DMI (Digital Management, LLC)’s Post

🚀 Explore the Future of Test Automation! 🌐 As our release deadline approaches, discover how our automated testing: 🔊 Adapts to UI Changes with AI ⭕ Automatically Maintains and Improves tests 👨🔬 Requires No Coding Skills 🌐 Works Across Web, Mobile, Desktop, and Mainframe Join the webinar and see how we're transforming software testing! https://bit.ly/3WeelMz #SoftwareTesting #Automation #AI #TechInnovation

The release deadline is fast approaching, and the code must be robust, reliable, and secure. With time running out, how can we automate all the test cases in time for the crucial go/no-go meeting? Test automation doesn't have to be a complex, time-consuming process. It shouldn't demand extensive programming skills and should withstand multiple builds without breaking. Here are five things that will change how you think about software test automation: - Tests can be resilient and keep testing accurately even if the UI changes – the AI can adapt. - Tests can break, but the AI can rewrite them for you – the AI can learn. - Test automation should not require programming skills – let the AI do the tedious stuff. - Tests should follow the test case and be visual, and anyone should be able to understand them. - Test automation should be the same if you are testing for the Web, Mobile, Desktop, or the Mainframe – why would you have separate tools? Join us Tuesday, July 23 @ 11:00 am PT / 2:00 pm ET https://bit.ly/3WeelMz

The release deadline is fast approaching, and the code must be robust, reliable, and secure. With time running out, how can we automate all the test cases in time for the crucial go/no-go meeting? Test automation doesn't have to be a complex, time-consuming process. It shouldn't demand extensive programming skills and should withstand multiple builds without breaking. Here are five things that will change how you think about software test automation: - Tests can be resilient and keep testing accurately even if the UI changes – the AI can adapt. - Tests can break, but the AI can rewrite them for you – the AI can learn. - Test automation should not require programming skills – let the AI do the tedious stuff. - Tests should follow the test case and be visual, and anyone should be able to understand them. - Test automation should be the same if you are testing for the Web, Mobile, Desktop, or the Mainframe – why would you have separate tools? Join us Tuesday, July 23 @ 11:00 am PT / 2:00 pm ET https://hubs.ly/Q02Ht2Nl0

The release deadline is fast approaching, and the code must be robust, reliable, and secure. With time running out, how can we automate all the test cases in time for the crucial go/no-go meeting? Test automation doesn't have to be a complex, time-consuming process. It shouldn't demand extensive programming skills and should withstand multiple builds without breaking. Here are five things that will change how you think about software test automation: - Tests can be resilient and keep testing accurately even if the UI changes – the AI can adapt. - Tests can break, but the AI can rewrite them for you – the AI can learn. - Test automation should not require programming skills – let the AI do the tedious stuff. - Tests should follow the test case and be visual, and anyone should be able to understand them. - Test automation should be the same if you are testing for the Web, Mobile, Desktop, or the Mainframe – why would you have separate tools? Join us Tuesday, July 23 @ 11:00 am PT / 2:00 pm ET https://bit.ly/3zFAryO

The release deadline is fast approaching, and the code must be robust, reliable, and secure. With time running out, how can we automate all the test cases in time for the crucial go/no-go meeting? Test automation doesn't have to be a complex, time-consuming process. It shouldn't demand extensive programming skills and should withstand multiple builds without breaking. Here are five things that will change how you think about software test automation: - Tests can be resilient and keep testing accurately even if the UI changes – the AI can adapt. - Tests can break, but the AI can rewrite them for you – the AI can learn. - Test automation should not require programming skills – let the AI do the tedious stuff. - Tests should follow the test case and be visual, and anyone should be able to understand them. - Test automation should be the same if you are testing for the Web, Mobile, Desktop, or the Mainframe – why would you have separate tools? Join us Tuesday, July 23 @ 11:00 am PT / 2:00 pm ET https://bit.ly/3zLRU8B

