Software Testing Basics

Software quality now depends on the constraints you set around your agents. When humans manually wrote most of the code we could look at the code itself for signs of quality. Is it clean? Is it thoughtful? Is it fast? Can another engineer understand it? Does it have tests? Agents can now generate more code than people can read. When code generation scales beyond review, quality - checks for one or more of correctness, maintainability, security, performance etc - increasingly has to live somewhere else.  It moves into the harness, environment and operating system around the agent. This can be the tests and deterministic checks that decide what the system is allowed to do (amongst others). Your constraints are what may eventually enable loops of agents to deliver production software reliably. They can include unit tests, property tests, acceptance tests, mutation testing and quality metrics. This back-pressure lets the system resist bad work before it becomes somebody elses problem. Set your constraints. They decide whether the code your agents generate is good enough to ship. #ai #programming #softwareengineering

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

I wrote the perfect test case. Then, the bug hit production. And I said it... “Oops, I missed that bug.” But that moment made me ask a better question: What if we designed our systems to make mistakes more challenging to make in the first place? Enter: A brilliant (and wildly underrated) concept from Toyota’s production line— Poka-Yoke. 👀 What’s that? It means “mistake-proofing.” Not fixing bugs. Not catching them late. But stopping them before they ever happen. This blew my mind. And it’s not just for factories. It’s powerful for software, too. Here’s how Poka-Yoke shows up in testing: 🧩 Form Field Validations → Stop lousy input before it enters the system. ⚙️ Environment Pre-checks → Is the test environment right? The test doesn’t run. 🧹 Code Linters & Static Analysis → Catch issues before you ever hit “merge.” 🚫 CI/CD Pipeline Guards → Fail early if the code doesn’t meet the bar. 🖱️ Disable Buttons Until Fields Are Filled → A tiny UX tweak = huge bug savings. But here’s the real lesson: Poka-Yoke isn’t just a tactic. It’s a mindset shift. From reactive QA → to proactive quality engineering. 💬 Your turn— Where could a little mistake-proofing save you a massive headache in the future? #SoftwareTesting #QualityEngineering #Pokayoke #TestMetry

Don’t Focus Too Much On Writing More Tests Too Soon 📌 Prioritize Quality over Quantity - Make sure the tests you have (and this can even be just a single test) are useful, well-written and trustworthy. Make them part of your build pipeline. Make sure you know who needs to act when the test(s) should fail. Make sure you know who should write the next test. 📌 Test Coverage Analysis: Regularly assess the coverage of your tests to ensure they adequately exercise all parts of the codebase. Tools like code coverage analysis can help identify areas where additional testing is needed. 📌 Code Reviews for Tests: Just like code changes, tests should undergo thorough code reviews to ensure their quality and effectiveness. This helps catch any issues or oversights in the testing logic before they are integrated into the codebase. 📌 Parameterized and Data-Driven Tests: Incorporate parameterized and data-driven testing techniques to increase the versatility and comprehensiveness of your tests. This allows you to test a wider range of scenarios with minimal additional effort. 📌 Test Stability Monitoring: Monitor the stability of your tests over time to detect any flakiness or reliability issues. Continuous monitoring can help identify and address any recurring problems, ensuring the ongoing trustworthiness of your test suite. 📌 Test Environment Isolation: Ensure that tests are run in isolated environments to minimize interference from external factors. This helps maintain consistency and reliability in test results, regardless of changes in the development or deployment environment. 📌 Test Result Reporting: Implement robust reporting mechanisms for test results, including detailed logs and notifications. This enables quick identification and resolution of any failures, improving the responsiveness and reliability of the testing process. 📌 Regression Testing: Integrate regression testing into your workflow to detect unintended side effects of code changes. Automated regression tests help ensure that existing functionality remains intact as the codebase evolves, enhancing overall trust in the system. 📌 Periodic Review and Refinement: Regularly review and refine your testing strategy based on feedback and lessons learned from previous testing cycles. This iterative approach helps continually improve the effectiveness and trustworthiness of your testing process.

10 Testing Principles That Work (from experience) I am sharing the 10 testing principles that work from my experience Test like a real user Don’t just follow the script, try what a real user might do. That’s where the real bugs live. Make bug reporting easy The easier it is to report and retest bugs, the faster things move. Keep feedback loops short and simple. Use data to test smarter Logs, usage stats, and real errors tell you what to test more. Let the data guide you. Work closely with other teams Quality isn’t just QA’s job; working with the dev, product, and design teams helps catch problems early. Test early, test later too Start testing at the idea stage, and don’t stop after release. Production bugs matter too. Stay flexible and experiment Be ready to adapt. Every build is different; what worked last sprint might not this one. Let testers lead Give testers the space and trust to try new ideas and take ownership. It makes a big difference. Do exploratory testing often Some bugs only show up when you break the rules a bit. Explore, question, and be curious. Good strategy > any tool. Don’t rely on one tool; Tools help, but don’t let them box you in. Think about test upkeep Build tests you won’t dread maintaining. A few good, stable tests beat 100 flaky ones. #testing #qa #testingprinciples #softwaretesting #qa #qatouch #QATouch #bhavanisays

