Coverage Assessment Techniques

🛡️ Measuring real MITRE ATT&CK coverage is hard. Detection rules are only part of the picture — Defender XDR fires tons of alerts with MITRE attribution. Your actual coverage could be 3× what Sentinel's dashboard shows — but proving it means stitching together API's, KQL, and external threat mappings. ⬇️ New agentic skill — 𝗠𝗜𝗧𝗥𝗘 𝗔𝗧𝗧&𝗖𝗞 𝗖𝗼𝘃𝗲𝗿𝗮𝗴𝗲 𝗥𝗲𝗽𝗼𝗿𝘁 for the Security Investigator framework. ⚙️ PowerShell pipeline gathers ALL data deterministically — Analytic rules, Custom detections, Platform alerts, CTID mappings, SOC Optimization recommendations. No LLM in the scoring loop. Reproducible every run. 🎯 🗺️ The 𝗖𝗲𝗻𝘁𝗲𝗿 𝗳𝗼𝗿 𝗧𝗵𝗿𝗲𝗮𝘁-𝗜𝗻𝗳𝗼𝗿𝗺𝗲𝗱 𝗗𝗲𝗳𝗲𝗻𝘀𝗲 (CTID) maps Microsoft security products to ATT&CK techniques (https://lnkd.in/gv9MHNC5). This report classifies platform coverage into three confidence tiers: 🟢 T1: Alert-Proven — Defender alerts fired with MITRE tags in your environment 🔵 T2: Deployed Capability — Defender product is active + CTID confirms detect coverage ⬜ T3: Catalog — CTID maps it, but no alert evidence in your workspace yet The report shows where platform detections fill rule gaps — tactics like Credential Access and Privilege Escalation jump dramatically with MDE behavioral alerts. It also catches untagged rules generating alerts invisible to coverage analytics. 🔍 📋 Sentinel's SOC Optimization recommendations (AiTM, ransomware, BEC, etc.) are cross-referenced — which threat scenarios are active, completed, or dismissed, and how your coverage aligns. 📐 𝗠𝗜𝗧𝗥𝗘 𝗖𝗼𝘃𝗲𝗿𝗮𝗴𝗲 𝗦𝗰𝗼𝗿𝗲 — 5 weighted dimensions: 𝗕𝗿𝗲𝗮𝗱𝘁𝗵 (25%), 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 (10%), 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 (30%), 𝗧𝗮𝗴𝗴𝗶𝗻𝗴 (15%), 𝗦𝗢𝗖 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (20%). Operational is the heaviest weight on purpose — deploying 200 Content Hub templates means nothing if they never fire. 🎯 Breadth is 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀-𝘄𝗲𝗶𝗴𝗵𝘁𝗲𝗱 — each technique gets credit based on its best covering rule: Fired (1.0), Ready (0.75), Partial (0.50), No data (0.25), Tier-blocked (0.0). Rules targeting Basic/Data Lake tables that structurally can't fire? Zero credit. Rules with missing data sources? Discounted. 📊 Deploying rules isn't enough — proving they fire is what counts. Purple team your detections — run Atomic Red Team, watch your score climb. Sentinel's dashboard doesn't reward that. This report does. 💜🔴 What's your 𝗠𝗜𝗧𝗥𝗘 𝗖𝗼𝘃𝗲𝗿𝗮𝗴𝗲 𝗦𝗰𝗼𝗿𝗲!? ⚡ Open source: https://lnkd.in/gV_DmVuS 📄 Example report: https://lnkd.in/gGE4UgUP #MicrosoftSecurity #DefenderXDR #MicrosoftSentinel #MITRE #PurpleTeam #CTID #GitHubCopilot #AgenticAI #KQL #OpenSource #DetectionEngineering #SecOps