Test cases are an essential part of software development. They help ensure that the software works as intended and meets the requirements of the end-users. Here are some things you need to know about test cases. #Testcase #software #testingprocedure #codeautomation #ai #likeforlike #followforfollow

With all the talk about designing software systems for testability, I have a request: Can we go back a few decades and redesign Machine Learning/Artificial Intelligence systems to be able to trace their decisions back to their training? Sure would help with identifying biases, among other things. ___________________ EDIT: ___________________ Thanks Wayne Roseberry and Ihe Onwuka for your thoughts. You clearly have more experience/knowledge of the math behind Machine Learning systems than I do, but it does bring up another interesting point... I often see the premise above used as a reason dedicated testers aren't needed. That is--if the developers have to do all of the testing themselves, they'll redesign the system to make all the testing easy. The discussion in the comments about how what I suggested in my initial post isn't really possible is, then, an example of how that doesn't always work. Sometimes testing is unavoidably hard, complex, fuzzy, and even messy. Dedicated testers can help with that, in addition to the testing that developers can and should be doing on their work.

𝐍𝐚𝐯𝐢𝐠𝐚𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐟𝐫𝐨𝐦 𝐂𝐡𝐚𝐨𝐬 𝐭𝐨 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 (Part 1) - by Vanya Seth 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘦𝘧𝘧𝘦𝘤𝘵𝘪𝘷𝘦 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 𝘧𝘰𝘳 𝘵𝘦𝘴𝘵𝘪𝘯𝘨 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴 𝘣𝘢𝘴𝘦𝘥 𝘰𝘯 𝘵𝘩𝘦 𝘱𝘳𝘰𝘣𝘭𝘦𝘮 𝘥𝘰𝘮𝘢𝘪𝘯 ? 𝘝𝘢𝘯𝘺𝘢 shares 𝘢 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬-𝘰𝘳𝘪𝘦𝘯𝘵𝘦𝘥 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘵𝘰 𝘵𝘢𝘤𝘬𝘭𝘦 𝘵𝘩𝘪𝘴 𝘤𝘩𝘢𝘭𝘭𝘦𝘯𝘨𝘦. Understanding the different problem domains and categorizing applications accordingly, can help us decide the best testing strategy. C͟l͟e͟a͟r͟ ͟(͟D͟o͟m͟a͟i͟n͟ ͟o͟f͟ ͟K͟n͟o͟w͟n͟ ͟K͟n͟o͟w͟n͟s͟ ͟-͟ ͟S͟o͟ ͟S͟e͟n͟s͟e͟ ͟-͟>͟ ͟C͟a͟t͟e͟g͟o͟r͟i͟z͟e͟ ͟-͟>͟ ͟R͟e͟s͟p͟o͟n͟d͟)͟ This is the domain of Known Knowns, where cause-and-effect relationships are clear. We can observe, predict, and make decisions accordingly by sensing, categorizing, and responding to situations based on best practices. e.g: A Simple web application - you can easily observe and predict its behavior, such as loading a webpage or submitting a form, or business case.   C͟o͟m͟p͟l͟i͟c͟a͟t͟e͟d͟ ͟(͟D͟o͟m͟a͟i͟n͟ ͟o͟f͟ ͟K͟n͟o͟w͟n͟ ͟U͟n͟k͟n͟o͟w͟n͟s͟.͟ ͟S͟o͟ ͟S͟e͟n͟s͟e͟ ͟-͟>͟ ͟A͟n͟a͟l͟y͟z͟e͟ ͟-͟>͟ ͟R͟e͟s͟p͟o͟n͟d͟)͟ Here Cause and Effect are hard to spot. We require experts to analyze, comprehend, and forecast effectively. E.g. Managing a monolithic application with variable CPU and memory spikes during peak loads illustrates the need for specialized knowledge. While best practices may not always be available, adopting "Good Practices" can help in understanding and addressing the problem.   C͟o͟m͟p͟l͟e͟x͟ ͟(͟D͟o͟m͟a͟i͟n͟ ͟o͟f͟ ͟"͟U͟n͟k͟n͟o͟w͟n͟ ͟U͟n͟k͟n͟o͟w͟n͟s͟"͟ ͟-͟ ͟S͟o͟ ͟P͟r͟o͟b͟e͟ ͟-͟>͟ ͟S͟e͟n͟s͟e͟ ͟-͟>͟ ͟R͟e͟s͟p͟o͟n͟d͟)͟ Here cause and effect are unclear, and we must evaluate and experiment to understand the subject. Use of Observability & Monitoring Logs, Traces, signals can be useful. E.g A Microservice Ecosystem, different services maybe in different state (Running, Non Operational etc.). Probing with Observability and Monitoring, can help to understand the inner signals the application emits and drive the testing accordingly.   C͟h͟a͟o͟t͟i͟c͟ ͟(͟D͟o͟m͟a͟i͟n͟ ͟o͟f͟ ͟R͟a͟n͟d͟o͟m͟n͟e͟s͟s͟ ͟-͟ ͟S͟o͟ ͟A͟c͟t͟ ͟-͟>͟ ͟S͟e͟n͟s͟e͟ ͟-͟>͟ ͟R͟e͟s͟p͟o͟n͟d͟)͟ Finding solutions is going to be difficult, if not impossible. In such domain - "Acting First" to stabilize the system is the priority. We can understand the root cause and address a creative solution comes last. E.g. Serv 1 Ticket in production for a Highly Critical Feature. Restoring the outage, and previously working state is priority, rather than solutioning a fix.     This is all about the "𝗖𝘆𝗻𝗲𝗳𝗶𝗻 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸" and we have more from Vanya Seth. > Complexities of LLM Based Applications > Challenges, Evaluating LLM Applications > Testing Personas and Full Stack Testing Watchout #saiprasadsabeson. We will continue to explore all the above in my next post. #TribeQonfReporter #TheTestTribe #TribeQonf #AI

