Exploring The Future of AI-Driven Testing In EdTech

Education Technology, or EdTech, has rapidly transformed the way we learn and teach. From interactive online learning platforms to virtual classrooms, technology has become an integral part of the educational landscape.  

According to the reports, the global edtech and smart classrooms market grew from $140.24 billion in 2022 to $162.89 billion in 2023 at a compound annual growth rate (CAGR) of 16.2%. Besides, the numbers are likely to expand with a likely reach of $304.63 billion in 2027 at a CAGR of 16.9%.

Nevertheless, with the constant evolution of EdTech, the role of artificial intelligence (AI) in the field has become increasingly prominent. One area where AI holds great potential is in EdTech and assessment.  

Thus, it becomes crucial to delve into the future of AI in EdTech Testing and explore how it goes beyond simply detecting bugs to revolutionize educational evaluation. Doing so could not only help EdTech in mapping the likely progress but also the impact AI testing could offer to the growth of EdTech industry.  

The Future of EdTech: Trends & Possibilities 

The future of EdTech (Educational Technology) is likely to be filled with exciting advancements and transformative changes in how we learn and teach. Here are some key trends and possibilities for the future of EdTech: 

1. Personalized Learning: With the help of artificial intelligence (AI) and machine learning algorithms, EdTech companies and platforms will be able to deliver personalized learning experiences tailored to each individual’s strengths, weaknesses, and learning style. This will enable students to learn at their own pace and focus on areas that need improvement. 

2. Virtual and Augmented Reality: Immersive technologies such as virtual reality (VR) and augmented reality (AR) have the potential to revolutionize education. VR can transport students to virtual environments, making abstract concepts more tangible and engaging. AR can overlay digital information onto the real world, enhancing classroom experiences and providing interactive learning opportunities. 

3. Gamification: Gamification techniques, such as incorporating game elements and mechanics into educational activities, can increase student engagement and motivation. EdTech platforms will likely leverage gamification to make learning more enjoyable, rewarding, and collaborative.  

4. Adaptive Assessments: Traditional exams and assessments may be replaced or supplemented by adaptive assessments that use AI to analyze and evaluate students’ knowledge and skills in real-time. These assessments can provide immediate feedback and adapt the difficulty level based on individual performance, promoting a deeper understanding of concepts. 

5. Collaborative Learning: EdTech tools will continue to facilitate collaborative learning experiences, allowing students to connect and collaborate with peers from different locations. Virtual classrooms, discussion forums, and project management tools will enable students to work together on projects, fostering teamwork and cross-cultural exchanges. 

6. Data-driven Insights: The collection and analysis of data in EdTech will enable educators to gain valuable insights into students’ learning patterns, preferences, and progress. This data can inform instructional strategies, identify areas for improvement, and personalize learning experiences further. 

7. Ethical Considerations: As EdTech becomes more pervasive, it will be important to address ethical concerns such as data privacy, security, and algorithmic bias. Striking the right balance between technological advancements and ethical considerations will be crucial for the future of EdTech. 

All in all, EdTech has the potential to bridge educational gaps and provide access to quality education for students worldwide. Online learning platforms and digital resources can reach underserved communities, empowering individuals who lack traditional educational opportunities. 

However, it’s important to note that while these trends and possibilities paint a promising picture for the future of EdTech, the successful implementation and adoption of these advancements will depend on various factors such as infrastructure, affordability, training for educators, and the evolving needs of learners. 

Read more: Future Of AI-driven Testing in EdTech

How To Test Native vs. Web vs. Hybrid Apps

Mobile technology has revolutionized the way we live, work, and interact with the world. With the exponential growth in smartphone adoption, mobile applications have become an integral part of our daily lives, providing us with access to information, services, and entertainment at our fingertips. Thus, the importance of mobile technology lies in its ability to enhance communication, productivity, and convenience on a global scale. 

Besides, it is the introduction of mobile applications that has created a significant impact on various industries, including healthcare, finance, education, transportation, and entertainment. From enabling seamless access to critical services to facilitating remote collaboration and promoting digital inclusion, Mobile apps have made way for new business opportunities. 