🎯 Testing isn’t just about clicking buttons — it’s all about strategy, logic, and insight. As QA professionals, our job isn’t just to “find bugs.” It’s about understanding how systems behave, predicting user actions, and applying the right testing techniques to uncover issues that others might overlook. Here are some key testing techniques every QA engineer should master 👇 ⸻ 🧩 1. Boundary Value Analysis (BVA) Why it matters: Bugs often hide at the boundaries of input ranges. Example: If valid age = 18–60 → test 17, 18, 19, 59, 60, 61. Testing edge cases helps ensure stability where systems are most fragile. ⸻ ⚖️ 2. Equivalence Partitioning Goal: Reduce redundant tests while maintaining coverage. Divide input data into valid and invalid sets, then test one from each group. Example: For a password length rule (8–12 chars): • ✅ Valid: 8–12 characters • ❌ Invalid: <8 or >12 characters It’s efficient, logical, and covers more ground with fewer tests. ⸻ 🧠 3. Decision Table Testing Best for: Complex logic with multiple input conditions. It ensures every rule combination is verified. Example: Login scenarios based on username & password validity — checking all combinations to confirm correct outcomes. ⸻ 🔄 4. State Transition Testing When to use: When the system’s behavior depends on its previous state. Example: • ATM allows withdrawal only after a valid PIN. • Account locks after 3 invalid attempts. This ensures the system responds correctly as states change. ⸻ 💡 5. Error Guessing Power move for experienced testers. Use intuition and experience to predict where the system might fail. Examples: • Submitting empty forms • Entering invalid characters • Uploading oversized files It’s about thinking like a user, developer, and hacker all at once. ⸻ ✅ In short: A great tester doesn’t just test the product — they understand it. By combining structured techniques with creative thinking, we deliver quality, confidence, and value. #SoftwareTesting #QAAutomation #TestingStrategy #TestDesignTechniques #BugHunting #QualityAssurance #APITesting #TesterMindset #TestMateAI #ThinkLikeATester

I’m a Test Manager with 6+ years of experience, and I’m sharing daily tips. Day 10. How to build the right test coverage (and where to start) Test coverage is often treated like a checkbox. Add tests. Increase numbers. Move on. That approach usually fails. Good coverage starts with understanding risk, not tools. Step 1: Identify what breaks the business Before writing a single test, answer: • What actions bring money? • What actions retain users? • What actions can corrupt data if they fail? Those flows are your coverage backbone. Step 2: Map real user journeys and not UI screens End-to-end behavior: login → key action → state change → persistence → recovery If a flow matters to users, it must be covered at least once end to end. Step 3: Choose the right test level E2E tests usually are expensive. Use it only for: • critical paths • integrations between systems • “must never break” scenarios Everything else belongs to unit, integration, or manual exploration. Step 4: Start small and stable One solid E2E test that survives releases is more valuable than ten fragile ones. Stability first. Expansion later. Step 5: Review coverage, not test count Coverage should be revisited every time: • business priorities change • flows are redesigned • new risks appear If a test no longer protects risk — remove it and don’t miss 😁 The goal of test coverage isn’t to prove the product works. It’s to know exactly where it will fail — before users do. #QualityAssurance #TestCoverage #E2ETesting #AutomationStrategy #QALeadership

Mutation Testing: Code coverage is just a number. Mutation testing makes it meaningful. Mutation testing is an advanced technique for assessing the true effectiveness of a test suite beyond basic code coverage. Traditional test coverage metrics only measure how much code is executed during testing but do not indicate whether tests can successfully detect defects. Mutation testing addresses this gap by introducing controlled modifications—known as "mutants"—to evaluate how well a test suite identifies potential faults. How It Works: 1️⃣ Generating Mutants – The system applies minor modifications to the code, such as altering logical operators, modifying values, or adjusting conditions. 2️⃣ Running Tests on Mutants – The test suite is executed to determine whether it can catch these changes. 3️⃣ Analyzing Results – If a test fails due to a mutation, it indicates strong fault detection. If the test passes despite the mutation, it reveals weaknesses in the test suite. 4️⃣ Calculating Mutation Score – This metric quantifies the proportion of detected mutants, providing deeper insights into test effectiveness. How We Use Mutation Testing at Early We use mutation testing to evaluate our technology for generating high-quality, working unit tests and to measure its effectiveness—ensuring that the tests it produces later prove themselves in real-world production code. By incorporating AI-driven testing methodologies, engineering teams can: ✔ Achieve higher accuracy in defect detection ✔ Optimize test coverage beyond traditional metrics ✔ Ensure more resilient software development Popular Mutation Testing Tools by Language: * JavaScript & TypeScript: Stryker – The most popular mutation testing framework for JS, TS, and Node.js. * Python: Mutmut – A simple, easy-to-use mutation testing tool. * C# / .NET: Stryker.NET – The .NET version of Stryker, supporting C#. * Java: PIT (Pitest) – One of the most widely used mutation testing tools. * C / C++: Mull – A fast and scalable mutation testing tool. Ruby: Mutant – Designed specifically for Ruby. Go: Go-mutesting – A mutation testing tool for Go. Bonus: Recently, Meta introduced mutation testing to the LLM world, unveiling a mutation-guided LLM-based system that generates faults in source code. A big step toward AI-driven software quality, but it probably requires a lot of effort to use it and get value. 🔗 Links to all these tools are in the first comment What’s your experience with mutation testing? How do you ensure your tests actually catch real bugs? Let’s discuss.

