Introducing DevAssure: a Gen-AI tool

**How to Improve Testing and Deliver Better Products** Testing is not just about finding bugs; it's about building confidence in the product. To improve testing processes and outcomes, here are a few practical tips: 1. **Understand the Big Picture**: Testing is not just about features but the overall user experience. Always ask, "Does this solve the user's problem?" 2. **Start Early**: Don’t wait until the last phase. Involve testing from the design and development stages to catch issues early. 3. **Communicate Clearly**: Collaborate with developers and stakeholders to understand requirements and share findings in a way that drives action. 4. **Leverage Tools**: Use automation where it makes sense but don’t ignore the importance of manual testing for exploratory and creative scenarios. 5. **Think Like a User**: Step into the shoes of real users. Test for edge cases, usability, and performance under real-world conditions. 6. **Learn and Adapt**: Testing is a continuous process. Analyze past mistakes, track metrics, and refine your approach. Remember, better testing leads to better products, and better products lead to happy users. Let’s make quality our shared responsibility. What steps do you take to improve your testing? Let’s discuss! #SoftwareTesting #QualityAssurance #TestAutomation #ContinuousImprovement #CollaborationInTech #UserExperience #AgileTesting #TechInnovation #SoftwareDevelopment #TestingMindset #TestEarlyTestOften #QualityFirst #Testers #SoftwareQuality #ContinuousLearning #TechTeams

Here is an interesting choice regarding priorities. You have two objectives that you are using suites of automated end-to-end checks to meet. One is "Detect problems that ought to stop deployment of the release to production" and the other is "Help developers find problems in their code before they submit to the repository." You have not built either of these yet. You have a set of end-to-end automated checks that so far have been run by the authors of those checks from their own machine, but the mechanism for other people to run them or to trigger them automatically (report results, etc.) has not been put together yet. Which do you prioritize to implement first? Do you prioritize deployment because it has the most critical impact, would have the most conservative set of checks, the more stringent need to run with reliable results? Do you prioritize developers finding problems because it has the most coverage potential, has the lower reliability threshold requirements and thus easier to work on first? I don't believe the answer is the same in all conditions. I believe the product maturity, engineer experience, state of the automation framework and scripts, and maybe even the mood of the team might indicate the better choice. I believe either path is going to introduce a lot of "oops" moments that need to get addressed. Add to this "is there a right way?" question, consider that a lot of people dismiss end-to-end automation as a no-starter project from the get-go. I understand that argument, but stand puzzled how people are content with evaluation that never looks at the product running as a whole, so count me in the "Yeah, but..." crowd on that one. That said, I do consider it a valid point, and don't leap into end-to-end automation lightly if somebody has a strong alternative (and if you don't have a strong alternative, there is probably something very wrong in the project). So, there you go, a question posed where the answer is more questions. Welcome to reality. If anybody sells you something different, well... yeah, they were selling you something, and that merits thinking about. #softwaretesting #softwaredevelopment #ifyoureadbetweenthelinesonmypostsyouwillhaveaprettygoodideawhatproblemsIamworkingon

🚀 Bridging the Gap Between Engineering and Business 🚀 As both an engineer and an entrepreneur, I’ve had the privilege to see product development from two very different perspectives. What may seem like an ideal development process to an engineer can look entirely different to a business. But there’s one crucial element that brings both views together: the need for deep and thorough testing from the earliest stages. 🛠️ Why Testing Matters More Than You Think 🛠️ Testing isn’t just about ensuring your product is bug-free—it’s a strategic investment that delivers value across the board: For business owners: 👉 Helps reduce long-term development costs by enabling faster iterations and improving product stability. 👉 Allows your team to focus on building new features rather than fighting fires. For engineers and testers: 👉 Simplifies workflows by automating repetitive tasks. 👉 Reduces human error during regression testing. 👉 Provides a reliable source of documentation to trace every functionality. Ultimately, better testing means a better product experience for your users and a smoother path to achieving business goals. ⚙️ This Insight Led Us to Create DogQ ⚙️ DogQ is a no-code testing automation tool designed for both business owners and engineers. Whether you have experience in automation or not, with DogQ, you can build reliable test frameworks and automate the execution and storage of your test cases and routine tasks. I’m thrilled to see how our solution is already making an impact, as reflected in being named Product of the Day on Product Hunt and Startup of the Year by HackerNoon. 🔗 In the upcoming weeks, I’ll be sharing my thoughts on product testing from both the engineering and business perspectives. If you're passionate about quality products and better development practices, I invite you to: 👉 follow our page (DogQ) 👉 check out site https://dogq.io/ 👉 and join the discussion! 💬 What’s your take on the importance of testing automation? I’d love to hear your thoughts and experiences in the comments below!

