Optimizing Test Systems for Better Performance

Our test suite used to take 47 minutes to run. Nobody waited for it. Developers would push code, start the pipeline, go to lunch, and merge when they got back without checking the results. The tests existed, but they were not actually protecting anything. So I spent a sprint optimizing. By the end of the week, the same suite ran in 8 minutes. Same tests. Same coverage. 10x faster. The team started actually reading the results. Flaky tests got fixed because people noticed them. Bugs got caught because developers waited for the pipeline before merging. Speed is not just a nice-to-have. It is the difference between a test suite that protects your product and one that gets ignored. Here are the 7 optimizations that made it happen. Save this and share it with your team. I guarantee at least 3 of these apply to your suite right now.

Automation is more than just clicking a button While automation tools can simulate human actions, they don't possess human instincts to react to various situations. Understanding the limitations of automation is crucial to avoid blaming the tool for our own scripting shortcomings. 📌 Encountering Unexpected Errors: Automation tools cannot handle scenarios like intuitively handling error messages or auto-resuming test cases after failure. Testers must investigate execution reports, refer to screenshots or logs, and provide precise instructions to handle unexpected errors effectively. 📌 Test Data Management: Automation testing relies heavily on test data. Ensuring the availability and accuracy of test data is vital for reliable testing. Testers must consider how the automation script interacts with the test data, whether it retrieves data from databases, files, or APIs. Additionally, generating test data dynamically can enhance test coverage and provide realistic scenarios. 📌 Dynamic Elements and Timing: Web applications often contain dynamic elements that change over time, such as advertisements or real-time data. Testers need to use techniques like dynamic locators or wait to handle these dynamic elements effectively. Timing issues, such as synchronization problems between application responses and script execution, can also impact test results and require careful consideration. 📌 Maintenance and Adaptability: Automation scripts need regular maintenance to stay up-to-date with application changes. As the application evolves, UI elements, workflows, or data structures might change, causing scripts to fail. Testers should establish a process for script maintenance and ensure scripts are adaptable to accommodate future changes. 📌 Test Coverage and Risk Assessment: Automation testing should not aim for 100% test coverage in all scenarios. Testers should perform risk assessments and prioritize critical functionalities or high-risk areas for automation. Balancing automation and manual testing is crucial for achieving comprehensive test coverage. 📌 Test Environment Replication: Replicating the test environment ensures that the automation scripts run accurately and produce reliable results. Testers should pay attention to factors such as hardware, software versions, configurations, and network conditions to create a robust and representative test environment. 📌 Continuous Integration and Continuous Testing: Integrating automation testing into a continuous integration and continuous delivery (CI/CD) pipeline can accelerate the software development lifecycle. Automation scripts can be triggered automatically after each code commit, providing faster feedback on the application's stability and quality. Let's go beyond just clicking a button and embrace automation testing as a strategic tool for software quality and efficiency. #automationtesting #automation #testautomation #softwaredevelopment #softwaretesting #softwareengineering #testing

