#automated performance testing

[atozofsoftwareengineering]

Maximizing Software Performance: How to Implement Automated Performance Testing in Agile Processes with Practical Examples

The Agile approach emphasizes the importance of delivering high-quality software quickly and continuously, and performance testing is critical to achieving this goal. Read more...

Automated performance testing is a crucial part of any software development process, especially in agile methodologies. Performance testing helps to identify and eliminate any potential bottlenecks, improve application response times, and ensure that the software is capable of handling a large number of users without compromising on performance. In this blog post, we will discuss how to…

[sensationsolutions]

Looking to improve your software testing process in 2023? The question of whether to use automation testing or manual testing has been a hot topic in recent years, and it's no different this year. Let's dive into the debate and see which one is better for your organization. Read this blog for Automation Testing or Manual Testing

[sdettechnologies]

Application development thrives on agility and speed to market. But in today's multi-cloud landscape, manual workflow processes can act like a handbrake on progress. Multi-Cloud Automation is the key to unlocking true efficiency and innovation in your development cycle. Click Here:https://bit.ly/3PVbu8M

[lokirme]

Bash Script: Calculate before/after 2: Calculate Harder

As an update, or an evolution of my earlier script that did some simple math for me, I’ve made one that will full-on test a URL while I’m making changes to see what the impact performance is of my updates. $ abtesturl.sh --url=https://example.com/ --count=10Press any key to run initial tests...Initial average TTFB: 3.538 secondsPress any key to re-run tests...Running second test...Second average…

[qualibarinc]

www.qualibar.com

Benefits of AI in Software Testing

AI (Artificial Intelligence) brings several benefits to software testing, enhancing efficiency, accuracy, and effectiveness in various aspects of the testing process.

Here are some key benefits of using AI in software testing:

Automation and Speed: AI enables the automation of repetitive and time-consuming testing tasks. This significantly speeds up the testing process, allowing for faster release cycles without compromising quality. Automated testing can run 24/7, providing continuous feedback.

Efficiency and Resource Optimization: AI-powered testing tools can optimize resource utilization by executing tests in parallel, reducing the overall testing time. This efficiency helps in allocating resources more effectively and allows testing teams to focus on more complex and creative aspects of testing.

Test Case Generation: AI can assist in the creation of test cases by analyzing application requirements, specifications, and historical data. This helps in identifying critical test scenarios and generating test cases that cover a broader range of possible scenarios, improving test coverage.

Predictive Analysis: AI algorithms can analyze historical data to predict potential areas of defects or vulnerabilities in the software. This predictive analysis helps testing teams prioritize testing efforts on high-risk areas, improving the overall effectiveness of testing.

Self-Healing Tests: AI can be employed to create self-healing tests that adapt to changes in the application's codebase. When minor changes occur, AI algorithms can automatically update test scripts, reducing maintenance efforts and ensuring that tests remain relevant.

Data-Driven Testing: AI can analyze large datasets to identify patterns and correlations, helping in the creation of realistic and data-driven test scenarios. This ensures that tests are more representative of real-world usage, leading to more effective testing.

Performance Testing: AI can be applied to simulate real-world user behavior and generate realistic load scenarios during performance testing. This helps in identifying and addressing performance bottlenecks and ensuring that the software can handle varying levels of user activity.

Security Testing: AI can enhance security testing by automating the identification of vulnerabilities and potential security threats. AI algorithms can analyze code for patterns associated with security risks and help in the early detection of potential security issues.

User Experience Testing: AI can analyze user interactions and feedback to provide insights into the user experience. This information can be used to optimize the software's usability and identify areas for improvement in terms of user satisfaction.

Regression Testing: AI can efficiently handle regression testing by automatically identifying changes in the codebase and selectively running relevant test cases. This ensures that new updates or features do not introduce unintended side effects.

In summary, integrating AI into software testing processes brings about improvements in efficiency, accuracy, and overall testing effectiveness, ultimately contributing to the delivery of higher-quality software.

[nitor-infotech]

Quality Engineering Services | Nitor Infotech

Nitor Infotech’s agile approach towards quality engineering and test automation services can help organizations achieve a flawless performance of applications and prolonged product sustenance, thus improving scalability as well as boosting revenues. Owing to an increase in demand for better, more flexible software systems, their complexity is increasing day by day. To ensure that these systems comply with quality engineering (QE) standards, a drastic evolution is seen in testing methods as well. Testing frameworks are now more complex than ever and deploying them adequately is often challenging.

