Tips for Testing and Debugging

💥 Every SDET should ask these 10 questions when a test fails—before blaming the code You see a test fail. Do you jump to blame the devs? Hold up. Ask these smart debugging questions first: 1. Is the test data still valid? Outdated or deleted data can break your test. ✅ Refresh test data before running. 2. Did the environment change? New deploy? Missing config? ✅ Check logs and environment versions. 3. Is the failure flaky or consistent? Random failures = flaky tests. ✅ Re-run 3–5 times to confirm. 4. Did a dependency fail? External APIs down? Are services unavailable? ✅ Add mocks or retry logic. 5. What changed recently? Code? Config? Infra? ✅ Review git diff or CI logs. 6. Is the selector still valid? UI changes break locators. ✅ Use stable data-testid or CSS selectors. 7. Local vs CI—same result? It works on your machine, but fails on Jenkins. ✅ Match CI environment locally. 8. Are tests dependent on each other? Shared state causes false failures. ✅ Keep tests isolated with setup/teardown. 9. Is the assertion too strict? Minor text/UI changes can fail tests. ✅ Use flexible matchers. 10. Are logs helping or hiding the issue? No logs = blind debugging. ✅ Log clearly: requests, responses, and context. ✅ Great testers don’t blame; they debug with purpose. 💬 Which one do you forget most often? 👇 Comment below or tag a teammate who needs this! 📌 Save this post for your next flaky test moment. #SDET #AutomationTesting #QA #Debugging #FlakyTests #TestAutomation

During the initial phase of my career in VLSI, I realised that writing Testcases is equally important as Testbench development. A Testcase in any language be it Verilog, VHDL, SystemVerilog, and UVM is not only used to verify the functional correctness and the integrity of the design but also point out areas where the Testbench could be improved. Below are the most important category of Testcases which are most critical: [1] Functional Tests --> In this type of test, the functionality or feature of an IP/module or a subsystem is verified. [2] Register-based Tests --> RW Tests, RO/WO Tests, Default Read/Hard reset Tests, Soft reset tests, Negative RO/WO Tests, Aliasing, Broadcasting, etc [3] Connectivity Tests [4] Clock and Reset Tests [5] Boot up Tests, wake up sequence, training sequence tests. For eg. In the case of DDR – MPC Training, RD DQ Calibration, Command Bus training, Write leveling, etc [6] Command and Sequence-based Tests. [7] Overlapping and Unallocated Region tests. [8] Back-to-back data transfer-based tests. [9] UPF Tests --> Power domain, Level Shifter, clock gating, voltage domain, etc [10] Code Coverage Tests --> In this test toggle, expression, branch, FSM, and conditional coverage holes are measured, and depending on the holes, tests are being written to completely exercise the DUT. [11] Functional Coverage Tests --> In these types of test categories, the functionality of DUT is being measured with the help of bins. There are several ways to do it. If there are coverage holes, more bins are coded to cover those areas, complex scenarios are covered with cross coverage, and bins of intersect functionality. [12] Assertions are basically a check against the design. Basically, these are insertion points within the design which improve the observability and debugging ability. The above are some of the categorizations of tests that need to be applied while checking a design but to achieve all the above features, testcases are broadly classified into the following two types: [1] Directed Testcase: These are the scenarios that the verification engineers can think of or can anticipate. [2] RandomTestcase: These are the scenarios where the maximum amount of bugs can be caught. The random seeds will hit many different use cases which can not be anticipated earlier and has the probability to catch the design issues. Ideally, random tests can be classified into the following two categories: [1] Corner cases --> This is the bug that is only possible to catch when many different scenarios are processed together or they overlap and the best way to catch this type of scenario is to run more repeated regression with more seeds. [2] Stress testing -->These types of tests are useful to check the performance and the scalability of the DUT under multiple concurrent activities and unpredictable scenarios. #vlsi #asic #electricalengineering #semiconductorindustry

