpose. Identify the objectives you aim to accomplish with this test.

It will help determine the test case scenarios, documentation, and test tools. You can also define the team when you are clear about the objectives.

Create the Governance Structure

The administration of stress testing should be clear and defined.

• You should first determine the team members, roles, and hierarchy
• The team will be responsible for conducting the stress test
• They will be overlooking all aspects of the test

Every person within the team should be clear about the roles and responsibilities they share. Having a person to manage the entire test is equally important.

Identify the Risks

As a software testing company, you should identify the risks of stress testing your ERP systems. Know the parameters that can lead to vulnerabilities in the system. Account for issues that can occur with the testing systems.

Once you know the issues, you should focus on mitigating the risks. You should also capture the material and infrastructure-related risks when determining the tests.

You can determine the risks and mitigate them when planning the test cases.

Conclusion

Stress test for ERP systems is an essential aspect of implementation. It will help you realize the system’s performance in the real world. You will know if your system can handle a sudden increase in load, breakdown, and limited availability of bandwidth.

When performing stress testing for ERP systems, there are several factors to consider. Partnering with the right ERP testing service provider like Elite QASolutions is crucial to ensure you follow the best communication protocols, identify the risks, and plan for the tests.

The post Importance of Leveraging Stress Testing for ERP Systems appeared first on ImpactQA.

Due to their independence from one another, testing microservices using test automation services can lead to streamlined benefits. When working with outdated systems or when the system architecture is goal-oriented, success with microservices is greatly enhanced.

In this article, we will attempt to comprehend the microservices architecture and look into the pros and cons of its implementation.

Introduction to Microservices Architecture

Microservices design is breaking down an application into many smaller services, each of which runs in its own process and communicates using lightweight protocols such as HTTP, AMQP, or XMPP. The best part is that these services can be simply built and implemented independently of one another. Microservices also have a self-owned data model, allowing them to manage their own database.

Microservices can be used in enterprise systems for a variety of tasks. This covers the creation of new features, application upgrades, etc. Microservices are typically implemented in tandem with containers to enable teams to bundle and operate their services independently. Containers are designed to make it easier to deploy your services across various environments.

Another way to understand the benefits of microservices architecture style is by comparing it with the monolithic style- a monolithic app created as a single entity.

Monolithic applications are no longer proving to be useful for users as more apps are being moved to the cloud. When using a monolith, the change cycles become entangled, making it difficult to keep a good modular structure for an extended period of time.

This issue is thought to be resolved by the microservice architectural style, which calls for the development of applications as collections of services. The ability to independently deploy, scale, and exhibit a strong module boundary for each service allows for the creation of distinct services in various programming languages.

Suggested Read

Top 5 Ways to Automate Microservices Testing

Advantages of Microservices

Enhanced Scalability

The microservices architecture allows for the autonomous design and development of each service. As a result, the team can update the relevant microservice, which is in charge of that functionality, if any particular piece of the application has to be updated. This feature enables a smooth upgrade procedure and efficient operation of the software system.

Simplified Maintenance

Updates to microservices are significantly simpler than updates to monolithic applications because they are compact and self-contained. Additionally, there is a lower possibility of problems happening when changes are performed because each microservice is accountable for a certain task. This reduces the risk and time involved in maintenance and updating significantly.

Fault Isolation for Reduced Downtime

If a particular microservice undergoes a problem, you can isolate it to just that microservice and stop compounding failures from causing the application to crash. Because of this fault isolation, even if one of the important components of your application fails, it can still function.

Deploy Multiple Technologies

When developing new services, development teams have the freedom and flexibility to experiment with new technologies. It is critical to understand that the modules have less technological dependencies. Also, returning to previous iterations is less difficult.

Disadvantages of Microservices

Increased Network Traffic

Because they are designed to be independent components, microservices rely heavily on the network to communicate with one another. This may result in increased network traffic and long response times. Finding problems might also be difficult when numerous microservices are interacting with one another.

Issues with Global Testing

To be honest, testing a microservices application can be difficult. In a monolithic paradigm, all that would be required of us is to ensure that our WAR and the core database can communicate before deploying it to an application server. Microservices must be confirmed for each dependent function before testing can begin using them. This is considered as a prominent downside of microservices.

Complex Communication Among Services

The communication overhead increases when an application is broken up into multiple smaller parts. Developers must proceed with utmost caution when handling requests between the different components. There may be a need for an interpreter if various systems interact in different ways. This could make installing the entire system at once more difficult.

Extended Development Time

Since microservices are more intricate and require more collaboration than monolithic systems, they take longer to develop. Moreover, because each microservice is deployed separately, it takes much longer to get them all up and running. Additionally, developers must be knowledgeable in a wide range of technologies in order to focus on a microservice-based solution.

Conclusion

As stated in the text above, understanding the advantages and disadvantages of microservices is essential to making an informed decision about whether to deploy them in your enterprise systems. Microservices are a favored SOA over monolith due to improved performance and technology consumption. However, several disadvantages, such as the difficult deployment process, should be taken into account.