Here is an interesting choice regarding priorities. You have two objectives that you are using suites of automated end-to-end checks to meet. One is "Detect problems that ought to stop deployment of the release to production" and the other is "Help developers find problems in their code before they submit to the repository." You have not built either of these yet. You have a set of end-to-end automated checks that so far have been run by the authors of those checks from their own machine, but the mechanism for other people to run them or to trigger them automatically (report results, etc.) has not been put together yet. Which do you prioritize to implement first? Do you prioritize deployment because it has the most critical impact, would have the most conservative set of checks, the more stringent need to run with reliable results? Do you prioritize developers finding problems because it has the most coverage potential, has the lower reliability threshold requirements and thus easier to work on first? I don't believe the answer is the same in all conditions. I believe the product maturity, engineer experience, state of the automation framework and scripts, and maybe even the mood of the team might indicate the better choice. I believe either path is going to introduce a lot of "oops" moments that need to get addressed. Add to this "is there a right way?" question, consider that a lot of people dismiss end-to-end automation as a no-starter project from the get-go. I understand that argument, but stand puzzled how people are content with evaluation that never looks at the product running as a whole, so count me in the "Yeah, but..." crowd on that one. That said, I do consider it a valid point, and don't leap into end-to-end automation lightly if somebody has a strong alternative (and if you don't have a strong alternative, there is probably something very wrong in the project). So, there you go, a question posed where the answer is more questions. Welcome to reality. If anybody sells you something different, well... yeah, they were selling you something, and that merits thinking about. hashtag #softwaretesting hashtag #softwaredevelopment hashtag #ifyoureadbetweenthelinesonmypostsyouwillhaveaprettygoodideawhatproblemsIamworkingon 5

🚀 Why Manual Testing Remains Essential in the Age of Automation 🤔 As automation testing continues to grow in popularity, it's easy to think that manual testing has become obsolete—but that's far from the case. Manual testing still plays a crucial role in the software development lifecycle, working hand-in-hand with automation. Here's why: 🔍 The Value of Human Insight • Exploratory Testing: Automation can't replace the human ability to identify edge cases and uncover unexpected bugs through intuition. • Usability & UX: Evaluating the user experience and design requires human judgment that scripts simply can't mimic. • Ad-hoc Testing: For quick, impromptu tests, manual methods are often faster and more flexible. 💡 Why Automation Alone Falls Short • While automation excels in repetitive, predictable tasks, it struggles with dynamic or rapidly changing projects. • It demands significant upfront investment for setup, ongoing maintenance, and crafting predefined scenarios. ⚖️ Striking the Right Balance Manual testing is perfect for early-stage validation, smaller projects, and gathering insights on usability. Automation, on the other hand, is best for regression testing and scaling efforts. By combining both, you create a comprehensive strategy that guarantees quality at every stage. 💬 What’s your perspective on the manual vs. automation debate? Share your thoughts in the comments! #ManualTesting #AutomationTesting #QualityAssurance #SoftwareTesting #ExploratoryTesting #UXDesign #AdHocTesting #TestAutomation #TestingStrategy #TechTrends #SoftwareDevelopment #QAInsights #AgileTesting #TechCommunity

