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jcmarchi · 2 months ago

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Amazon Bedrock gains new AI models, tools, and features

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Amazon Bedrock gains new AI models, tools, and features

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Amazon Web Services (AWS) has announced improvements to bolster Bedrock, its fully managed generative AI service.

The updates include new foundational models from several AI pioneers, enhanced data processing capabilities, and features aimed at improving inference efficiency.

Dr Swami Sivasubramanian, VP of AI and Data at AWS, said: “Amazon Bedrock continues to see rapid growth as customers flock to the service for its broad selection of leading models, tools to easily customise with their data, built-in responsible AI features, and capabilities for developing sophisticated agents.

“With this new set of capabilities, we are empowering customers to develop more intelligent AI applications that will deliver greater value to their end-users.”

Amazon Bedrock expands its model diversity

AWS is set to become the first cloud provider to feature models from AI developers Luma AI and poolside, while also incorporating Stability AI’s latest release.

Through its new Amazon Bedrock Marketplace, customers will have access to over 100 emerging and specialised models from across industries, ensuring they can select the most appropriate tools for their unique needs.

Luma AI’s Ray 2

Luma AI, known for advancing generative AI in video content creation, brings its next-generation Ray 2 model to Amazon Bedrock. This model generates high-quality, lifelike video outputs from text or image inputs and allows organisations to create detailed outputs in fields such as fashion, architecture, and graphic design. AWS’s presence as the first provider for this model ensures businesses can experiment with new camera angles, cinematographic styles, and consistent characters with a frictionless workflow.

poolside’s malibu and point

Designed to address challenges in modern software engineering, poolside’s models – malibu and point – specialise in code generation, testing, documentation, and real-time code completion. Importantly, developers can securely fine-tune these models using their private datasets. Accompanied by Assistant – an integration for development environments – poolside’s tools allow engineering teams to accelerate productivity, ship projects faster, and increase accuracy.

Stability AI’s Stable Diffusion 3.5 Large

Amazon Bedrock customers will soon gain access to Stability AI’s text-to-image model Stable Diffusion 3.5 Large. This addition supports businesses in creating high-quality visual media for use cases in areas like gaming, advertising, and retail.

Through the Bedrock Marketplace, AWS also enables access to over 100 specialised models. These include solutions tailored to fields such as biology (EvolutionaryScale’s ESM3 generative model), financial data (Writer’s Palmyra-Fin), and media (Camb.ai’s text-to-audio MARS6).

Zendesk, a global customer service software firm, leverages Bedrock’s marketplace to personalise support across email and social channels using AI-driven localisation and sentiment analysis tools. For example, they use models like Widn.AI to tailor responses based on real-time sentiment in customers’ native languages.

Scaling inference with new Amazon Bedrock features

Large-scale generative AI applications require balancing the cost, latency, and accuracy of inference processes. AWS is addressing this challenge with two new Amazon Bedrock features:

Prompt Caching

The new caching capability reduces redundant processing of prompts by securely storing frequently used queries, saving on both time and costs. This feature can lead to up to a 90% reduction in costs and an 85% decrease in latency. For example, Adobe incorporated Prompt Caching into its Acrobat AI Assistant to summarise documents and answer questions, achieving a 72% reduction in response times during initial testing.

Intelligent Prompt Routing

This feature dynamically directs prompts to the most suitable foundation model within a family, optimising results for both cost and quality. Customers such as Argo Labs, which builds conversational voice AI solutions for restaurants, have already benefited. While simpler queries (like booking tables) are handled by smaller models, more nuanced requests (e.g., dietary-specific menu questions) are intelligently routed to larger models. Argo Labs’ usage of intelligent Prompt Routing has not only improved response quality but also reduced costs by up to 30%.

Data utilisation: Knowledge bases and automation

A key attraction of generative AI lies in its ability to extract value from data. AWS is enhancing its Amazon Bedrock Knowledge Bases to ensure organisations can deploy their unique datasets for richer AI-powered user experiences.

Using structured data

AWS has introduced capabilities for structured data retrieval within Knowledge Bases. This enhancement allows customers to query data stored across Amazon services like SageMaker Lakehouse and Redshift through natural-language prompts, with results translated back into SQL queries. Octus, a credit intelligence firm, plans to use this capability to provide clients with dynamic, natural-language reports on its structured financial data.