It is best to work with a knowledgeable microservices testing services company if you want to implement microservices architecture within your business. Join hands with Elite QASolutions which is a global leader in offering smart solutions around microservices deployment & testing to enterprises, SMEs and start-ups alike.

The post What are the Advantages and Disadvantages of Microservices? appeared first on ImpactQA.

87% of companies have embraced agile methodology for testing their software solutions. By implementing agile methods right from the start of SLDC (Software Development Lifecycle), businesses have a tight hold on the bugs, thus ensuring that code-cleaning is more efficient. To learn why agile methodology can enhance testing outcomes and how to integrate QA into the agile methodology, continue reading.

Reasons to Embrace Agile Methodology

For the longest time, developers and testers had worked in silos. As a result, the QA team only got to start work after developers finished building a product. Although time-tested, this method carried significant risks:

• If the testing team identified the bug, the development team had to rework the code. In other words, one team could begin work only when the other team finished theirs. Hence, project timelines got stretched.
• The teams were forever uncertain about when was a product launch likely.

So, how has agile methodology transformed the landscape? Listed below are some of its primary benefits:

• Teams no longer work in silos; Instead, they communicate internally on issues and setbacks, thus working towards a common goal.
• With developers and testers working closely, bugs resolve faster. Hence, this approach allows products to launch without delays.
• With bugs identified before a product launch, companies do not worry about quality issues.

However, the software testing company must be well versed in the best practices and steps to implement the agile methodology in QA

Suggested Read

Agile Testing: Best Practices and Methodology

How to Implement QA in Agile Methodology?

Here are ways to implement QA in agile methodology, smoothly:

Determine the Process

When teams embrace agile methodology, testing and development are simultaneous.
The development process gets divided into smaller iterations. Once the build is submitted at the end of every iteration, the QA team starts working on it.
The first step to integrating QA into agile methodology is identifying the iterations, determining the gaps, and executing the test cases. Once the stages are defined, the development and testing teams collaborate to build the software solution.

When defining the process, companies must ensure that the build and feedback move seamlessly between the development and testing teams. This will help the two teams finalize the build at the earliest and enhance the outcome.

Know the Users

Who is the end-user of the software solution? Defining the target market, identifying their personas, building images, and testing use cases are essential.
Planning the tests for a pre-determined target group helps develop a focused approach.

Analyze the Risks

Companies might face several risks when setting up the QA team with a new methodology. Hence, identifying the risks, and analyzing and mitigating them is crucial.

For instance, the end goal for any team is to create a bug-free product. However, one or several bugs may go unseen despite thorough QA checks. As a result, the teams must understand and assess various probable bugs. This understanding will help them mitigate the risks of unseen bugs. Additionally, it will also help them avoid problems during development.

After all, the QA team’s work doesn’t end at the launch of the application. They will continue working on resolving issues and patching bugs for better experiences.

Automate the Tests

It would help if the teams integrated automation with the correct methodology to improve test quality. While a few tests still require manual tools and techniques, several can be automated. Regression testing is, by far, the most tedious and time-consuming aspect of QA. It would help if specific iterations were automated to ensure the team members don’t spend much time testing the software. With the tools producing the test cases, the team can focus on the most critical tests.

Test automation solutions can help accelerate bug identification and resolution. If there are too many variables or cases where the users cannot reproduce the tests, they should opt for manual testing.

Early Testing Wins

As discussed, implementing QA at the start of the SLDC is one of the essentials. The agile testing company or provider should get to work as soon as the developer commits to the project’s first phase. This will ensure that the bugs are removed immediately. When the team tests smaller iterations, cleaning the code and maintaining the quality is easier.

However, testing early isn’t the only solution; tests should occur as frequently as possible. If the developers have committed two builds, the testing teams test the co-relation to ensure smooth outcomes.
With this agility, they can test new features as soon as they are introduced. They can even save time and money by resolving problems and mitigating risks.

Solid White-box Testing Methods

When integrating QA with agile methodology, ensure the testing teams know the potential problems and outcomes. Planning will help them determine the risks and acknowledge the solutions easily.
For instance, they would have anticipated the issues that could cloud their judgment and would be aware of the different test scenarios and the potential outcomes. Hence, going into testing with an open and knowledgeable mind can help determine the errors and resolve them.

The idea behind implementing agile methodology is to ensure all departments work closely. Companies should opt for white box testing, as it anticipates error conditions, thus helping teams identify different scenarios.

Conclusion

Moving from a traditional methodology to agile is not the only guarantee of transformation. Businesses must change mindsets, adopt a defined approach to implementing the change and make it more streamlined.
Thus, partnering with an expert agile testing company to integrate QA into testing processes is essential. They can help one manage the integration in a phased manner.

EliteQA Solution is an expert in agile testing services having a vast experience with global clients. They have delivered capable testing solutions and mitigated risks for several business solutions. Businesses can connect with them to integrate QA into agile for the best outcomes.

The post How To Integrate QA Testing In Agile Methodology? appeared first on ImpactQA.