Struggling with test creation? Visual Test Design with cTAF is a game-changer!   ✅ Lightning-fast test creation  ✅ Seamless team collaboration  ✅ Drastically fewer bugs  ✅ Proactive issue detection   Transform your QA process and empower your entire team with cTAF's intuitive visual interface. Ready to revolutionize your testing strategy? Dive into our latest blog written by Sriharsha NG to explore the future of visual testing and see how cTAF can elevate your QA game! ➡️ #visualtesting #testingautomation #testdesign

🚀 Boost Software Quality with Test Automation! In today’s fast-paced development cycles, test automation is a game-changer for ensuring software quality while accelerating releases. By automating repetitive, high-value test cases, teams can increase efficiency, maintain consistency, and focus on complex testing scenarios that require human insight. But successful automation isn’t just about writing scripts—it’s about selecting the right test cases, adopting best practices, and leveraging the right tools and frameworks. 🔍 Want to learn how to implement effective test case automation? Our latest guide covers: ✅ Why test automation is essential ✅ How to identify ideal test cases for automation ✅ Best practices for building maintainable automated tests ✅ Step-by-step strategies for success Check out the full guide here: https://lnkd.in/dc6K2KHa #TestAutomation #SoftwareTesting #QualityAssurance #DevOps #AgileTesting

this is excellent use case to automate testing of APIs end to end with Agent AI.

How can we enhance speed and agility in API development with No Code: 👉 Rapid Prototyping :- ◼ Instant Implementation - No-Code platform enables users to create and test API's without writing any code, which allows them to create functional API's in minutes for real time deployment. User can easily modify these API's based on end user feedback which helps in better final product outcome. 👉 Reduced Bottlenecks :- ◼ Better Collaboration - While building API's with No Code, it enhances collaboration between IT and business teams so the development stays on track based on quick feedback, which helps in streamlining processes and faster delivery time. 👉 Enhanced Flexibility :- ◼ Better Adaptability and Integration - No-code platforms offer flexibility to quickly pivot and adapt APIs to new business requirements or market opportunities, and also allows you to integrate with existing systems and platforms, allowing for a seamless flow of information and enhanced operational efficiency. Adopting a no-code approach to API development is not just a technical shift, it’s a strategic move to enhance your business's speed and agility. Empower your team today to innovate rapidly, respond swiftly to market changes, and maintain a competitive edge. Gridzy #NoCode #API #BusinessAgility #DigitalTransformation #Innovation #CompetitiveAdvantage #Gridzy

🌟 Simplifying Test Automation with Katalon Studio! 🌟 In today's fast-paced Agile environments, automating repetitive tasks isn't just an option—it's essential. That's where tools like Katalon Studio shine! Recently, I had the opportunity to work on automating [specific task or feature, e.g., regression testing of a complex web application], and Katalon's intuitive interface, built-in keywords, and cross-browser support made the process seamless. 🔑 Key Takeaways from My Experience with Katalon: 1️⃣ Ease of Use: Its record-and-playback feature is perfect for kickstarting test cases quickly. 2️⃣ Versatility: Supports Web, API, Mobile, and Desktop applications, all under one roof. 3️⃣ Robust Reporting: Detailed logs and reports help in debugging and tracking progress. 4️⃣ Integration Power: Easily connects with CI/CD tools like Jenkins, Git, and JIRA for a smooth DevOps pipeline. 💡 Pro Tip: To make the most out of Katalon, combine its pre-built keywords with custom scripts for advanced test scenarios. If you're looking for a tool to accelerate your automation journey without steep learning curves, Katalon Studio is worth exploring. 🚀 💬 How has Katalon helped in your testing journey? Drop your experiences in the comments, and let’s discuss! #TestAutomation #KatalonStudio #QualityAssurance #SoftwareTesting #SeleniumAlternative #AutomationTools