GraphRAG integration

By incorporating automated graph modelling (powered by Amazon Neptune), customers can now generate and connect relational data for stronger AI applications. BMW Group, for instance, will use GraphRAG to augment its virtual assistant MAIA. This assistant taps into BMW’s wealth of internal data to deliver comprehensive responses and premium user experiences.

Separately, AWS has unveiled Amazon Bedrock Data Automation, a tool that transforms unstructured content (e.g., documents, video, and audio) into structured formats for analytics or retrieval-augmented generation (RAG). Companies like Symbeo (automated claims processing) and Tenovos (digital asset management) are already piloting the tool to improve operational efficiency and data reuse.

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The expansion of Amazon Bedrock’s ecosystem reflects its growing popularity, with the service recording a 4.7x increase in its customer base over the last year. Industry leaders like Adobe, BMW, Zendesk, and Tenovos have all embraced AWS’s latest innovations to improve their generative AI capabilities.

Most of the newly announced tools – such as inference management, Knowledge Bases with structured data retrieval, and GraphRAG – are currently in preview, while notable model releases from Luma AI, poolside, and Stability AI are expected soon.

See also: Alibaba Cloud overhauls AI partner initiative

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, Amazon, amazon web services, artificial intelligence, aws, bedrock, models

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nyuway · 2 months ago

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The Role of DAST in Protecting Your Web Applications From Vulnerabilities

Nyuway

As cyber attacks become increasingly sophisticated, enterprises require DAST to help defend their applications against sophisticated cyber threats. By scanning a running application and simulating hacker behavior, it can identify vulnerabilities before enterprising hackers exploit them.

DAST complements static application security testing (SAST) and software composition analysis (SCA), offering additional runtime insights beyond source-code scans. Furthermore, it serves as a valuable companion to manual penetration testing.

Web Application

Dynamic application security testing (DAST) scans running web applications by simulating malicious external attacks and identifying vulnerabilities which could be exploited. DAST can reveal business logic flaws like SQL injection, XSS attacks and authentication issues which often go undetected through static code analysis tools (SAST) or manual penetration testing methods.

While DAST tools can be effective, they do have their limitations. False positives and lack of context can result in security gaps when applied solely. To address this limitation, it may be useful to combine DAST with other methodologies like SAST, IAST or software composition analysis (SCA) in order to create a comprehensive security program.

Implementing DAST into your CI/CD pipeline can ensure vulnerabilities are identified and fixed as code changes are made, leading to decreased costs and speedier time to production. Furthermore, early detection helps minimize accidental releases or potential data breaches; many of today's most harmful cyberthreats rely on unpatched vulnerabilities within running applications for attack.

API Security

DAST tools differ from SAST in that they attack an API without knowledge of its application; instead, this kind of testing mimics how attackers would try to exploit it - thus helping detect vulnerabilities which are harder to spot with traditional testing techniques.

DAST can be particularly effective at protecting web APIs. It can scan API endpoints to expose vulnerabilities that attackers could exploit, such as injection attacks or misconfigurations; and can identify unexpected data leaks or performance issues which might signal deeper security holes.

DAST excels at scanning web application UIs, yet struggles to access and test APIs tucked behind dynamic behavior layers. These layers hide backend API calls behind their respective UI layers until JavaScript code executes and uses an appropriate request format for runtime visibility.

Continuous

As web applications continue to be developed and evolve, security risks continue to shift and adapt accordingly. DAST can help address this challenge effectively.

This type of testing works by simulating attacks a malicious actor might employ to penetrate an application. By employing a black box approach and looking at it from outside in, this approach can detect vulnerabilities which other methods such as SAST or SCA fail to find.

DAST provides feedback and reporting to help developers and security teams prioritize vulnerabilities for remediation. It can also be easily integrated with the CI/CD pipeline to scan at every stage of development, making it easy to detect security issues before they reach production.

DAST can provide an overall picture of your application's vulnerability to threats when used alongside SAST & IAST (which examine code line by line), to form part of an integrated security assessment process. DAST tests entry points such as forms & API endpoints while SAST & IAST examine internal risks like misconfigurations & coding errors to provide a full assessment.

Automated Vulnerability Scanning

DAST differs from traditional static testing by testing an application while it runs, simulating how a hacker would search for vulnerabilities in real time. DAST can run both unauthenticated and authenticated modes to see how the app responds to attacks that typically gain control over an account and reveal sensitive data.