According to Grand View Research, “the global mobile application market size was valued at USD 206.85 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 13.8% from 2023 to 2030.”  

However, the success of mobile technology heavily relies on the reliability and performance of mobile applications. This is where mobile app testing comes into play.  

Rigorous mobile app testing allows developers to ensure that applications function as intended across various devices, operating systems, and network conditions. Besides, access to advanced approaches like mobile test automation helps identify and fix potential bugs to deter security vulnerabilities, and compatibility issues for a seamless and satisfying user experience. 

In this blog, we will walk through the various dynamics of mobile app testing while diving through various tools and techniques that simplify testing of native, web, and hybrid apps.  

Let’s jump in.  

Testing Native vs. Web vs. Hybrid Apps 

Testing native, web, and hybrid apps involves different approaches and considerations due to their distinct characteristics. Therefore, it becomes necessary to align with the right approach and strategy in order to ensure maximum output of the testing efforts made.  

Here’s a breakdown of how to test each type of app: 

Testing Native Apps 

1. Device-Specific Testing: Since native apps are built specifically for a particular operating system (e.g., iOS or Android), it’s crucial to test them on the target devices to ensure compatibility, functionality, and UI/UX consistency. 

2. Native Feature Testing: Native apps often leverage platform-specific features and APIs. Testers should thoroughly evaluate these features to ensure they function as intended and adhere to platform guidelines. 

3. Performance Testing: Native apps have direct access to device resources, so performance testing is crucial to ensure smooth operation, responsiveness, and efficient memory usage. 

4. App Store Compliance: Native apps need to adhere to the respective app store’s guidelines and policies. Testers should verify that the app meets the required standards for submission and does not violate any policies. 

Testing Web Apps 

1. Cross-Browser Compatibility: Since web apps run on web browsers, cross browser testing is done (e.g., Chrome, Firefox, Safari) to ensure consistent behavior and appearance. 
2. Responsiveness and Scalability: Web apps should be tested on various screen sizes, resolutions, and devices to ensure they are responsive and scalable. 
3. Performance and Load Testing: Web apps may experience performance issues due to factors like slow network connections. Conducting performance and load tests helps identify bottlenecks and ensure optimal performance. 
4. Security Testing: Web apps are prone to security vulnerabilities such as cross-site scripting and SQL injection. Comprehensive security testing should be performed to identify and address these risks. 

Hybrid Apps 

1. Compatibility Testing: Hybrid apps combine native and web app elements, so it’s essential to test them across different devices and platforms to ensure compatibility and consistent performance. 
2. Native Feature Integration: Hybrid apps often integrate native device features using plugins or frameworks. Testers should verify that these integrations work correctly and do not impact app functionality or performance. 
3. UI Consistency: Hybrid apps should maintain a consistent UI across different platforms. Testers should check for any UI inconsistencies or glitches. 
4. Offline Functionality: Since hybrid apps can work offline, it’s crucial to test their behavior in offline mode and ensure that data synchronization occurs correctly when the app reconnects. 

All in all, native, web, and hybrid apps require specific testing approaches based on their unique characteristics. By considering the factors mentioned above and selecting the right approach, technique, and tool, testers can ensure that each type of app performs optimally, meets user expectations, and provides a seamless user experience. 

Breaking Into Mobile App Testing: The Process & Techniques  

Mobile app testing ensures that applications meet industry standards, comply with data protection regulations, and maintain user trust. Quality testing contributes to enhanced user satisfaction, increased retention rates, and positive reviews, ultimately leading to higher app downloads and revenue generation for businesses. 

Mobile App Testing Process 

The mobile app testing process is a crucial step in ensuring the quality and functionality of a mobile application. It typically involves multiple stages, starting with test planning and strategizing, followed by test case development, execution, and bug tracking.  

Usually, the key areas of focus include user interface testing, functionality testing, performance testing, security testing, and compatibility testing across different devices and operating systems. To gain precision, testers use various testing tools and techniques to simulate real-world scenarios and uncover any bugs or issues. The process concludes with result analysis and reporting to provide valuable feedback for developers, leading to continuous improvement and a seamless user experience. 