𝗗𝗼𝗻’𝘁 𝗚𝘂𝗲𝘀𝘀, 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲: 𝗔 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗧𝗶𝗽 𝗳𝗼𝗿 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Last week, my team almost wasted days optimizing the wrong thing. A specific search function was frustratingly slow for users under load. The gut feel was slow SQL queries - seemed like a simple fix. But if experience has taught me anything, it’s to verify first, no matter how obvious the problem looks. Here’s the 3-step process they followed to get the best result: 1️⃣ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗙𝗶𝗿𝘀𝘁: 𝗨𝘀𝗲 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗮𝗻𝗱 𝗣𝗿𝗼𝗳𝗶𝗹𝗶𝗻𝗴 They could have spent days tweaking queries based on assumptions. Instead, we invested in proper tooling: ✅ 𝗞𝟲 simulating the requests to put the system under a similar load ✅ 𝗢𝗽𝗲𝗻𝗧𝗲𝗹𝗲𝗺𝗲𝘁𝗿𝘆 to capture traces and metrics across all backend systems ✅ 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 as our APM to bring traces, metrics and logs into one place for analysis The surprise: CPU-bound processing was the real culprit, not the database. 2️⃣ 𝗣𝗶𝗻𝗽𝗼𝗶𝗻𝘁 𝘁𝗵𝗲 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 With OpenTelemetry, we could trace requests end to end: ✅ API response times ✅ Business logic execution ✅ Actual SQL query performance ✅ External service calls The reality check: complex nested LINQ logic in the search algorithm was spiking CPU usage, using up more than 60% of request time. 3️⃣ 𝗙𝗶𝘅 𝘄𝗶𝘁𝗵 𝗙𝗼𝗰𝘂𝘀 Once the facts were clear, they could prioritize: ✅ First: Fix the CPU bottlenecks ✅ Next: Still improve the SQL queries and optimize table structures ✅ Throughout: Monitor each change to prove real gains 𝗧𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: performance tuning should never be guesswork. Measure, fix, then measure again. Listen to your gut, but verify first. The time spent diagnosing prevented less-effective "fixes" and saved us days. 𝘞𝘩𝘢𝘵'𝘴 𝘺𝘰𝘶𝘳 𝘨𝘰-𝘵𝘰 𝘵𝘰𝘰𝘭 𝘴𝘵𝘢𝘤𝘬 𝘧𝘰𝘳 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘥𝘪𝘢𝘨𝘯𝘰𝘴𝘵𝘪𝘤𝘴? 𝘈𝘯𝘺 𝘩𝘪𝘥𝘥𝘦𝘯 𝘨𝘦𝘮𝘴 𝘺𝘰𝘶'𝘥 𝘳𝘦𝘤𝘰𝘮𝘮𝘦𝘯𝘥?

Two hours of focused testing every day for a week → far more effective than burning out with 12 hours on a weekend. Tools like Jira, TestRail, or Azure DevOps help you track progress without chaos. One solid bug report with clear steps, logs, and impact → more valuable than logging five vague issues no one can reproduce. Screenshots, HAR files, and logs from tools like Chrome DevTools or Kibana make the difference. Fixing one flaky test properly → more useful than ignoring ten red failures in the pipeline. Using Playwright retries, Selenium waits, or CI logs from GitHub Actions or Jenkins saves hours later. Refactoring an existing automation flow for clarity and reuse → stronger than adding five new scripts no one maintains. Good structure in frameworks using PyTest, TestNG, or Page Object Model pays off fast. Reading logs and tracing a failure end to end → more insightful than raising a ticket and moving on. Centralized logging with ELK, Datadog, or CloudWatch tells the real story. Doing a dry run before execution → more efficient than discovering blockers mid-cycle. Local runs, staging checks, and Postman collections catch issues early. Reviewing and improving old test cases → more productive than blindly adding new ones. Test management tools help you spot duplication and gaps quickly. One real conversation with a developer to clarify behavior → better than writing twenty assumptions in a test plan. A 10-minute Slack or Teams call can save days of rework. Documenting learnings after every release → more effective than starting from zero each time. Confluence pages, Notion docs, or simple markdown notes build team memory. Catching one critical edge case early → more impressive than executing 100 tests that add no value. Exploratory testing supported by logs, metrics, and monitoring tools wins here. Quality doesn’t come from last-minute heroics. It comes from showing up daily, using the right tools, and thinking clearly. QA is not about how many test cases you wrote. It’s about how many problems you prevented. Consistency over chaos. Repost if this reflects how you actually test. Most QA growth doesn’t come from doing more. It comes from doing the right things consistently.