Businesses using advanced DAST solutions that utilize proof-based scanning can quickly identify and prioritize critical vulnerabilities using sophisticated DAST solutions that use proof-based scanning to eliminate false positives, making their teams focus their efforts on real risks that could cause serious breaches instead of spending hours sifting through massive test results.

DAST tools also give development and QA teams detailed information on how they can reproduce and fix vulnerabilities more quickly, so as to minimise disruption in production environments. When integrated into the Continuous Integration and Continuous Deployment pipeline, DAST can detect vulnerabilities at each stage of development and production to decrease chances of breach as well as ensure compliance with regulatory standards such as PSI-DSS or HIPAA.

Managed DAST Services

DAST is an essential part of any comprehensive application security program, and an indispensable element for its testing capabilities. As the most adaptable security testing tool on the market, it can be integrated into each stage of development from early design through quality assurance testing, staging deployment and production deployment. When integrated into an CI/CD pipeline DAST can also help developers identify vulnerabilities before reaching production, saving both time and money in development costs.

DAST works by conducting automated tests simulating external attack behavior without understanding its internals, similar to malicious attackers' tactics, in order to uncover unexpected outcomes and vulnerabilities. Language independent, DAST can detect runtime issues like server configuration problems, authentication/encryption misconfigurations and more that SAST cannot.

To maximize the value of DAST, organizations should set clear security objectives and incorporate it into existing CI/CD and DevOps workflows. This includes developing strategies for handling false positives and regression tests to verify previously fixed vulnerabilities do not resurface. Ideally, DAST should be integrated with CI/CD pipeline so every code push or deployment triggers dynamic security checks automatically.

#Dynamic Application Security Testing (DAST)#Web Application DAST #API Security DAST #Continuous DAST #Automated Vulnerability Scanning

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softweb-solutions · 4 months ago

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Comprehensive Guide to Web Application Testing: Types, Use Cases & Tools

The success of your web application can be the linchpin of your business’s growth trajectory. Today’s modern business must not merely rely on functionality, but also on exceptional user experience, driving engagement and building customer loyalty.

According to IBM, the cost to fix bugs found during the testing phase could be 15x more than the cost of fixing those found during design.

As businesses increasingly rely on sophisticated web platforms to connect with their audiences, web application testing emerges as a strategic imperative. This blog delves into the transformative impact of web application testing services. Let’s explore different types of web application testing.

Understanding web application testing and why it is necessary

Web application testing is essential for ensuring its functions are accurate and meet the requirements of end users. To understand the testing process precisely, here is a comprehensive breakdown:

Purpose of web application testing

It aims to identify bugs, ensure functionality, and verify that the application meets the specific requirements. It ensures the application:

Functions correctly across different browsers and devices

Performs well under varying loads

Secure against vulnerabilities

Provides good user experience

Number of end users

It involves assessing how the application:

Handles high volume of users

Robustness under extreme conditions

Ability to scale efficiently with user demands

Target domain

Domain-specific testing: Based on target domains such as e-commerce, banking, healthcare, etc., it assures specific regulations and compliance requirements are met. Therefore, testing ensures that domain-specific functions such as payment processing and data security are working correctly and adhere to relevant standards.

Functional testing: It validates that all features and functions are relevant to the target domain and work as expected.

User role

Role-based testing: Various users have different login permissions and roles within an application, for example admin, regular user, and guests. Testing ensures that each role has appropriate access and functionalities, and unauthorized users do not have permission to access restricted areas.

User authentication and authorization testing: Verifies user authentication such as login mechanism and authorization such as role-based access control is working accurately.

Importance of AI in software testing AI in software testing improves software quality by analyzing data and identifying patterns. It optimizes the testing process, automates repetitive tasks, and analyzes vast datasets efficiently for more reliable software solutions. Read more

Further, web application testing is described in detail below:

Unit testing

It is a software testing method where the smallest part of an application such as units or components are tested in isolation from the rest of the system. These units are individual functions, methods, or classes validating each unit’s performance.

In 2023, unit testing used in 63% of all software projects was the most common type of software test. – Statista

For example:

Consider an online shopping cart

Add items to the cart:

Test adding a single item to the cart to verify it appears correctly.

Test adding multiple items to ensure the cart updates accurately.

Test adding items with various quantities to check the cart reflects the correct total.