🧪 “How do you decide what to test?” This question gets asked a lot. And the answer isn’t sexy, but it’s strategic: You don’t test everything. You test what matters. Here is MY go-to model for delivering maximum test coverage with minimum waste: 1. ⚠️Risk First: If it breaks, how bad is it? → Ask: What’s the worst thing that could happen if this breaks? → Prioritize payment flows, auth, data integrity, anything with "compliance" in the email subject. 2. 👤User Behavior: How could a chaotic user destroy this? → Test like a chaotic user, not a compliant one. → Think: double-clicks, network drops, copy-pasted emoji payloads, 200 open tabs. 3. 🔁Regression: Could this break something old or shared? → Cover legacy logic and shared components. → One div in one modal can break 12 other places. Ask me how I know. 4.🧬Code Changes: Did the code touch something fragile? → New code? New tests. → Test where the code changed not just what the ticket says changed. 5. 🔗Integration > Unit (sometimes): Bugs hide in the seams. Not the functions. → Unit tests are cheap. → But bugs don’t care about your microservices’ feelings, they happen at the seams. 6. 📉Analytics: Is this even used by real humans? → Use analytics: What features are actually used? → Test coverage should reflect reality, not just the backlog. 💥 TL;DR: Don’t test for the sake of testing. Test to protect value, reduce risk, and simulate user chaos. QA isn’t about being thorough, it’s about being strategic. 💬 What’s one thing you always test, no matter what the spec says? (Mine: anything labeled “optional” in a signup form. It’s never optional.) #SoftwareTesting #QAEngineering #RiskBasedTesting #TestingStrategy #QualityAssurance #TestSmarter

Here’s a trick to diagnose pipeline problems: ask what percent of ICP accounts your reps have engaged in the last 6 months. If you don’t engage, you can’t create pipeline. Yet most people don’t have this answer handy. I’d bet more folks could quote how many dials and emails their reps do on a daily basis. We measure outbound activity, but rarely where that activity is applied. It’s like awarding the NBA MVP to the player that runs the most miles during the season. Lots of effort, but unclear if it’s being put to good use. Account coverage is a straightforward way to look at that problem. Pick a minimum engagement level (e.g. 3 touches), period (e.g. last quarter) and a segment (e.g. mid-market). Count how many accounts got that level of engagement during the period. Express that as a percentage of total accounts in that segment. Strategically it tells you if your team’s actually working the accounts you intend them to. You’d be surprised how often reps aren’t really executing the strategy and are off working the account their cousin’s best friend’s sister just got an AE role at. At Gradient Works, we often do a coverage analysis for teams considering our pipeline platform. We typically use a year’s worth of account and activity data and show account coverage as a series of heatmaps (aka an “Engagement Grid”) using two different dimensions. I like this view because it immediately shows coverage gaps. (There's a version of this in-product as well.) In the e-commerce software example below you can see that only 30% of accounts with ≤$10M in GMV in the grocery industry have been worked. If that’s an important segment, it should set off alarm bells. It's not required to have the fancy heatmap, you can just calculate a number for any group of accounts in your CRM to see if you’re covering them properly. You can also do the opposite to see if you’re spending time on accounts that aren’t very good. Here’s how you to use this to diagnose a pipeline problem: 📉 Not Enough Pipeline If you’re missing pipeline targets it *could* be because reps aren’t working hard enough. That's likely not the whole story. It could be because they’re working a large number of low-quality accounts and their opportunity creation rate is low. By working the wrong accounts, rep activity isn’t converting efficiently into opportunity. 📉 Low Conversion Rates If you're hitting pipeline targets but not hitting the revenue plan, you're not converting raw pipeline into wins efficiently. It could be the result of creating opportunities with the wrong kind of accounts. In this case, you might see good activity levels, a good opportunity creation rate but low coverage of high quality accounts. Rule this out before jumping to the conclusion you need new reps or a big new enablement push. If you’re seeing these problems, give me a shout. It’s a core part of Gradient Works.