If your organization has entered the realm of digital transformation and wishes to make that big leap, Robotic Process Automation (RPA) is the way to go. RPA helps to automate high volume, repetitive tasks to improve process accuracy, service efficiency and better cost savings.  

 But what if RPA turns disruptive? 

Although RPA is seen as the go-to method to improve business processes, if it’s deployed in isolation, the chances of operations getting disruptive are high. This translates into highlighting one of the biggest challenges to RPA testing services is spotting the appropriate process to automation, since automating a lawed or dysfunctional process can amplify inefficiencies.   

Intelligent process mining is the ideal remedy for augmenting RPA. By extracting vital information from data, it is a technique for identifying, evaluating, monitoring, and improving business processes in order to get rid of bottlenecks and redundancies. 

 Let’s examine intelligent process mining in detail and see how it helps to improve automation processing. 

 What Exactly is Intelligent Process Mining?

The phrase “intelligent process mining” is widely used to refer to process mining that is AI- or ML-powered and emphasizes the use of specialized software that makes use of artificial intelligence. Reconstructing a virtual picture of the business process is one of the main advantages of intelligent process mining for global organizations. Process mining solutions make use of log information produced by enterprise systems like CRM, ERP, HCM, etc.  

There are three levels to process mining which are identified as:  

Process Discovery– It can be called the most widely implemented version of process mining. Discovery covers the utility of event log information to independently create a process model. Such a process basically leads to the building of a simulation (digital twin).  

Process Conformance– The goal of conformance is to determine whether or not the finished process model has been implemented in practice. Any variances from the desired model can be identified by comparing the process description to a current process model that focuses on event log data. 

But what if RPA turns disruptive?  – The final level of process mining involves extending or improving a model using data from an event log or any other data sources.  

Value Add to RPA Using Intelligent Process Mining

Looking at the emerging intelligent automation technologies, the imperativeness of intelligent process mining is gaining supreme prominence. To support this statement- according to a Gartner poll, eighty percent of finance experts believe finance must dramatically accelerate its deployment of digital technology such as RPA and artificial intelligence to better satisfy the business by 2025. In fact, CFOs consider investing in process mining to be critical in maximizing the technology’s returns when using RPA.   

The power of process mining provides a complete blueprint of a company’s existing (as-is) arrangement of processes. The RPA (Robotic Process Automation) team can use this knowledge wisely to construct effective automation. 

Process mining software plays its part in providing pre-automation historical data points together with the upstream and downstream effects of automation. On the other hand, RPA solutions lets you evaluate post-automation markers of precision and productivity. 

With intelligent process mining, machine learning and AI aid in the automation of process mining activities and the simplicity of modelling and prediction attributes. You might find it a mix of data analytics and intelligent test automation; however, it’s not similar. Machine learning methods, for example, enable users to analyze and uncover business processes using Java, Python, R, and other programming languages.  

When human interventions are involved, the advantage of constructive process intelligence combined with RPA is nothing short of providing value. This includes:  

• Recognize any inefficiencies that are not known or clearly evident  
• Determine ineffective human-digital manual tasks or vice versa 
• Detect robotic process enhancements that can eventually free up digital workforce cycles  

• Deliver quantitative data focused on the financial impact of digital workforce based on process 
• Strike a comparison on the basis of cost, efficiency, and productivity 

Suggested Read

Top Challenges to Enterprise Test Automation & Ways to Break Them

 Utilize the Right RPA Opportunities

At the moment, the most persistent business difficulties revolve around comprehending processes and determining the priority of high-grade vs low-grade activities. According to recent statistics, the various methods involving the identification, prioritization and documentation account for about half of the bot development lifecycle. 

Here are four important frequent RPA difficulties addressed by an Intelligent Process Mining system, which improves both implementation and at-scale sustainability: 

•  Delivers a unified end-to-end view of real process execution across several business applications in order to identify prime automation opportunities as well as any side effects. 
• Allows for measurable, data-driven return on investment calculations based on: the number of transactions; the number of process stages; the process AHT/TAT (time); and the cost per transaction. 

• Identify high-value automation candidates quickly using actual process execution data that shows all process variants as well as time and cost impacts. 
• Eliminates the need for time-consuming, costly, and often subjective manual process review.

Final Say

Regardless of the number of systems utilized to hold your data, a Process Intelligence platform can help you enhance every area of Robotic Process Automation — from start to finish. Converting process data from a variety of IT systems into actionable insight leads to more accurate automation decisions that are made faster and at a reduced cost. Intelligent Process Mining provides the assurance that data-driven decisions will have a long-term influence on any area of service delivery. In brief, Intelligent process mining technology allows you to ensure that your processes run at optimal efficiency in less time and at a lesser cost.  

To run and administer such high-end automation technology for your enterprise systems, you need a skilled process architect. Elite QASolutions provides RPA testing services managed by experienced professionals. One of the primary advantages is the incorporation of intelligent process mining for accurate automation. Contact us today to Discuss Your Project requirements!  

The post Intelligent Process Mining for Better RPA Results appeared first on ImpactQA.