🚀 𝗨𝗻𝗹𝗼𝗰𝗸 𝗣𝗲𝗮𝗸 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗧𝘂𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻! 🚀 Is your application running slower than you'd like? Performance tuning and optimization are crucial for delivering high-quality, efficient software. Whether you're a developer, architect, or IT professional, mastering these best practices can significantly enhance your projects. 🔧 𝗧𝗼𝗽 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝘂𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝗣𝗿𝗼𝗳𝗶𝗹𝗲 𝗬𝗼𝘂𝗿 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Identify bottlenecks using profiling tools. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲: Refactor inefficient code and leverage asynchronous programming. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Indexing, query optimization, and proper use of transactions. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Reduce memory leaks and optimize garbage collection. 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺: Utilize multi-threading and asynchronous operations. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Efficient use of CPU, memory, and I/O resources. 💼 𝗪𝗵𝘆 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲? 𝗨𝘀𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: Faster applications provide a better user experience. 𝗖𝗼𝘀𝘁 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Optimized applications use fewer resources, reducing costs. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: High-performance apps handle increased loads better. 📚 𝗟𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗘𝘅𝗽𝗲𝗿𝘁𝘀! Step-by-step guides on profiling and optimizing your applications. Real-world case studies highlighting common pitfalls and solutions. Practical tips to implement immediately for noticeable improvements.

Much of the initial effort in software development doesn’t just involve writing code, but also requires the process of handling every possible condition the program will encounter. When a developer overlooks a possible combination of inputs or events, the application may perform the wrong function. This is a common situation, particularly when it involves the interaction between dozens of different conditions and actions. Automatically Validating and Completing these rules in an AI enabled decision table like the videos on https://lnkd.in/gEDDTBK5 show, prevents those errors. How do you approach resolving these kinds of errors when encountered in your applications? #DecisionTable #DecisionTables #C #ProgramLogic #RulesManagement