Tools for unit testing:

JUnit: A widely used testing framework for Java applications.

NUnit: A robust testing framework for .NET applications.

Jest: A popular JavaScript testing framework.

PyTest: A flexible and scalable testing framework for Python.

Integration testing

It combines multiple units, modules, and components of software applications for testing and ensures communication and data flow functions are consistent as a cohesive unit. Its goal is to identify issues in the interaction between integrated components.

For example:

Consider an event registration system

User registration and event enrollment:

Verify user details are correctly passed from the registration module to the event enrollment system.

Ensure users can register, view their details, and successfully enroll in events.

Payment processing and confirmation:

Confirm the payment module securely processes transactions and confirms success.

Test that after payment, the registration status is updated, and a confirmation email or notification is sent.

Ensure errors during payment processing trigger appropriate messages and provide resolution instructions.

Tools for integration testing

JUnit: Used for testing in Java applications.

TestNG: A Java testing framework with advanced configuration capabilities.

Postman: A tool for API integration testing to validate HTTP requests.

SoapUI: A tool used for testing of web services, particularly for SOAP and REST APIs.

System testing

It is a comprehensive phase in the software testing lifecycle that evaluates the complete and integrated software system to ensure it meets specified requirements and functions as intended. This type of testing is conducted after integration testing and focuses on verifying the entire system’s behavior and performance in a unified environment.

For example:

Consider hotel reservation system

Verify that the user interface allows users to search for available rooms based on their selected criteria (e.g., dates, location, room type).

Test the booking form to ensure users can enter their details (name, contact information) and that the system displays accurate room availability and pricing information.

Check that users receive confirmation of their booking with a summary of their reservation details once they complete the process.

Tools for system testing

Selenium: A widely used tool for automating web application testing.

Cypress: An end-to-end testing framework designed for modern web applications.

Playwright: Open-source framework for end-to-end testing of web applications.

Performance testing

Evaluate how a system performs under various conditions, focusing on how application meets user expectations for speed, reliability, and stability, especially under varying operational conditions.

Assessing user experience under load: Evaluate how well the app maintains responsiveness and functionality when subjected to varying user loads and network speeds.

For example, test how the app performs when 5,000 users access it simultaneously under different network conditions.

Determining peak performance limits: Identify the maximum number of concurrent users the app can handle while keeping the response time within 2 seconds.

For example, ensure the app remains performant with 7,500 active users.

Evaluating performance on low-bandwidth connections: Test the app’s responsiveness and stability when accessed from a low-bandwidth network.

For example, 2G or slow 3G, to confirm that essential functionalities remain usable.

Tools for performance testing

Apache JMeter: An open-source tool to simulate heavy loads on web applications.

LoadRunner: Predicts system behavior under varying loads.

Gatling: Open-source tool designed to test web applications and services.

BlazeMeter: A cloud-based tool that supports JMeter scripts.

Security testing

It focuses on identifying vulnerabilities, weaknesses, and potential threats within an application or system. It aims to safeguard the applications from threats, ensuring compliance with security policies and protecting user data and system integrity, maintain user trust.

For example:

Authentication verification: Test the strength and effectiveness of user login mechanisms and credential storage.

Authorization checks: Ensure users have appropriate access rights and cannot access unauthorized areas or functions.

Vulnerability scanning: Identify and assess potential security vulnerabilities, such as SQL injection or cross-site scripting (XSS).

Penetration testing: Simulate attacks to identify weaknesses and test the system’s resilience against real-world threats.

Tools for security testing

OWASP ZAP (Zed Attack Proxy): Open-source tool for finding vulnerabilities in web applications.

Burp Suite: Comprehensive solution for web application security testing.

Acceptance testing

It determines whether a software application meets the acceptance criteria of the end-users or stakeholders and ensures the system fulfills business requirements and satisfies user needs.

For example:

Consider shopping experience on an e-commerce website

User registration and login:

Verify that new users can successfully register for an account by entering their personal information and receiving a confirmation email.

Test that returning users can log in with their credentials and access their accounts without issues.

Product search and purchase:

Ensure users can search for products using keywords or filters and view accurate search results.

Test that users can select products, add them to their shopping cart, and proceed to checkout.

Verify that users can enter shipping and payment information, apply discount codes, and complete the purchase.

Tools for acceptance testing

TestRail: Used for test case management.

Zephyr: A robust test management solution for end-to-end test planning and execution.