💡 𝟯 𝗙𝗶𝗿𝗺𝘄𝗮𝗿𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗧𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗠𝗮𝘁𝘁𝗲𝗿 After years of working with embedded systems, I've learned that optimization isn't about making everything faster—it's about making the right things better. Here are three techniques that deliver real impact: 𝟭. 𝗗𝗠𝗔 𝗢𝘃𝗲𝗿 𝗣𝗼𝗹𝗹𝗶𝗻𝗴 Stop burning CPU cycles waiting for data transfers. Direct Memory Access frees your processor to handle critical tasks while peripherals move data independently. → Real impact: CPU load reduction of 40-60% in data-intensive applications → When to use: SPI/I2C sensors, UART communication, ADC sampling 𝟮. 𝗜𝗻𝘁𝗲𝗿𝗿𝘂𝗽𝘁 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Not all interrupts are created equal. Strategic priority assignment prevents critical tasks from being starved by less important ones. → Real impact: Eliminates timing issues and missed events → The key: Safety-critical > Time-sensitive > Background tasks 𝟯. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗣𝗮𝗱𝗱𝗶𝗻𝗴 Understanding how your microcontroller accesses memory can dramatically improve performance. Proper alignment reduces memory access cycles. → Real impact: 20-30% speed improvement in struct-heavy code → Bonus: Reduces power consumption on memory-constrained devices The Bottom Line: Optimization is a tool, not a goal. Profile first, optimize second. Focus on bottlenecks that actually impact your system's performance, reliability, or power consumption. What's your go-to optimization technique in embedded systems? #EmbeddedSystems #Firmware #Optimization #Microcontrollers #Engineering #EmbeddedProgramming #IoT #TechTips

Many teams think performance testing means throwing traffic at a system until it breaks. That approach is fine, but it misses how systems are actually stressed in the real world. The approach I’ve found most effective is to split performance testing into two distinct categories: 🏋️♀️ Benchmark testing 🚣♀️ Endurance testing Both stress the system, but they answer different questions. 🏋️♀️ Benchmark Testing: Benchmark tests are where most teams start: increasing load until the system fails. Failure might mean: ⏱️ Latency SLAs are exceeded ⚠️ Error rates cross acceptable thresholds Sometimes failure is measured by when the system stops responding entirely. This is known as breakpoint testing. Even when SLAs are the target, I recommend running breakpoint tests after thresholds are exceeded. Knowing how the system breaks under load is useful when dealing with the uncertainties of production. 🚣♀️ Endurance Testing: Endurance tests answer a different question: > Can the system sustain high load over time? Running at high but realistic levels (often near production max) over extended periods exposes different problems: 🪣 Queues, file systems, and databases slowly fill 🧹 Garbage collection and thread pools behave differently 🧵 Memory or thread leaks become visible These issues rarely show up in short spikes of traffic. If you only run benchmarks, you’ll discover them for the first time in production. ⌛️ Testing Thoroughly vs Deployment Speed: Benchmarks run fast; Endurance testing takes time. A 24-hour endurance test can slow down releases, especially when you want to release the same service multiple times a day. It's a trade-off between the system's criticality and the need for rapid deployments. How tolerant is the system to minor performance regressions? If performance truly matters, slowing releases down to run endurance tests might be the right call. 🧠 Final Thoughts: Effective performance testing isn’t just about surviving spikes. Spikes matter, but so does answering: 📈 Can the system withstand peak load for extended periods? 🔎 If not, how does it fail, and why? All too often, I see the system's capacity become the breaking point during unexpected traffic patterns. While an application might handle spikes, the overall platform often can't sustain them. That's where endurance tests deliver their real value. #Bengineering 🧐