Mobile app testing techniques can be categorized into two main categories: common and advanced techniques.  

7 Leading Mobile App Testing Tools 

There are numerous mobile app testing tools available in the market that can help developers and testers ensure the quality and reliability of their mobile applications. These include some of the most advanced mobile app testing tools as well as those that are highly capable of automation yet specific to testing of mobile apps with dedicated features and technology use.  

1. Appium: Appium is an open-source and widely used automation tool for mobile app testing. It supports both Android and iOS platforms and allows testers to write test scripts in multiple programming languages, including Java, Python, and Ruby. 

2. Xamarin Test Cloud: Xamarin Test Cloud, now a part of Microsoft Visual Studio App Center, is a cloud-based testing tool that enables testing on real devices. It supports both Android and iOS platforms and offers extensive device coverage for efficient testing. 

3. TestComplete: TestComplete is a comprehensive automation testing tool that supports mobile app testing on both Android and iOS platforms. It offers scriptless testing capabilities, making it easy for non-technical testers to create and execute test cases. 

4. Firebase Test Lab: Firebase Test Lab is a cloud-based testing platform by Google. It provides a wide range of testing options, including compatibility, performance, and usability testing, on real Android and iOS devices. It integrates well with other Firebase services. 

5. Kobiton: Kobiton is a mobile testing platform that provides real-device testing for both Android and iOS apps. It offers features like manual testing, automation testing, and device cloud management, allowing testers to execute tests on a wide range of devices. 

6. Perfecto: Perfecto is a cloud-based mobile testing platform that supports automated testing on real devices. It offers a variety of testing options, including functional, performance, and security testing, and integrates with popular test frameworks like Appium and Selenium. 

7. TestFairy: TestFairy is a mobile testing platform that provides real-time testing and monitoring of mobile apps. It offers features like crash reporting, user feedback, session recordings, and over-the-air build distribution, making it easier to identify and fix issues. 

These mobile app testing tools offer a range of features and capabilities to meet the diverse needs of developers and testers. However, choosing the right tool entirely depends on factors such as the platform, testing requirements, budget, and team’s expertise. 

In case, you are looking for some professional quality assurance services or need expert assistance testing your mobile apps, we at BugRaptors bring you a team of most experienced ISTQB testers who could help you transform your entire mobile testing strategy.  

Read more: Common Mobile App Testing Techniques

Mobile App Testing In The IoT Era: Challenges And Opportunities

The Internet of Things (IoT) has revolutionized the way we interact with technology, connecting various devices and enabling seamless communication between them. With the proliferation of IoT devices, mobile apps have become a vital interface for users to control and manage these interconnected devices.  

Therefore, ensuring the quality and reliability of mobile apps in the IoT era is crucial, as they directly impact the user experience and the functionality of connected devices.  

This blog explores the unique challenges and opportunities associated with mobile app testing services in the IoT era. 

The Challenges of Mobile App Testing in the IoT Era 

A. Device Diversity and Compatibility Testing 

• The IoT ecosystem consists of diverse devices with varying hardware specifications, operating systems, and communication protocols. Testing mobile apps for compatibility across a wide range of devices poses a significant challenge. 

• The need to validate seamless interoperability between mobile apps and IoT devices, ensuring smooth communication and data exchange. 

B. Network Connectivity and Performance Testing 

• IoT devices rely on network connectivity, including Wi-Fi, cellular, and Bluetooth, to transmit data. Testing the performance and stability of mobile apps under different network conditions is crucial. 

• Evaluating the app’s responsiveness, data transfer speed, and network resilience when faced with poor or intermittent network connectivity. 

C. Security and Privacy Testing 

• The interconnected nature of IoT devices exposes them to potential security vulnerabilities. Mobile apps must be thoroughly tested to identify and mitigate risks such as unauthorized access, data breaches, and privacy concerns. 

• Testing the app’s authentication mechanisms, encryption protocols, and data integrity to ensure secure communication between the app and IoT devices. 