Suggested: Explore test automation with Healenium Automation testing

Automation testing

Automation testing involves using specialized tools to execute pre-scripted tests on the application. This type of testing is useful for repetitive tasks, regression testing, and load testing.

For example:

Efficiency: Speeds up the testing process by automating repetitive tasks.

Consistency: Ensures consistent execution of test cases.

Cost-effective: Reduces the manual effort involved, lowering costs in the long run.

Tools for automation testing

Selenium: A widely used tool for automating web application testing.

Cypress: An end-to-end testing framework designed for modern web applications.

Katalon Studio: Supports web, API, mobile, and desktop applications.

Appium: An open-source tool for automating mobile applications.

Robot Framework: A versatile open-source framework supports various testing libraries and tools.

Playwright: Open-source framework for end-to-end testing of web applications.

Regression testing

It ensures that recent changes or additions to an application have not adversely affected its existing functionality but involves re-running previously executed test cases verifying that new code changes, bug fixes, or enhancements have not introduced any new defects causing unintended side effects.

For example:

Feature enhancements: Ensure new features such as a recommendation engine on an e-commerce site, don’t disrupt existing functions such as the shopping cart.

Bug fixes: Verify fixing a bug, like in a banking app’s transaction history, doesn’t create new issues in other areas like transfers.

Performance optimizations: Confirm performance improvements such as faster image loading on a social media platform without affecting the existing features.

External system integration: Test integrating new systems or APIs such as email marketing tools in a CRM system, doesn’t disrupt current functionalities.

System upgrades: Check application features such as content publishing in CMS remain functional after system upgrades.

Tools for regression testing

Selenium (with CI/CD tools like Jenkins): Integrates with CI/CD pipelines to automate web applications.

TestComplete: Supports functional, regression, and load testing for desktop, web, and mobile applications.

Cypress: A JavaScript end-to-end testing framework to automate browser interactions.

Playwright: A Node.js library that automates web tests across Chromium, Firefox, and WebKit.

AI and ML are revolutionizing test automation AI and ML are revolutionizing test automation by resolving UI issues, enhancing functional and performance testing, and minimizing unrealistic data risk. This transformative impact makes testing more accurate and reliable. Read more

TestOps

TestOps is an emerging practice that integrates testing into the overall DevOps pipeline. It focuses on the management, orchestration, and analysis of testing activities across all phases of development. TestOps ensures that testing is aligned with continuous integration/continuous delivery (CI/CD) processes, enabling faster and more reliable releases.

Benefits of TestOps:

Enhanced collaboration: Bridges the gap between development, operations, and testing teams.

Continuous testing: Ensures that testing is a continuous activity integrated with the CI/CD pipeline.

Improved quality and speed: Accelerates the release process while maintaining high quality.

Tools for TestOps

Jenkins: CI/CD tool to automate the building, testing, and deployment of software.

CircleCI: A cloud-based CI/CD platform facilitates continuous integration and delivery workflows.

Azure DevOps: Provides a complete suite for end-to-end DevOps practices.

Gitlab: Integrated CI/CD functionality for managing the full DevOps lifecycle.

Smoke testing

It is a preliminary level of software testing conducted to ensure that the most critical functions of an application are working correctly before more detailed testing is performed.

For example:

1. Build verification

Before extensive testing begins, smoke testing verifies that the latest build is stable enough for further testing. For example:

E-commerce application: After deploying a new build, perform smoke testing to ensure that basic functions like login, product search, and checkout are working.

2. Feature release

When a new feature is added, smoke testing ensures that the new feature doesn’t break the existing core functionality. For example:

Social media app: After introducing a new messaging feature, conduct smoke testing to confirm that existing features like posting updates and notifications still work.

Tools for smoke testing

Selenium: Supports automated smoke testing for web applications.

JUnit: Widely used for Java applications.

TestNG: Framework for automated testing that can handle smoke tests.

Cypress: A JavaScript end-to-end testing to automate basic smoke test.

Playwright: A Node.js library that automates smoke tests across different browsers.

Load testing

Type of performance testing designed to assess how a system performs under a specific and an expected load of users or transactions. The primary objective is to determine whether the system can handle the anticipated volume of traffic or data without issues.

For example:

API performance: Test how an API handles 1,000 requests per minute to ensure it operates smoothly without errors or delays.