Fixing production bugs is 640x more expensive than during coding. 4 ways to transform how you handle bugs. The later you identify a bug, the more expensive it becomes. That’s why it’s important to design how you work to surface bugs faster. Here are four ways to increase your product quality. 1- Increase quality practices How you work determines the quality of your output. Simple practices can help you reduce bugs or identify them faster: . Prototype testing: Test the usability of your idea with users before implementing it . Code review: A second pair of eyes will help your team uncover undesired behaviors . Unit tests: The boring thing developers hate, but it prevents many future bugs . Automated tests: This one can accelerate tests and uncover undesired side effects . Dog food: Use your product to feel as much as possible like users 2- Delegate highly complex yet standard features Choosing what to delegate is wise. You don’t have to build commodity features. For example, SAML, SSO, and SCIM are immediately available with WorkOS, which can save you time and nerves. Focus on the core of your product and delegate standard features. That removes a heavy burden from your shoulders. 3- Get a software engineer specialized in quality as a role model Software quality can be complex. You can benefit from onboarding someone who’s done that and could act as a role model to other software engineers. Yet, I’m not recommending a classic QA engineer outside the team. I suggest that a team member focused on quality level up the expertise. A software engineer specializing in quality can mentor others who are less experienced. It’s an investment that quickly pays off when you realize your product is more stable and reliable. 4- Continuously review how to improve your work Every team has opportunities to improve. What worked yesterday may not work tomorrow. It’s key to step back and review what pushes you further and what holds you back. I recommend doing an overall quality review once a quarter: . How many bugs did you catch after release compared to the previous quarter? . How’s your test coverage compared to your previous quarter? . Which practices helped you the most? . Which practices slowed you down? . Where do software engineers struggle? Understand the status quo, agree on what to improve, take action, rinse and repeat. – Which other practices do you recommend to avoid painful bugs? Let’s rock the product world together.

The Project Management Triangle suggests that you have to choose between speed, quality, and cost. But is this true for software, too? Recent evidence shows that the triangle needs rethinking. High-quality code doesn't take longer to write; on the contrary. Speed and quality aren't opposing forces -- in fact, quality code is the key to sustained speed, allowing you to ship more faster. What evidence do I have for these claims? Over the past few years, CodeScene's research team has studied the relationship between code quality and business outcomes. Here's what we found:  🎯 "Code quality" can be reliably measured through the Code Health metric (Red, Yellow, Green code).  💡 Teams deliver new features and fix bugs twice as fast in healthy (green) code compared to problematic code.  💡 Green code reduces the risk of cost overruns by 9X, due to less time spent trying to understand the existing solution.  🐞 It also has 15X fewer defects on average than Red code, translating directly into improved customer satisfaction and less unplanned work.  🕺 Green, healthy code cuts onboarding time in half, allowing new developers to contribute faster.  ﹩And even with Green, healthy code, there's a progressive gain to improving code quality. Given these competitive advantages, shouldn't code quality be a standard business KPI?

Hello VLSI Aspirants ..... In a Design Verification interview, candidates are often presented with scenarios that test their understanding of methodologies, tools, and best practices. Here are some common scenarios and challenges that can come up: 1. Basic Functionality Verification Scenario: Verify a simple digital circuit like a 2-to-1 multiplexer or a full adder. Focus: Test cases should cover all input combinations, ensuring correct output. 2. State Machine Verification Scenario: Verify an FSM (e.g., traffic light controller). Focus: Check state reachability, valid transitions, and look for deadlocks or livelocks. 3. Protocol Verification Scenario: Verify communication protocols like UART or SPI. Focus: Data transmission correctness, handling start/stop bits, and error detection. You may be asked to write a testbench simulating protocol behavior. 4. Timing Analysis Scenario: Analyze a circuit with multiple clock domains. Focus: Setup/hold times, metastability issues, and verifying timing constraints. 5. Randomized Testing Scenario: Create a randomized testbench for a complex design (CPU or memory controller). Focus: Constrained random input generation and the use of coverage metrics. 6. Assertion-Based Verification Scenario: Write assertions for design properties. Focus: Use of SystemVerilog Assertions (SVA) or PSL, and their integration into the flow. 7. Coverage Analysis Scenario: Improve functional coverage in verification. Focus: Differentiate between code and functional coverage, and find untested areas. 8. Debugging Verification Failures Scenario: Analyze a failed test case. Focus: Use waveform analysis, logs, and simulation tools to debug. 9. Cross-Platform Verification Scenario: Verify a design across platforms/configurations. Focus: Address compatibility, clock differences, and behavioral consistency. 10. Testbench Architecture Scenario: Build a testbench for a complex IP. Focus: Discuss stimulus generation, scoreboarding, and checkers. Tips for Interview Preparation: 1. Understand methodologies like UVM and OVM 2. Practice writing code snippets for testbenches and assertions 3. Be ready to talk about tools like ModelSim, VCS, or Questa and their usage