D. Usability and User Experience Testing 

• In the IoT era, mobile apps serve as the primary interface for controlling and monitoring IoT devices. Usability testing becomes crucial to ensure intuitive navigation, seamless interactions, and a consistent user experience across different devices. 

• Conducting usability tests with real users to gather feedback, iterate app design, and optimize the user interface for enhanced user satisfaction. 

Opportunities For Mobile App Testing In The IoT Era 

A. Test Automation and Continuous Integration 

• The complex nature of IoT systems demands efficient testing methodologies. Test automation enables the execution of repetitive test cases across multiple devices and platforms, reducing manual effort and enhancing test coverage. 

• Integrating mobile app testing into the CI/CD pipeline ensures regular testing and rapid feedback, facilitating quick bug identification and resolution. 

B. Performance Testing at Scale 

• With the growing number of IoT devices and the potential for massive user adoption, mobile apps must undergo performance testing at a scale. Simulating high user loads, network traffic, and data exchange scenarios helps identify performance bottlenecks and optimize app performance. 

• Leveraging cloud-based testing infrastructure enables scalability and the ability to test apps under realistic user load conditions. 

C. Security Testing and Penetration Testing 

• The increasing complexity and interconnectedness of IoT ecosystems expose apps to security risks. Robust security testing, including penetration testing, helps identify vulnerabilities and ensure that the app and connected devices are resistant to potential cyber threats. 

• Regular security assessments and audits minimize the risk of data breaches and unauthorized access, instilling user confidence in the app’s security. 

D. Edge Case and Real-World Scenario Testing 

• IoT devices often operate in diverse environments, subject to various conditions and edge cases. Testing mobile apps with real-world scenarios, such as extreme temperatures, limited power supply, or unstable network conditions, ensures app resilience and reliable performance. 

• Collaborating with domain experts and performing field testing provides valuable insights into potential challenges and opportunities in specific IoT use cases. 

Conclusion 

Mobile app testing in the IoT era presents unique challenges due to device diversity, network complexities, security concerns, and user experience expectations. However, these challenges also bring forth opportunities to adopt innovative testing approaches and methodologies.  

By leveraging automation, ensuring performance at scale, prioritizing security, and conducting real-world scenario testing, mobile app developers can deliver high-quality apps that provide a seamless user experience and drive the success of IoT ecosystems. 

As the IoT landscape continues to evolve, mobile app testing must evolve in tandem. Embracing the challenges and opportunities of the IoT era will enable organizations to create robust, secure, and reliable mobile apps that unlock the full potential of interconnected devices and deliver exceptional user experiences. By staying proactive and adaptable, mobile app testers can help shape the future of IoT applications and contribute to the advancement of this transformative technology. 

How Can EU AI Draft Impact BFSI Firms?

Even though the world took some time to recover from the pandemic and the Ukraine-Russia war, the BFSI sector is booming.  

According to the business research company, the global banking, financial services and insurance (BFSI) security market size grew from $53.23 billion in 2022 to $59.79 billion in 2023 at a compound annual growth rate (CAGR) of 12.3%.  

And therefore, banks, insurance firms, and financial service providers are aiming to leverage Artificial Intelligence and Machine Learning to foster added productivity, better customer service, and overall reduction in the cost of operations.  

Though the use of predictive AI and the available open AI tools are meant to simplify BFSI customer targeting, EU even takes aim at tools like ChatGPT and Google Bard to get a handle on AI use.  

In other words, the integration of all these technologies have raised the question on AI (Artificial Intelligence) ethics, data governance, legality, and trust. Considering the use of customer data and meeting the privacy goals, New EU AI act has identified and flagged the AI systems for high-risk possibilities that BFSI organizations need to watch through integrated BFSI testing solutions and effective compliance management.  

So, let’s explore the relation between AI, ML, and Big Data while walking through existing regulations surrounding their use. Besides, we will map out the new EU rules for trustworthy AI and how organizations could prepare for it.  