The cheapest place to fix a mistake in a software project is on a piece of paper, not in six months of code. Writing a clear requirement is a great start. But the real skill, the thing that separates a good project from a great one, is actively trying to break the logic before you build it. Here at Full Metal, we call this pre-emptive debugging. We map out the "happy path," where the user does everything perfectly. But then we spend more time on the "unhappy paths." We ask a series of 'what if' questions. For a simple password reset feature, we'll ask: ❌ What if the user enters an email that isn't registered? ❌ What if they click the reset link after it has expired? ❌ What if they try to reuse an old password? Each of those 'what ifs' becomes a new requirement, closing a loophole that could have caused problems down the line. It's about finding flaws where they're free to fix. This also helps us avoid common pitfalls I've seen time and again. The biggest is the ambiguity trap: using fuzzy words like "fast" or "easy." My "fast" is not your "fast." Instead of "The system should be quick," we define it: "The system shall return a response within 500ms." One is a wish; the other is a testable fact. This meticulous approach might seem like a lot of work up front, but it saves a fortune in rework and frustration later on. We explore these common pitfalls and how to avoid them in our latest blog for SME leaders. Find the blog here: https://lnkd.in/eptHVTKA Have you ever had a project go a bit pear-shaped because of a single, unasked 'what if' question? #SoftwareEngineering #RiskManagement #DigitalTransformation

Stop guessing why your Linux process is failing. Open a portal to its state. Every running process on Linux has a dedicated directory at /proc/[pid]. It’s a live window into how the kernel sees your application. Instead of restarting your app and hoping for the best, you can use these three files to debug it in real-time: 1. /proc/[pid]/cmdline When top or ps only shows "python3" or "node," this file gives you the truth. It contains the full command-line arguments used to launch the process. It's the quickest way to verify if a service started with the correct configuration flags. 2. /proc/[pid]/environ Environment variable issues are the root cause of many "works on my machine" bugs. This file shows the exact environment the process inherited at runtime.Tip: Use cat /proc/[pid]/environ | tr '\0' '\n' to make the null-separated output readable. 3. /proc/[pid]/fd This directory holds symlinks to every file, socket, and pipe the process has open. If you’re hit with "Too many open files" errors or need to know exactly which log file a process is writing to, ls -l in this folder provides the answer. The Workflow: 1. Grab the PID: pgrep [process_name] 2. Verify launch: cat /proc/[pid]/cmdline 3. Verify config: cat /proc/[pid]/environ 4. Verify resources: ls /proc/[pid]/fd The Linux kernel gives you all the data you need to debug!

How to Vibe Debug 101: “What do I do when the agent repeatedly fails at solving a problem?” First, the most important thing to avoid: Do not just say “it’s not working”. As tempting as it is, LLM performance improves dramatically when given information that it does not already have, or is forced to think about the problem in a way that it hasn’t before. Here’s what you should do instead: 1. Start by describing the symptoms of what you’re seeing in as much depth as possible. E.g. “When I click the download button, it downloads a txt file. When I open the txt file, it showing me weird characters…” 2. Provide a screenshot of the problem. Most vibe coding platforms will allow you to attach an image with your prompt. This will help the agent see what you see and gather additional context. 3. Describe the expected behavior “… what I expect to happen is when I click the download button, for a PDF file to be downloaded. The contents should contain the invoice that is specified by the form.” 4. Ask it to explain what might be happening to you - WITHOUT making changes This is often the most important piece of advice I give to people. Coding LLMs are often over-eager and will bias towards making changes. When debugging a challenging problem, it’s helpful to force it to spend time thinking about the root cause of the problem first. Example prompt: “before making changes, please explain in depth everything that is happening related to what I’m seeing. Explain the most likely causes and, for each, how we would able to verify if that’s actually what’s happening.” 4a. There’s another VERY important reason why you should always do this. When you ask it to explain what the likely cause of the problem is, it helps snuff out when it has fundamentally misunderstood the requirements. It may be doing something it doesn’t need to do (therefore making the code more complex than it needs to be) OR missing an important requirement. 4b. What you think might be the problem might be completely wrong. This is especially difficult for non-coders since you can’t understand what’s actually going on under the hood. For example, let’s say you expect a graph to appear on a page but it’s not showing up. This could be due to: - the data that powers the graph is missing - the graph is only programmed to appear when certain conditions are met - the code for displaying the graph itself has a bug - or really, an infinite number of possibilities If you assume that one of these things are happening without being sure, the LLM will often take your assumption as gospel and go down the wrong rabbit role. 5. This is why you should always, always, always describe symptoms and resist the urge to provide a diagnosis. A good vibe coding product will have tools that help the agent arrive on the correct diagnosis (e.g. ability to inspect logs, run tests, etc.).