Test/Inference Time Compute vs. Systems 2 Thinking Scaling test-time (or inference-time) compute has been an emerging research direction in the past few months. The core focus of such research is to figure out how an LLM’s performance can improve if it’s given a fixed, substantial compute during prediction. Google Deepmind’s latest research (Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters) makes some promising progress on this front. Humans, when faced with complex problems, tend to think longer and deeper. In such scenarios, our minds perform additional processing compared to when we’re solving simpler and straightforward problems. These 2 types of cognitive processing are popularly known as System 1 and System 2 Thinking, which was introduced by Daniel Kahneman in his book Thinking, Fast and Slow. A good analogy (but not equivalence) for Test-time compute is System 2 thinking, which refers to deep and conscious deliberation and is more suited to complex problem solving tasks. DeepMind's research tries to explore if such capabilities can be instilled in LLMs. Test-time compute can be effective over pre-training, especially because of its practical advantages. At inference time - ↗️ a) the model can iterate and self-improve (no need to retrain models) ↗️ b) smaller on-device models can achieve performance comparable to those deployed in data-centers ↗️ c) compute can be allocated depending on the problem difficulty Here’s what the authors observed in their experiments - 1️⃣ In some settings, it is more effective to pre-train smaller models and apply test-time compute to generate better results. This is, however, limited to easy and intermediate questions and some types of hard questions. 2️⃣ For extremely challenging questions, test-time compute barely shows any advantage - it is more effective to invest in additional pre-training compute in such scenarios. This is expected, given the limitations autoregressive LLMs have in reasoning. 3️⃣ There is no winner-takes-all approach when it comes to test time compute. Different test-time strategies work better in different settings. For e.g. on easier problems, letting the model refine its initial answer by making n sequential revisions worked well. For harder problems, either generating multiple answers in parallel or a tree search against a process-based reward model works better. Planning is often formulated as a search problem, and therefore it’s encouraging to see that search approaches are more promising for complicated problems. We're likely to see more compute spent on inference vs pre-training. Architectural improvements in Open AI’s o1 models and Ilya Sutskever’s “Pre-training as we know it will unquestionably end” remark at NeurIPS a few months back are other signals that support this hypothesis. This however, does not mean that test-time compute replaces pre-training.

We cut a stored procedure's execution time from 40 seconds to under 1 second. The problem: spPartsReceived was timing out applications. Excessive table scans were burning through 687,157 logical reads per execution. The fix: We rewrote sections of the query to eliminate unnecessary scans. Same result set, completely different execution path. Results after optimization: - Duration: 40,000ms down to <1,000ms - Logical reads: 687,157 down to 90 - Scan operations: Reduced by 80% The catch: Row order changed. SQL Server never guarantees row order without ORDER BY, but applications sometimes depend on it anyway. We validated with the client's dev team before deploying. This is why testing matters. Better performance on paper means nothing if it breaks application logic. You need identical functionality, not just identical results. The optimization worked. The app stopped timing out. The client's users got their system back. Performance tuning requires both database expertise and understanding how applications actually use your queries. Read the full technical breakdown and see the detailed metrics: https://lnkd.in/eQXPBMye

How I Used Load Testing to Optimize a Client’s Cloud Infrastructure for Scalability and Cost Efficiency A client reached out with performance issues during traffic spikes—and their cloud bill was climbing fast. I ran a full load testing assessment using tools like Apache JMeter and Locust, simulating real-world user behavior across their infrastructure stack. Here’s what we uncovered: • Bottlenecks in the API Gateway and backend services • Underutilized auto-scaling groups not triggering effectively • Improper load distribution across availability zones • Excessive provisioned capacity in non-peak hours What I did next: • Tuned auto-scaling rules and thresholds • Enabled horizontal scaling for stateless services • Implemented caching and queueing strategies • Migrated certain services to serverless (FaaS) where feasible • Optimized infrastructure as code (IaC) for dynamic deployments Results? • 40% improvement in response time under peak load • 35% reduction in monthly cloud cost • A much more resilient and responsive infrastructure Load testing isn’t just about stress—it’s about strategy. If you’re unsure how your cloud setup handles real-world pressure, let’s simulate and optimize it. #CloudOptimization #LoadTesting #DevOps #JMeter #CloudPerformance #InfrastructureAsCode #CloudXpertize #AWS #Azure #GCP