What EU AI ACT Says 

Though there are no new rules rolled out or exercised, the EU has published a draft on AI act in April 2023. It is a legislative framework that controls the use of AI and is set on public trust. The act is originally defined to address the organizations working on AI systems and is likely to show its implications on BFSI firms across the European boundaries. Some of the possible outcomes that we can witness with the new EU rules include:  

• With the new EU AI act, the commission aims to protect the tech users against the wide set of complications or harm that may occur with the misuse of personal information.  
• Defined on a risk-based approach which puts all the AI used to check on past credit history, work performance, and behavior of the user under high-risk category. Also, there are certain AI systems that are likely to be prohibited with the new AI act, but these systems are likely to have or show minimum application to BFSI.  
• Based on the regulations, any organization that develops or uses AI-based high-risk systems are likely to witness strict rules that will be subject to conformity assessments as well as requirements to register.  
• The rules defined with EU AI act would be complicated as they will address risk management, accuracy, data quality, documentation, human oversight, robustness, security, and transparency.  
• Above all, the inability to align with the defined regulations will accommodate hefty fines for any incidence of non-compliance, which might be 6 percent of global turnover of the organization or up to €30 million. 

Even if the implementation has not been finalized, the available set of guidelines and information need BFSI firms to prepare well in advance. 

How Can EU AI Draft Impact BFSI Firms? 

It’s important to note that the specific provisions of the EU AI Act and its impact on the BFSI sector may evolve or change before it becomes law. It’s advisable to refer to the latest version of the act or consult legal professionals and industry experts for the most up-to-date and accurate information. However, some of the few important factors that BFSI firms need to understand in context to EU AI act includes: 

1. Regulation of AI systems: The EU AI Act aims to classify AI systems into different risk categories, with higher-risk systems subject to stricter regulations. In the BFSI sector, where AI is already being used for applications such as risk assessment, fraud detection, and customer service, there could be increased scrutiny and requirements for ensuring the safety, reliability, and transparency of these AI systems. 
2. Ethical considerations: The EU AI Act emphasizes ethical principles such as human oversight, non-discrimination, and transparency. This means that AI systems used in the BFSI sector would need to adhere to these principles, potentially impacting areas such as credit scoring, loan approvals, and insurance underwriting. 
3. Data governance: In the BFSI sector, where data is crucial for risk assessment, fraud prevention, and customer insights, financial institutions may need to ensure compliance with data protection regulations and establish robust data governance frameworks. This could involve implementing privacy-preserving techniques, obtaining appropriate consent for data usage, and ensuring the security of sensitive financial information. 
4. Increased regulatory oversight: The EU AI Act proposes the creation of a European Artificial Intelligence Board and a network of national supervisory authorities. These bodies would be responsible for monitoring compliance, conducting audits, and enforcing the regulations. 
5. Market impact: Some financial institutions may need to re-evaluate their AI strategies, invest in compliance measures, and potentially modify or replace existing AI systems to meet the new requirements. This could result in a temporary slowdown in AI innovation and deployment until regulatory compliance is achieved. 

Establishing The Relation Between AI, ML, and Big Data 

Since AI and ML are becoming the new normal with more and more organizations adopting the technologies, the use of these technologies along with Big Data is quickly expanding. While Big Data becomes the base for training AI algorithms, the tech enthusiasts are harnessing AI to understand big data better.  

Besides, it is necessary that Big Data is used wisely and must meet the GDPR compliance goals, to ensure lawful and fair use of the information. On the other hand, it is equally important to create more reliable AI and ML systems that could complement more trustworthy decisions.   

Aligning The Trio For The Best 

To yield the best of automation, predictability, and reliable output, AI, ML, and Big Data must be well integrated. Though the use of these technologies comes as challenge to BFSI firms, the outputs driven through the process could enable businesses to thrive in the right direction.  

On top of that, AI and ML paired with Big Data create space for opportunities in the BFSI sector by simplifying the transactions for consumers, reducing the effort, and ultimately adding to the revenue generation. For instance, a user paying electricity bills through a payment management portal could be advised on savings through personal recommendations to switch electricity suppliers.  

However, the process further needs to ensure that all the recommendations generated must clearly state to a user that the given recommendations are made in regard to the best price options in the market or are shared considering the best for the user from a list of preferred suppliers.  