Database efficiency: Evaluate the database’s performance with multiple concurrent queries to ensure response times stay within acceptable limits.

Streaming services: Assess how a video streaming service manages 500 simultaneous HD streams to ensure quality and minimal buffering.

Tools for load testing

Apache JMeter: Widely used for performance and load testing of applications.

LoadRunner (Micro Focus): Enterprise-grade tool for load testing and performance monitoring.

Stress testing

It evaluates how a system behaves under extreme or peak conditions beyond its normal operating limits. It determines the system’s robustness, identify breaking points, and assess its ability to recover from failure.

For example:

System limits: Determine the maximum load the system can handle before failure, like handling 10,000 transactions per second.

Resource exhaustion: Assess how the system behaves when resources like CPU, memory, or storage are maxed out.

Error handling: Verify how the system manages and recovers from high-load scenarios that cause errors or crashes.

Tools for stress testing

Apache JMeter: Widely used for performance and load testing of applications.

LoadRunner (Micro Focus): Enterprise-grade tool for load testing and performance monitoring.

Insights Discover The Power of QA Automation Testing Services Assurance testing. Before the application is made public or sent to the client, it needs to have addressed every issue and bug that might have crept in during the coding of the application. Delegating a dedicated engineer to perform manual tests can get cumbersome. Download

Conclusion

As we navigate the intricate landscape of digital transformation, the role of comprehensive web application testing becomes undeniably pivotal to steering business growth. Beyond safeguarding functionality and performance, meticulous testing cultivates a resilient foundation for user satisfaction and operational excellence.

By embracing a strategic approach to testing, businesses can unveil latent opportunities, mitigate risks, and enhance their competitive edge. Talk to our QA test engineers to discuss your requirements.

Originally published at softwebsolutions on August 23, 2024.

#Web application testing #ai testing services #Application testing services #Software application testing services #AI Software testing services #Application automation testing

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sm-techved · 5 months ago

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Revolutionizing Application Testing in Agile Environments: Strategies for Success

In today's fast-paced digital landscape, traditional application testing methods often struggle to keep up with rapid development cycles. As businesses strive for quicker releases and continuous updates, the need for efficient and effective testing strategies has never been more critical. Enter the world of agile testing, where innovative approaches are transforming how we ensure software quality and drive innovation.

Agile testing is not merely about executing tests; it's about seamlessly integrating validation processes into the development workflow. By embracing agile methodologies, organizations can leverage cutting-edge techniques to enhance their software testing practices and deliver superior products to market faster than ever before.

key strategies for optimizing application testing in agile environments:

Embrace the "Shift Left" Approach

The "Shift Left" philosophy is fundamental to agile testing, emphasizing the importance of early application testing in the development process. Unlike traditional waterfall models, where testing occurs at the end, agile encourages validation from the outset. This approach incorporates unit testing and functional testing to ensure each component is thoroughly validated during development.

By integrating validation testing early, teams can significantly reduce defect inflow in the final product. This proactive strategy not only saves time but also minimizes costs associated with post-deployment bug fixes.

Harness the Power of AI Automation

As development speeds increase, manual testing often becomes a bottleneck. AI automation testing offers a powerful solution to this challenge. AI-powered tools can accelerate testing processes by identifying key areas for validation, generating test cases, and even predicting potential failure points.

AI automation is particularly effective in enhancing regression testing. By learning from past test results, AI can focus on areas with higher failure probabilities, improving test coverage and productivity. Moreover, AI-driven performance testing tools can simulate various user scenarios at scale, ensuring web application testing and mobile application testing are prepared for real-world usage.

Implement Continuous Testing with DevOps

In agile environments, the synergy between agile and DevOps practices is crucial. Continuous testing is the linchpin of this collaboration. By integrating testing into Continuous Integration/Continuous Deployment (CI/CD) pipelines, teams ensure that every code change undergoes automated testing before reaching production.

This approach accelerates the development process and enhances overall software quality. Automated tests, ranging from security testing to usability testing, are triggered with each commit, ensuring new features don't introduce vulnerabilities or compromise user experience.

Prioritize Agile Performance and Load Testing

As user expectations for application performance continue to rise, performance testing in agile setups becomes increasingly critical. Rather than conducting performance tests solely before release, agile advocates for continuous performance monitoring throughout the development cycle.

