Key Performance Testing Strategies

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 🧐

When we do performance testing, we want to do both mixtures of operations in parallel to understand how the service behaves under anticipated loads, as well as operations insulated in consecutive execution to understand how the individual operations behave. Both types of performance test provide useful information, often the results of the two together explain something not obvious from only one. I saw something this week I have seen many times before. A run of parallel execution, built in anticipation of real-world load, was yielding latencies much higher than target across the board. Even though we were able to isolate the system resources at fault, we couldn't tell if all the operations were having problems, or if one of them was starving resources the others needed. We executed the same set of operations, but one at a time without the others in parallel, so we could get percentile distributions on each one. Only one of the operations was exceeding latency targets, everything else was well within goal. That one operation on its own was using resources the other services needed. That information in hand, we knew where to begin fix investigations. After getting isolated measurements, the next step is investigation, which varies based on what the measurements show. Is it in a front end, a database, CPU, disk, network IO, thread pools, memory utilization, connection pools, or some other resource? What you need to look at is made much simpler when you have the two sets of results guiding you toward further analysis. #softwaretesting #softwaredevelopment #performancetesting Prior articles and cartoons of mine can be found in my book Drawn to Testing, available in Kindle and paperback format. I'm watching how sales of this first go. If it does well, I will collect my newer articles into another edition, so if you like my cartoons and want more, spread the word! https://lnkd.in/gB4NS4BS

Every Black Friday, PMs make the same mistake. Functional testing ≠ Load testing. Know the difference. Black Friday deal goes live. First minute: 12,000 concurrent users. Second minute: system timeout errors. Third minute: angry customers on social media. Fourth minute: CEO calling you. The PM kept saying "it passed all our tests." Here's what happened: They tested if the checkout button works. They never tested if it works with 12,000 people. This is your system under load. 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲? 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 asks: → Does this feature work? → Can users add items to cart? → Does the checkout button respond? → Are orders processed correctly? 𝗟𝗼𝗮𝗱 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 asks: → Does this feature work under pressure? → Can 5,000 users add items simultaneously? → Does checkout work with 1,000 concurrent transactions? → Can your database handle 10,000 queries per second? One tests if your car starts. The other tests if it can handle a cross-country road trip. 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱: 𝗕𝗮𝘀𝗲𝗹𝗶𝗻𝗲 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 = Regular Tuesday performance. 100 concurrent users browsing casually. 𝗟𝗼𝗮𝗱 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 = Expected peak traffic. 2,000 users during your planned sale. 𝗦𝘁𝗿𝗲𝘀𝘀 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 = Breaking point discovery. Push to 5,000 concurrent users and identify what fails first. 𝗦𝗽𝗶𝗸𝗲 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 = Sudden surge survival. Everyone hitting "Buy Now" at the exact same second. 𝗦𝗼𝗮𝗸 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 = Sustained pressure endurance. Black Friday isn't one hour—it's 12+ hours of continuous chaos. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗰𝗼𝘀𝘁𝘀 𝗼𝗳 𝘀𝗸𝗶𝗽𝗽𝗶𝗻𝗴 𝗹𝗼𝗮𝗱 𝘁𝗲𝘀𝘁𝗶𝗻𝗴: → System crashes during your biggest revenue opportunity → Emergency production fixes cost 10x more than testing → Dev team working weekends to patch live systems → Customer trust destroyed in minutes → Competitors capture the market share you lost Your users don't care that checkout worked in testing. They care that it works when THEY need it— During your biggest moment of success. Testing for functionality is necessary. Testing for scale is survival. 🧡 New to PM? Follow for practical leadership tips. ♻️ Repost to empower your network.