Read more: Modern Financial Data Privacy Laws And The EU AI Act

How Digital Assurance Testing Works?

According to Gartner, 56% of CEOs say digital improvements have increased revenue while 89% of all companies have already or are planning to adopt a digital-first business strategy. 

The transition refers to a rapid increase in the number of software releases and added pressure on development teams to develop technology that delivers the ultimate end-user experience. And since user experience takes the front seat when it comes to any aspect of software development, digital assurance comes here to the rescue.  

As per Forrester, the organizations implementing QA are 50 percent more likely to deliver software on time. Besides, the ever-increasing complications, use of integrated technologies, and complex systems have all added to greater complexity on SDLC. Digital Assurance allows syncing the product objectives with the scope and expected output of a technology.  

Ultimately, digital assurance services come as a value practice to the entire customer chain when developers or enterprise need to meet the highest capabilities on a product intended to drive. Since BugRaptors has always underlined the need for quality engineering and digital assurance in overcoming business specific challenges, let us quickly explore digital assurance testing in detail while learning the implementation challenges and the way to success through them. 

Digital Assurance & Its Components  

Digital assurance complements the digital transformation projects by simplifying the entire path to the achievement of end objectives. In other words, digital assurance practices are defined to drive quality to the digital transformation projects by aligning them on essential technological paradigms like cloud, mobility, etc.  

Integrating digital assurance services offers significant business benefits when worked along with development and testing lifecycle. It is meant to offer streamlined workflows for improved product capability, improved user journey, and accelerated product delivery.  

Also, agile and DevOps methodologies are vital for digital assurance as they complement the entire development and testing framework. Besides, the key components of the digital assurance process could be listed as:  

• Data Assurance: To work on the accuracy, completeness, and security of the data associated to app system or cloud.  
• Business Value Chain Testing: To examine the flow of business process in cloud and digital system to support users as well as business strategy.  
• Localization Testing: To test software to align with regional requirements such as cultural and linguistic goals.  
• Usability/Accessibility Testing: To ensure software made must be easy to use and accessible to all users, irrespective of any disabilities.  

How Digital Assurance Testing Works? 

Traditional quality assurance involved a limited set of testing procedures including functional testing, web testing, end-to-end testing, and testing of non-functional aspects that complement all the defined operations.  

However, the increasing complexity in tech and the customer objectives have made organizations align with agile testing techniques.  

Here comes the role of digital assurance testing in digital transformation! 

Digital assurance testing works by addressing the goals surrounding user experience while keeping a check on all the factors that help create a sustainable product. These include:  

Globalization & Localization 

Digital assurance testing aims to give equal and consistent access to any digital product or service. It involves checking on the localized and global versions of the product to ensure they work well in all geographical locations.  

Omnichannel Experience 

Since customers as well as business users operate across dynamic channels, creating a seamless user experience requires creating products that work well across all channels. These include apps, mobile solutions, web technology as well as improving the in-store experience using technology in order to keep users engaged.  

User Experience 

Since customers these days are familiar with the technology, it has become necessary to create technology that can overrule those available in the market. The digital assurance strategies are designed to offer consistent yet extraordinary customer experience.  

Performance & Security 

The digital assurance measures are designed to work on all the features and benchmarks of excellence. Digital assurance covers all the security vulnerabilities to ensure the product must meet highest benchmarks of quality.  

Continuous Deployment

Unlike siloed testing, digital assurance allows embracing modern testing techniques like continuous deployment and continuous delivery. Ultimately, digital assurance infuses quality into a product from the earliest stages allowing users to meet business assurance goals and seek continuous deployment.  

Cross-browser Compatibility

With digital assurance, apps are tested for the highest quality across different browsers considering all the aspects of performance superiority.  

Deployment & Time-to-market

With integration of continuous testing and continuous deployment practices, the digital assurance teams could foster quality engineering at a greater pace. From use of scriptless tools to automation, digital assurance complements the entire development lifecycle.  

Read more: Challenges Implementing Digital Assurance