Modern tools enable teams to incorporate web application testing and mobile application testing into their agile workflows, providing real-time insights into application performance under various conditions. These continuous feedback loops help identify and address performance issues early, long before they impact end-users.

Emphasize User-Centric Testing

Agile methodologies place the user at the center of the development process. User testing involves frequent feedback loops that enable rapid iteration based on real user input. Similarly, accessibility testing should be ingrained in the process to ensure applications are usable by all, including those with disabilities.

Advanced AI-powered testing solutions can simulate a wide array of user interactions, providing valuable insights into how different user groups engage with applications. This information is invaluable for refining application design and functionality to meet genuine user needs.

Integrate Security Testing Throughout the SDLC

In an era of increasing cyber threats, security cannot be an afterthought. Agile development demands that security testing be integrated into every stage of the software development life cycle (SDLC). Automated security testing allows for continuous scanning of the codebase, identifying vulnerabilities as soon as they're introduced.

By embedding security testing within existing agile and DevOps processes, organizations can create more secure and resilient applications. This proactive approach ensures that software meets not only performance and usability standards but also rigorous security expectations.

Conclusion

Agile testing is revolutionizing how we approach software quality assurance, security, and user experience. By adopting these strategies, organizations can ensure their testing processes are as agile and responsive as their development cycles.

In a world where change is the only constant, staying ahead of the curve in testing methodologies is crucial. By embracing agile testing principles and leveraging cutting-edge technologies like AI automation, businesses can develop applications that are not only robust and secure but also deliver exceptional user experiences.

As we continue to navigate the complex landscape of software development, one thing is clear: agile testing is not just a trend��it's a necessity for organizations looking to thrive in the digital age. By investing in these advanced testing strategies, businesses can ensure their applications are ready to meet the challenges of tomorrow, today.

#application testing #agile testing #validation testing #ai automation testing #software testing #Performance Testing #web application testing #mobile application testing

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seoperfectqa · 7 months ago

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In today’s fast-paced digital landscape, ensuring the reliability and performance of web applications is crucial. With increasing user expectations and the need for rapid releases, manual testing alone cannot meet the demands. This is where automation testing for web applications becomes indispensable. In this comprehensive guide, we will explore best practices for automation testing, highlighting the importance, benefits, and effective strategies to implement automation in your testing process.

#automation testing for web applications

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yethiconsulting · 8 days ago

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Breaking Myths About Test Automation: What Every QA Engineer Should Know

Test automation has become a cornerstone of modern software development, but several myths still surround it, often leading to misconceptions among QA engineers. Let’s debunk some of the most common myths:

Test Automation Replaces Manual Testing:

Automation enhances efficiency, but it cannot replace the need for manual testing entirely. Exploratory testing, UX evaluation, and critical thinking remain areas where human intuition is irreplaceable.

Automate Everything:

While automation can save time, automating every test is neither practical nor cost-effective. Focus on web automation solution repetitive, high-risk, and regression tests while leaving edge cases and one-off scenarios to manual testing.

Test Automation Is a One-Time Effort:

Automation is an ongoing process. Scripts require updates as applications evolve, and frameworks may need to adapt to new technologies or environments.

Anyone Can Automate Without Coding Skills:

While some codeless tools exist, creating robust, maintainable scripts for complex projects often requires programming knowledge. Upskilling in coding is invaluable for QA engineers.

Automation Guarantees 100% Bug-Free Software:

Automation helps detect issues faster but doesn’t eliminate all bugs. It’s a tool to improve quality, not a silver bullet.

By understanding these myths, QA engineers can approach test automation with realistic expectations and use it effectively to enhance software quality.

#test automation solution #ai software testing #web automation software #web automation testing #automation web application #test automation platform #test automation as a service #test data automation #qa automation solutions

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absoftitsolutions · 7 months ago

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#zoho crm #zoho #zoho partner #zoho creator #business #app development #mobile app development #web application development #software #technology #crm #automation #website

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todayiot · 3 months ago

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What is the Use of BLE Mesh?

Bluetooth Low Energy (BLE) mesh is a game-changer for connected devices. It creates a robust, scalable network that allows multiple devices to communicate seamlessly over large areas. From smart homes to industrial IoT, BLE mesh enables secure, low-power communication between sensors, lights, and other devices, making it ideal for automation, remote control, and real-time monitoring. Whether you're controlling a city's streetlights or optimizing energy in a smart building, BLE mesh helps devices work together efficiently.