Stuck on a coding problem? Here’s how top engineers actually solve them. Whether you’re prepping for interviews or building real-world systems, it’s not just about writing code — it’s about solving problems intelligently. Here’s a 10-step mindset that transforms debugging into breakthroughs: 1. Understand the problem Restate it in your own words. Clarity first, code later. 2. Work through examples by hand Manual tracing helps uncover hidden logic. 3. Break it down Small steps → Simple code → Fewer bugs. 4. Pick the right approach Map it to known algorithms or problem patterns (greedy, sliding window, recursion, etc.) 5. Write pseudocode first Your thinking should be clear before your syntax is. 6. Code in chunks Build incrementally and test as you go. It’s okay, the random print statements are always going to help (just comment them out after ;)) 7. Test edge cases Empty inputs, large datasets, invalid values — test for chaos. 8. Optimize after it works First, get it working. Then, make it elegant and efficient. 9. Stay calm when stuck Take a break. Talk it out LOUD. Google concepts, not answers. Still doesn’t work? Try to get at least one test case. 10. Reflect after solving Ask: What did I learn? What pattern was this? Could I solve it faster next time? ⸻ 💬 Real talk: Being a good coder isn’t about avoiding bugs but about knowing how to find your way out of them.

I was today years old when I discovered the Testing feature in Vscode. That’s what I’ve been missing for years! A good code is a tested code. Sometimes, an error occurs during tests, and it becomes quite hard to understand the source of the error if the exception message is not clear. That’s particularly my case when I develop API with exception handling. I found a way to use the debugger by configuring the launch.json file to handle debugging with pytest. It worked, but the process was still painful. Until I discover that VSCode already implement a testing feature amongst its basic features… 🤦 “That was what this lab bottle was for…” Nobody talks about it, yet it is extremely powerful. So let me do it for you: 🧪 Automatic discovery using Pytest or Unittest Indicate the tests/ folder and let VSCode do the rest. It gives you an overview of all single tests and run them. In one look, you know which tests are failing. 🧪 Run tests individually With my previous setup, I had to run the CLI command to run all tests, even if only one was failing. Lot of time and energy wasted. Now, one click = one test. Just efficient. 🧪 Debugger integrated with Testing Probably the best feature of all! Not only can I run all of a single test, the VSCode debugger also works straight out of the box. It means adding break points and checking the content of data objects. Incredibly useful! Never too late to discover basic features! Have you any other tips I’m missing?

