Automated Testing Procedures

This paper shows that you can fully automate a previously manual, multi-step test process for automotive REST APIs by strategically using Large Language Models. Their system, SPAPI-Tester, accurately generates, runs, and reports on test cases—cutting days of manual work down to seconds, while matching or exceeding human test coverage and detection of real bugs. The success hinges on (i) carefully segmenting the process (so that each step is small, focused, and verifiable), and (ii) leveraging LLMs as a flexible “glue” to overcome data/document mismatches in a robust, trackable way.

🛠️ What Running Test Automation Involves 🔎 📌 On-Demand Test Automation: This approach allows teams to execute test automation whenever there is a requirement to do so. It can be integrated into various stages of the development process, such as during product development, the addition of new features, or when there are new developments in testing methodologies. 📌 Timed Test Automation: Test automation can be triggered based on time. Initially, automation may take minutes due to fewer iterations, but as the number of iterations and version numbers increases, it may take hours. Running automation tests overnight is a common practice to analyze new changes to the software. 📌 Activity-Based Test Automation: As the application grows, developers shift from time-based triggers to activity-based triggers. The goal here is to target changes in the application, which can include updates, new features, or modifications to the existing features. 📌 Regression Testing: Test automation is particularly useful for regression testing, where previously implemented functionalities are tested to ensure that new changes or updates haven't introduced any unintended side effects or regressions. 📌 Parallel Execution: To speed up the testing process, automation tools often support parallel execution of test cases across multiple environments or devices. Parallel execution helps reduce the overall testing time, allowing teams to achieve faster feedback cycles and accelerate time-to-market for their products. 📌 Integration with Continuous Integration/Continuous Deployment (CI/CD): Test automation can be seamlessly integrated into CI/CD pipelines to automate the testing process as part of the overall software delivery pipeline. Automated tests can be triggered automatically whenever new code changes are committed, ensuring that each code change is thoroughly tested before deployment to production. 📌 Reporting and Analysis: Test automation tools often provide detailed reports and analytics on test execution results, including test coverage, pass/fail status, execution time, and more. These reports help stakeholders make informed decisions about the quality of the software and prioritize areas for improvement. 📌 Maintenance and Refactoring: Test automation requires ongoing maintenance and refactoring to keep test suites up to date with changes in the application codebase. As the application evolves, test scripts may need to be updated or refactored to accommodate new features or changes in functionality. 📌 Scalability and Flexibility: Test automation frameworks should be scalable and flexible to accommodate the evolving needs of the organization and the application. Scalable automation frameworks can handle large test suites efficiently, while flexible frameworks allow for easy customization and extension to support new testing requirements.

Robot + Test Automation Tool + Microcontroller = Relief Hardware automation testing challenges had me in a chokehold for years. Until I discovered this approach: The problem: Manual hardware testing controlled my workflow. Cost me: Time, accuracy, and scalability Affected: Product development cycles and quality assurance Tried everything: Manual checks, partial automation, inconsistent results The breakthrough process: 1. Integrated microcontroller-based automated testing systems Immediate relief: Consistent and rapid evaluations with reduced human error. 2. Leveraged robotic automation for physical device testing Momentum built: Enhanced device verification through simulation of real-world conditions. 3. Combined software and hardware testing automation frameworks Freedom achieved: Streamlined processes with scalable, repeatable testing protocols. Result after implementation: → Improved accuracy and reliability in hardware validation → Reduced testing time and operational costs → Accelerated product development and higher quality standards The secret? Harnessing the synergy of microcontrollers and robotics to automate complex hardware testing tasks, enabling precise, efficient, and scalable verification. Your testing challenges have a solution. You just haven't implemented it yet. What hardware-related challenges does your automation testing process face? #TestAutomation #SoftwareTesting #QualityAssurance #TestMetry

Automation ALONE won't give you the coverage you're looking for. It needs to be in line with manual testing ✅ Automation won’t yield instant results ✅ Automation usually comes with high upfront cost ✅ Your mindset is ready. What’s missing for successful adoption?  👉 A clear, step-by-step strategy. Here’s what I've seen working for our customers: 🎯 Define why you're thinking about automation, what the ideal end-state would be and, based on that, you'll be able to define the metrics that will help you measure your ROI (hint: end-state can't be to replace manual testing) 🔍 Evaluate your existing tests to determine which ones are good candidates for automation (hint: need to be run frequently, technically feasible, etc.) 🛠️ Choose tools that best match your team's skills and can scale across teams (hint: if your team can't write code, there are low-code/no code automation tools. If they want to learn how to code, these tools offer an easy on-ramp towards coded automation) 👥 Ensure your team has the necessary skills and training for test automation (hint: don't underestimate the need for proper education around test automation strategy. If you start it wrong, it's hard to scale later) 🌱 Start small and scale gradually (hint: this is key to capture the value/ROI in small steps from the beginning) 📈 Continuously monitor automation performance and refine your strategy (hint: if you're not getting ROI, something is wrong with your automation strategy. Always monitor your metrics) ⚖️ Leverage the strengths of both manual and automated testing for a comprehensive testing approach (hint: all automated testing enables is speed in test execution. Combining both your slower, but critically valuable, manual test executions with your super fast automated test executions will be key to achieving your desired coverage) By following these steps, I've seen our customers navigate the complexities of automation adoption and achieve a more efficient, reliable, and scalable testing process. 🚀 What other advice would you share? 🫵 #AutomationStrategy #SoftwareTesting #TestAutomation #QualityEngineering #SoftwareQuality Derek E. Weeks | Mike Verinder | Lucio Daza | Mush Honda | Gokul Sridharan | Hanh Tran (Hannah), MSc. | Daisy Hoang, M.S. | Parker Reguero | Florence Trang Le | Ritwik Wadhwa | Mihai Grigorescu | Srihari Manoharan | Phuong Nguyen