[bmpsubmissions]

Quality Engineering Services in Australia - BMP Technologies 

BMP Technologies delivers industry-standard quality engineering services in Australia, employing modern tools and test automation for superior product quality assurance.

[testrigtechnologies]

Top Agile Performance Testing Best Practices 

Agile methodology has become the cornerstone of modern software development, emphasizing collaboration, flexibility, and rapid iteration. With Agile's focus on delivering working software, it's crucial to integrate performance testing seamlessly into the development process.

This article explores the significance of performance testing in Agile and outlines the top best practices to ensure your Agile projects run smoothly and deliver high-performing software. 

Importance of Performance Testing in Agile 

Performance testing is vital in Agile software development as it directly impacts user satisfaction, revenue, and competitiveness. In Agile, quick iterations and frequent changes require continuous performance testing to ensure that the software meets speed, scalability, and reliability requirements. It's a proactive approach to prevent slow performance, which can lead to user abandonment, revenue loss, and customer churn.

By integrating performance testing into Agile processes, teams can detect and resolve issues early, maintain a competitive edge, and ensure software scalability, thereby delivering high-quality and high-performing software. 

Top 8 Best Practices for Agile Performance Testing 

1. Define Performance Acceptance Criteria Early: 

In Agile development, it's crucial to define performance requirements and acceptance criteria early in the project. This means specifying what level of performance the software needs to meet in terms of response times, throughput, and other relevant metrics. 

By incorporating performance criteria into user stories or acceptance criteria, everyone on the Agile team understands and agrees on the performance expectations from the beginning. This ensures that performance is considered a fundamental part of delivering user stories. 

2. Establish a Cross-Functional Team: 

In Agile, cross-functional teams are the backbone of project success. Include performance engineers or specialists as part of your Agile teams. Having their expertise available from the project's inception helps in the early identification and mitigation of performance-related issues. 

Cross-functional teams ensure that performance is not a separate phase but is integrated into the development process, facilitating faster issue resolution and better communication among team members. 

3. Select Agile-Ready Performance Testing Tools: 

Choose performance testing tools that are compatible with Agile practices. These tools should support automation, continuous integration, and the rapid feedback loops that Agile development relies on. 

Agile-ready tools often come with features that allow for easy integration with CI/CD pipelines, enabling automated performance tests to run as part of the development process. 

4. Create Performance Test Data Early: 

Waiting for complete feature development to generate performance test data can lead to delays in testing. Agile teams should create realistic test data as early as possible to ensure that performance tests can be conducted when needed. 

Having test data available upfront also allows for more accurate performance testing that closely mirrors real-world scenarios. 

5. Define Performance Test Scenarios: 

Focus on critical user journeys and define realistic load profiles for performance testing. This ensures that your performance tests simulate actual user behavior and usage patterns. 

Prioritize test scenarios that align with the most important functionality of the application, helping identify and address performance bottlenecks where they matter most. 

6. Shift-Left Testing: 

In Agile, the concept of "shift-left" testing means starting testing activities as early as possible in the development lifecycle. This includes performance testing. 

By identifying performance issues early, teams can avoid costly rework and delays later in the development process. Shift-left testing emphasizes proactive issue prevention rather than reactive issue resolution. 

7. Automate Performance Tests: 

Automation is a cornerstone of Agile practices. Automate the execution of performance test scripts to speed up testing and ensure consistency in testing conditions. 

Automated performance tests can be integrated into the CI/CD pipeline, allowing for regular regression testing and immediate feedback on performance issues with each code change. 

8. Continuous Monitoring: 

Implement real-time monitoring during performance testing and in production. Continuously monitor critical metrics such as response times, error rates, and resource utilization. 

Continuous monitoring ensures that any performance regressions or anomalies are detected promptly, allowing for rapid issue resolution and improved software stability. 

Bottom Line 

Incorporating performance testing into Agile development is essential for delivering software that not only meets functional requirements but also performs optimally in production. These top Agile performance testing best practices ensure that performance testing is an integral part of your Agile process, leading to better software quality, faster releases, and happier users. 

Are you seeking guidance on the ideal timing for conducting performance tests and assessments? Look no further! At Testrig Technologies, we are a leading Performance Testing Company with a wealth of expertise in performance testing methodologies. 