Step 9: Debugging and Optimization in Embedded Systems With your prototype integrated and tested, it’s time to polish it. Debugging and optimization ensure your embedded system is reliable, efficient, and ready for the real world. This step is about fine-tuning hardware and software to perfection—let’s dive in: 1.     Use Tools to Troubleshoot Hardware/Software Issues 👉 Purpose: Identify and fix problems in the system’s operation. 👉Tools: Oscilloscopes: Analyze signal timing or voltage issues (e.g., noisy sensor outputs). Logic Analyzers: Monitor digital signals, like I2C or SPI communication errors. Debuggers: Step through code to find software bugs using tools like JTAG or an IDE’s debugger. 👉Example: For a smart thermostat, you might use an oscilloscope to check a sensor’s noisy signal or a debugger to fix a UART data glitch. 👉Why it’s key: These tools pinpoint issues—whether a hardware misconnection or a software logic error—saving time and preventing failures. 👉Tip: Start with simple tests (e.g., toggle a GPIO) to isolate problems before deep dives. 2.     Optimize Code for Speed, Memory Usage, and Power Efficiency 👉What’s this? Refine firmware to make it leaner and faster. 👉Speed: Streamline algorithms or reduce interrupt latency for responsive tasks (e.g., motor control in a drone). 👉Memory: Minimize RAM and Flash usage, critical for MCUs with limited resources. 👉Power: Implement low-power modes or optimize loops to extend battery life. 👉Example: In a wearable, you’d rewrite a sensor polling loop to use less CPU time and enable sleep modes to save power. 👉Goal: Ensure the system runs smoothly within its constraints, especially for IoT or battery-powered devices. 3.     Refine Hardware Design if Necessary 👉Purpose: Address hardware issues found during testing. 👉Actions: Adjust component values (e.g., resistors for better signal clarity), improve grounding to reduce noise, or swap parts for better performance. 👉Example: A smart lock might need a larger capacitor to stabilize power or a rerouted trace to cut EMI. 👉Impact: Small tweaks can boost reliability and compliance, avoiding costly redesigns later. Why It Matters Debugging and optimization turn a working prototype into a polished product. For an IoT device, you’d fix sensor glitches, slim down code, and tweak the PCB for efficiency. It’s the final push to ensure your system shines in production! 🚀 Ready to Transform Your Product Vision into Reality? Let’s collaborate to build cutting-edge solutions that set your business apart. Get a Quote for our embedded system, IoT Product design services, RF Design and RF Components Design Services, Antenna Design Services, and Product Certification Services: https://lnkd.in/gHzPZhNz #EmbeddedSystem #EmbeddedSystems #IoTInnovation #Innowave #embeddedsystem #IoT #Internetofthings Innowave  

Debugging Low-Level Device Drivers: Techniques for Success🛠️ Developing low-level device drivers is challenging enough, but debugging them can be a whole new level of complexity. When you're working close to the hardware — initializing peripherals, managing registers, or handling interrupts — small errors can lead to system crashes, silent failures, or unpredictable behavior. Here are some essential techniques for debugging low-level drivers effectively: 🧰 Key Debugging Techniques 🧰 1️⃣ Logging with UART/Serial Output - 📡 Why It Helps: When traditional debugging tools can't be used, adding debug messages via UART or serial output can provide real-time insights. - 🔍 Best Practice: Use concise messages to track the flow of execution, register values, and error states. Ensure logs can be toggled on/off to reduce overhead. 2️⃣ Hardware Breakpoints and JTAG Debuggers - 🛑 Why It Helps: Hardware breakpoints stop the system at specific points, allowing you to inspect memory, registers, and call stacks. - 🔧 Tools to Consider: JTAG/SWD debuggers (e.g., Segger J-Link, Lauterbach, or OpenOCD) enable detailed real-time analysis of the system state. 3️⃣ LED Indicators - 💡 Why It Helps: A simple LED can be invaluable in pinpointing where a driver hangs or fails during initialization. - 🏁 Use Case: Flash different patterns to indicate various states or errors. It's a simple but effective method for quick diagnostics when other options are unavailable. 4️⃣ Oscilloscopes & Logic Analyzers - 📊 Why It Helps: Visualizing signals (e.g., SPI, I2C, GPIO) can confirm if the hardware communication matches expectations. - ⚙️ Use Case: Verify timing constraints, clock signals, and data transfers to detect glitches or protocol issues. 5️⃣ Memory Inspection and Analysis - 🧠 Why It Helps: Low-level bugs often involve incorrect memory access or corruption. - 🔎 Technique: Use debuggers to inspect stack/heap regions or leverage tools like valgrind and memwatchfor dynamic analysis (where applicable). 6️⃣ Kernel Debugging (for OS-Based Drivers) - 🐧 Why It Helps: If you're working with Linux or RTOS-based drivers, tools like kgdb, ftrace, and dmesg, can provide detailed logs and live debugging. - 📄 Tip: Always check kernel logs for error messages and warnings related to driver failures. 7️⃣ Assertions and Watchpoints - ✅ Why It Helps: Adding assertions ensures that critical conditions are met during execution. Watchpoints let you track specific memory addresses for unexpected changes. - 🚨 Best Practice: Use assertions to catch anomalies early and identify code paths that lead to hardware misbehavior. 🛠️ Practical Tips 🛠️ ✅ Incremental Development: Develop and test driver functions step-by-step to isolate issues more easily. ✅ Code Reviews: Peer reviews plays very important role. #EmbeddedSystems #LowLevelDrivers #Debugging #FirmwareDevelopment #TechInsights