📌 Manual ETL testing in data warehouse projects can lead to delays in project timelines, accumulation of bugs, and increased project costs. Whereas, Automated ETL testing can: → significantly streamline the testing process,  → save considerable time and resources,  → ensure more reliable and efficient project outcomes. Here is how you can automate ETL testing: 1️⃣ Choose the right tools: When selecting tools, consider: → Data Comparison Tools: Identify discrepancies between source and target data sets. → ETL Testing Frameworks: Provide structure and reusability to automate test cases, scenarios, and workflows. 2️⃣ Outline the test strategy and scope: → Determine Test Coverage: Identify which ETL components, data elements, and transformations to test and frequency → Use Realistic Test Data: Reflect real-world data source/target conditions; synthetic, sample or production data. → Set Up Test Environment: Mimic production environment closely with cloud or on-premise servers, databases, ETL tools. 3️⃣ Develop & implement test cases: Well-designed test cases are key to cover ETL functionality, performance, and security: → Data Quality Checks: Validate data validity, consistency, completeness, and accuracy → Data Transformation Checks: Assess correctness of ETL logic and mappings → Data Loading Checks: Verify efficiency and reliability of loading process (e.g. load times, volumes, errors) 4️⃣ Execute and monitor test cases: → Schedule Test Runs: Automate execution of test scripts → Review Progress Dashboards: Monitor status and results in one view → Follow Best Practices: Use integrated tools, customize test parameters, enable debugging, etc 5️⃣ Review and Report Test Results: → Generate Test Reports: Highlight key findings and insights through charts and graphs. → Utilize Visualization Tools: Connect to test data and ETL tools; enable drilling down into metrics. → Share Interactive Reports: Support collaboration; allow exporting and publishing of final reports Have you considered automating your ETL testing process to save time and resources?

AI-Informed Test Automation Engineers The Reality of Test Automation Today While many imagine test automation engineers spending their days writing new test scripts, the reality is quite different. Most of their time is consumed by maintaining existing test code that breaks due to website changes, and worrying about all the new untested features or backlog of tests yet to be automated. Even more concerning, traditional test automation often takes longer than a typical sprint cycle to implement. This timing gap means new features frequently ship before automated tests are ready, leaving critical functionality to be verified only through manual and infrequent testing. Traditional automation scripts, especially low-code, and no-code solutions, have significant blind spots. They typically follow hardcoded sequences — finding elements, clicking them, entering form values, and verifying specific strings or states. These scripts navigate through pages that might have serious accessibility issues, performance problems, or usability flaws, yet detect none of these issues. The AI-Informed Approach to Test Automation AI-informed test automation engineers transform this landscape in two significant ways: It takes only minutes to AI-Inform existin test automation scripts. Automation engineers need only add a simple ai_check() method to their automation scripts, called at strategic points in their test flowsto add additional test coverage. This addition enables automatic quality checks across nine different dimensions, identifying bugs that traditional automation would miss. This represents a dramatic shift on coverage and value from automated test scripts—when was the last time your test automation actually found a bug? Best Part: A light version is opensource for Python/Selenium/Playwright:. Code and instructions are @ https://lnkd.in/gYwCv-ji The XBOSoft and Checkie.AI Partnership XBOSoft and Checkie.AI have joined forces to identify effective AI integration strategies for software testing. We share our current thinking on how to create AI-Informed versions of traditional testing roles and business processes, with real-world AI Tooling and practices, and we will even some of the things that didn't work well so you don't have to learn the same mistakes 🤔 We have an upcoming free webinar on March 19th to share this vision and what we have learned: https://lnkd.in/giKcfb7C Follow/connect with me here for more details on this topic every day this week.

Traditional automated testing promises efficiency, but the reality is that tests crumble at the slightest UI change. It’s an all too common scenario: Spend weeks writing the perfect test, only for a minor button update to make half your test flash red. This ensues a cycle of constant firefighting that leaves QA teams exhausted and quality taking a hit. But what if tests could evolve as your product does? 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝘀𝗵𝗶𝗻𝗲𝘀. At testRigor, we’ve helped companies like Netflix and Cisco reduce their reliance on implementation details and make their tests more stable and easier to maintain. We do this by marrying AI’s adaptability with human context. How? By allowing tests to be written in plain English. This approach doesn’t just make tests more stable — it captures nuances that often slip through the cracks of traditional automation. Product managers gain direct visibility into test cases, finally bridging the gap between vision and execution. Developers receive clear, actionable feedback, pinpointing issues accurately. QA team tackles complex edge cases and lets AI handle the grunt work. The result? A virtuous cycle of faster iterations, better products, and happier customers. Make your QA process an accelerator, not a bottleneck >> https://lnkd.in/eijgpWTj #AI #Automation #softwareengineering #softwareengineering