As a top QA Testing Company, our commitment to excellence extends beyond just identifying performance bottlenecks. We work tirelessly to ensure that your applications not only meet but exceed performance expectations.

Contact us today to explore how our specialized expertise can elevate your software's performance and streamline your testing processes.

[market-insider]

Automation Testing Insights: Transforming Testing

The global automation testing market size is expected to reach USD 92.45 billion by 2030. Prominent technological advancement in artificial intelligence (AI) and machine learning (ML) is propelling the demand for the market. Mobile application usage is growing across various end-user industries, and smartphone penetration is rising, opening an attractive potential for market expansion. Furthermore, there is a rising demand for automation testing as web-based applications have developed significantly and new software technologies have emerged. The market is growing rapidly due to the increasing usage of ML and AI for advanced analytics and continuous testing across DevOps and DevSecOps areas.

Automation Testing Market Report Highlights

The services segment dominated the market and accounted for over 56% of the global revenue owing to rapid advancements in implementation services, which make it easier to include automation into a functioning infrastructure for software testing

With the aid of this service, automation is integrated into an existing software automation testing setup

The large enterprises segment held the largest revenue share in 2022 as it helps improve efficiency, reduce manual effort, increase test coverage, and ensure the quality of software applications

The BFSI segment is estimated to have significant growth over the forecast period; adopting digitalization in the BFSI sector creates a significant demand for application software automation testing

Gain deeper insights on the market and receive your free copy with TOC now @: Automation Testing Market Report

The emerging use of RPA to automate time-consuming, error-prone manual processes are just a few instances of the usage of AI & ML in automation testing. Moreover, a bot uses the page’s numerous links and web forms to systematically explore through an online application when web crawling or spidering. This is a new use for AI and ML in automation testing. This approach is typically used for indexing online browsing. It may be improved further to perform reverse engineering on an application being tested and automatically find Test Cases. Emerging automation testing tools are significantly fueling market growth.

For instance, Testcraft, a codeless Selenium test automation platform for regression and continuous testing, as well as monitoring of web applications, is gaining traction among users. Their revolutionary AI tech removes maintenance time and cost, as it certainly affects changes in the app. Similarly, Applitools Eyes, Testim, and Test.ai are more automation testing tools propelling the market growth. Furthermore, mergers and acquisitions by other key players are propelling market growth. For instance, in 2022, to improve the user experience on 5G smartphones, Key sight introduced AI-driven and automated testing. Automation and AI enable mobile service providers and app developers to more swiftly evaluate how smartphone users engage with native apps in the real world.

[atozofsoftwareengineering]

Agile Testing: Delivering High-Quality Software with Continuous Testing and Collaboration

In this paper, we will explore the key principles of agile testing, its benefits, and how it can be implemented successfully in organizations.

Agile testing is an approach to software testing that emphasizes the need for continuous testing and feedback throughout the software development lifecycle. This approach is based on the principles of the Agile Manifesto, which emphasizes collaboration, flexibility, and customer satisfaction. Agile testing has become increasingly popular in recent years as organizations seek to deliver…

[intellitechconsultin]

Optimizing Performance: Comprehensive Performance Testing Services for Superior Results

Intellitech offers cutting-edge Performance Testing Services designed to evaluate and enhance the efficiency and responsiveness of your applications or systems. Our expert team employs industry-leading tools and methodologies to ensure optimal performance, providing you with a competitive edge in today's digital landscape.

[sdettechnologies]

At SDET, we're experts in identifying and eliminating performance bottlenecks to ensure your systems run smoothly and efficiently. With our tailored solutions, you can load up with confidence, knowing that your operations are optimized for peak performance. Say goodbye to slowdowns and hello to seamless productivity! #PerformanceOptimization#EfficiencyBoost #MaximizeProductivity SDET Tech. To Read More: https://bit.ly/45vFyfx

[marrywillson]

Web application testing is a software testing technique that is used exclusively to test web-hosted applications, in which the web application interfaces, and other functionalities are tested.

[coffeebeansconsulting]

CoffeeBeans is a technology company that helps businesses create great products using cutting-edge technology. We’re also a product company, and we create products for various domains primarily built on AI. Our core powerhouse lies in the people who make CoffeeBeans. We value a positive attitude, a sense of ownership, and a can-do spirit. Our mission is to use technology to help businesses achieve their goals and dreams.

URL:- https://coffeebeans.io/

Email:- enquiries@coffeebeans.io