🚨 STOP Before You Route That Trace You might be thinking layout… But your MCU schematic isn’t ready yet — and it’s about to cost you. Over the years, I’ve reviewed dozens of embedded hardware designs and mentored engineers. One pattern keeps repeating: ⛔️ Rushing to layout with a half-validated schematic = costly PCB respins and debug nightmares. Let’s break it down 🔍👇 💥 1. Power Rail Assumptions Kill Boards “I connected VDD and GND — good enough.” 👉 Nope. Did you size your bypass caps correctly? 👉 Are analog and digital domains isolated or at war? 👉 Did you verify power-up sequencing, brown-out thresholds, and inrush limits? Use the datasheet AND the reference manual. Some MCUs require sequencing that’s not obvious from the block diagram. ⚡️ 2. Decoupling: Not Just a Checkbox Slapping a few 100nF caps on VDD pins? Try again. 📏 Calculate placement by pin inductance. 💡 Add bulk caps based on load step current. 🛑 And NEVER daisy-chain supply lines across multiple ICs without local decoupling. 🔄 3. Reset, Boot, and Clock Configuration = The Heartbeat Your MCU won’t even wake up if: Reset is floating or bouncing BOOT0 is misconfigured Your crystal doesn’t meet ESR or load cap requirements And yes, I’ve seen engineers debug for days just to realize… a missing pull-up. 😬 🧰 4. Programming & Debugging Interfaces: Design for the Future You Don’t trap SWD, JTAG, or UART lines under BGA balls. ✅ Use test points or edge headers ✅ Leave space for scope probes ✅ Add 1k series resistors on debug lines to avoid contention You’ll be grateful when your firmware misbehaves just before a client demo. 🛡️ 5. Real-World Protection = ESD + EMI Defense Your lab is calm. The field is not. TVS diodes on USB, UART, GPIOs PTC fuses on power inputs RC filters for noisy ADC pins Ferrites on analog power 🌐 And yes, common-mode chokes for Ethernet! 📐 6. Think Layout Before You Even Start 💡 Ask yourself: Will this schematic allow a clean ground plane? Can I route clocks short and shielded? Are high-speed interfaces length-matched and impedance-controlled? A layout-aware schematic saves you DAYS later. 🎯 Golden Rule: Your schematic isn’t just a functional diagram. It’s the blueprint for signal integrity, power stability, manufacturability, and sanity. 📌 Respect it. Simulate it. Review it. Challenge it. Then — and only then — click “Switch to PCB.” Till next post 😉 👇👇👇👇 🔜 Formation en Conception de PCB avec Altium Designer Pro: De la Conception à la Fabrication 📆 limite d'inscription: 30 mai 2025 🔗 Lien d'inscription: https://lnkd.in/dEwR3eX4 © Thamer HW-Expert