#BLEMesh #SmartConnectivity #IoTInnovation #SmartHomes #WirelessTech #FutureOfIoT #Automation #iot applications #iotnbo #iot #web development #software development #bluetooth #iotsolutions #iot platform #innovation #iot development services

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atcuality1 · 2 months ago

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Advanced WordPress Security Services for Peace of Mind

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Amazon strives to outpace Nvidia with cheaper, faster AI chips

New Post has been published on https://thedigitalinsider.com/amazon-strives-to-outpace-nvidia-with-cheaper-faster-ai-chips/

Amazon strives to outpace Nvidia with cheaper, faster AI chips

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Amazon’s chip lab is churning out a constant stream of innovation in Austin, Texas. A new server design was put through its paces by a group of devoted engineers on July 26th.

During a visit to the facility in Austin, Amazon executive Rami Sinno shed light on the server’s use of Amazon’s AI chips. This development is a bold step toward competing with Nvidia, the current leader in the field.

The main reason Amazon is developing its own processor is this: it doesn’t want to rely on Nvidia and buy the company’s chips. The expensive Nvidia chips power a big part of the AI cloud business at Amazon Web Services. This business is the most significant growth engine of the company. Thus, the so-called “Nvidia tax” was pushing the company to look for a cheaper option.

Amazon’s chip development program has a dual purpose. Firstly, the project is meant to provide customers with more affordable opportunities for complex calculations and large data volume processing. Secondly, the initiative was developed to preserve Amazon’s competitiveness in the volatile cloud computing and AI industry. This move was also supported by the directions of tech giants such as Microsoft and Alphabet, which are developing custom-made chips to maintain their leadership in the market.

Rami Sinno, director of engineering for Amazon’s Annapurna Labs, a key element of the AWS ecosystem, emphasised that customer demand for more economical solutions to Nvidia’s products is growing. The acquisition of Annapurna Labs in 2015 was a savvy move by Amazon as it enabled the company to lay the groundwork to begin developing popular chips.

Although Amazon’s chips for AI are in their early days, the company has been making and refining chips for other mainstream applications for nearly a decade, most notably its general-purpose chip, Graviton, which is now in its fourth generation. Amazon has announced that its Trainium and Inferentia chips, the company’s latest and strongest, are still in their early days and are specially designed processors.

The impact is potentially huge because the impressive performance underscores the reports by David Brown, vice president of compute and networking at AWS. In this light, it should be acknowledged that Amazon’s in-house chips could deliver up to a 40-50% price-performance ratio improvement compared to Nvidia-based solutions. In turn, this potential improvement could mean considerable savings for AWS clientele deploying their AI workloads.

AWS’ significance to Amazon’s overall business cannot be underestimated. In the first quarter of this year, AWS made up a little under a fifth of Amazon’s total revenue, as its sales soared by 17 per cent year over year to reach $25 billion. At the moment, AWS holds about a third of the global cloud computing market, and Microsoft’s Azure covers about a quarter, or 25%.

Amazon’s commitment to its custom chip strategy was demonstrated during the recent Prime Day, a two-day sales event at Amazon.com. To handle the highly elevated level of shopping as well as streaming video, music, and other content, Amazon deployed an impressive 250,000 Graviton chips and 80,000 of its custom AI chips across its platforms. Adobe Analytics announced record Prime Day results of $14.2 billion in sales.

It seems that as Amazon intensifies its work on the development of AI chips, the industry leader, Nvidia, is not going to remain at the same level. Nvidia’s CEO, Jensen Huang, has presented Nvidia’s latest Blackwell chips, which are scheduled for release later in the year. Their performance has increased significantly, and Huang promised that the new chips are twice as powerful for AI model training and five times faster for inference.

Nvidia’s dominant position in the AI chip market is underscored by its impressive client list, which includes tech giants like Amazon, Google, Microsoft, OpenAI, and Meta. The company’s focus on AI has propelled its market value to a staggering $2 trillion, making it the third most valuable company globally, behind only Microsoft and Apple.

As the AI chip race intensifies, Nvidia is also diversifying its offerings. The company has introduced new software tools to facilitate AI integration across various industries and is developing specialised chips for emerging applications such as in-car chatbots and humanoid robots.

(Image by Gerd Altmann)

See also: Nvidia: World’s most valuable company under French antitrust fire

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, Amazon, Nvidia, processors

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