API Testing Guide for Key Test Types and Best Practices 🔥 API failures can disrupt systems, compromise security, and frustrate users. Understanding API testing types and their applications is a common challenge. Here’s an optimized breakdown: 1/ Validation Testing ⭢ Ensures API meets functional and business requirements. 🔸Contract Testing: Verifies requests and responses follow agreements. 🔸Schema Validation: Confirms data structures match expected formats. 🔸Data Integrity Testing: Ensures data consistency and accuracy. 2/ Integration Testing ⭢ Validates system and component interactions. 🔸Component Integration Testing: Assesses module connections. 🔸Third-party Integration Testing: Evaluates interactions with external APIs or services. 3/ Security Testing ⭢ Identifies vulnerabilities and secures data. 🔸Penetration Testing: Simulates attacks to uncover weaknesses. 🔸Authentication Testing: Validates identity mechanisms. 🔸Authorization Testing: Ensures access control. 🔸Data Encryption Testing: Verifies secure data handling. 4/ Performance Testing ⭢ Measures API speed, responsiveness, and stability. 🔸Load Testing: Evaluates expected traffic performance. 🔸Stress Testing: Assesses behavior under extreme conditions. 🔸Spike Testing: Tests response to traffic surges. 🔸Endurance Testing: Ensures long-term operational reliability. 5/ Stability Testing ⭢ Ensures consistent performance over time. 🔸Endurance Testing: Validates sustained operations. (Same test, different goal) 🔸Failover Testing: Tests recovery mechanisms during failures. 6/ Scalability Testing ⭢ Assesses system’s ability to scale with demand. 🔸Horizontal Scaling Tests: Distribute workloads across servers. 🔸Vertical Scaling Tests: Tests increased system resources. Best Practices ▪️Automate Where Possible: Streamline repetitive tests like regression and load testing using automation tools to save time and reduce human error. ▪️Test in Staging Environments: Ensure tests are conducted in environments that closely mimic production for accurate results. ▪️Focus on Edge Cases: Ensure tests cover a variety of edge cases to catch unexpected failures or performance issues. ▪️Continuous Monitoring: Regularly monitor API performance post-deployment to quickly identify and resolve potential issues. What’s the biggest misconception you've encountered about API testing in your teams? 📷 Want more bold and high-energy visuals like this? Follow Sketech I'm Nina, Software Tech Lead & PM, crafting tech visuals engineers trust.

During my recent #interview for the Test Lead position at a leading product based company, I was asked to outline a test strategy for a call center flow that includes components such as language preference, issue types, new/existing plans, and agent availability. The interviewer emphasized the importance of identifying bottlenecks, particularly with a focus on API testing. 1. Understanding the Call Center Flow The call center flow consists of: - Language Preference: Allowing customers to select their preferred language. - Types of Issues: Handling various customer issues effectively (e.g., billing, technical support). - New/Existing Plan: Differentiating between new and existing customers. - Agent Availability: Ensuring calls are routed to available agents based on the above conditions. 2. Identifying Potential Bottlenecks I identified several key areas where bottlenecks could occur: - Language Handling: Quick switching between languages without delays. - Routing Logic: Efficient and responsive call routing based on issue types and customer status. - Agent Availability Checks: Real-time accuracy in reflecting agent availability. - Load Handling: System performance during peak call times when multiple calls are initiated. 3. Manual Testing Strategy To address the flow manually, I proposed: - Scenario Testing: Creating comprehensive test cases covering all possible user paths and edge cases. - Exploratory Testing: Conducting exploratory tests to uncover hidden issues, especially regarding language preferences and issue categorization. - User Acceptance Testing (UAT): Engaging real users to validate the flow against business requirements. 4. API Testing Strategy Given the interview's emphasis on API testing, I outlined a focused strategy: a. Identify Key APIs - Language Preference API - Issue Routing API - Customer Status API - Agent Availability API b. Automation of API Tests - Using tools like Postman and Library as RestAssured for automation. - Automating tests for various scenarios, including successful calls with different languages, correct routing for issue types, and validating customer status. c. Load and Performance Testing - Conducting load tests on APIs to assess performance under peak conditions, utilizing tools like JMeter or Gatling. d. Continuous Testing - Integrating API tests into the CI/CD pipeline to ensure rapid feedback and improve reliability with every code change. #Conclusion In summary, I emphasized the importance of identifying bottlenecks and implementing a robust API testing strategy to ensure a smooth and efficient call center flow. This dual approach of manual and automated testing not only mitigates risks but also enhances the overall user experience.