💡Revolutionizing Wireless Networks: Automation in RF Coverage Analysis and Optimization Optimizing RF coverage and maximizing RAN network performance are crucial for delivering superior wireless services. The following paragraphs outline a step-by-step approach that combines geolocated data with automation to enhance RF coverage, reduce interference, and increase network capacity: - The first step involves analyzing the best server areas for a specific EARFCN. This analysis provides a clear visualization of each antenna's intended coverage area, ensuring optimal network planning and resource allocation. - Next, we examine the Reference Signal Received Power (RSRP) within each cell's best server area. This assessment helps identify signal strength variations and potential coverage gaps within the primary service area. - The third step expands the RSRP analysis to encompass the entire cell footprint. This broader perspective allows us to detect any unintended signal propagation beyond the planned coverage area. By comparing the actual footprint with the intended coverage, we can determine if a cell is overshooting its designated area. The optimization process takes into account various contextual factors: - Area morphology (dense urban, urban, rural, etc.) - Inter-site distance - Cell capacity (utilizing PRB utilization data) - Historical optimization activities - Constraints due to VIP areas or other special considerations By considering these factors, we can propose tailored antenna recommendations that improve coverage without negatively impacting other KPIs.

If I am helping a team with their testing approach, I want a sense of how they are doing. This is partly to pick where to focus, and partly to assess later whether there has been any improvement. I want to avoid rigid assessments, but I do want something that gives me a sense of "needs work" versus "doing great", and various flavors in between. The main things I want to know are how much does the team all the way through management understand the testing and product quality (Testing Transparency), how well does the testing cover the risks (Testing Quality), and how much is the team observing product after release to improve both testing and the product itself (Issue Escapes). I use a 1-4 numerical scale that is subjective, but which can also be checked with evidence. A 1 means "not at all, or in no meaningful way", 2 means "a small degree but poorly", 3 means "good, but with room to improve, and 4 means "could not imagine better, excellent". One can collect this assessment from interview with team members, and then check if there is actual evidence (documentation, reports, activity) which matches the self-assessment. Each of the three categories has multiple statements. Testing Transparency - The team knows what is going to be tested. - The team knows what was tested and what was discovered. - The team has an assessment of product quality before release. - The team has an assessment of product quality after release. - The team knows what to do to improve testing and product quality. Testing Quality - Testing coverage is described. - Testing covers all of product functional areas. - Testing covers quality categories (security, performance, accessibility, etc.). - Testing is efficient. Issue Escapes - All post-ship issues are traced back to a set of root causes. - Post-ship fixes target systemic causes and prevention rather than one-off fixes. A team in high performance mode is probably going to have 3s or better on most of those questions. A team that is struggling and lost is going to be most 1s and a few 2s. A team making improvements is going to see numbers increase over time. Avoid treating this like a math exercise. One team's 2 might be another team's 3, and an average across all the numbers probably cooks so much subjectivity into the assessment to make the final number useless. Use it instead to orient yourself on where to focus attention, and to remind yourself and the team of any success they have achieved already. #softwaretesting #softwaredevelopment You will find more articles and cartoons in my book Drawn to Testing, available in Kindle and paperback format. https://lnkd.in/gM6fc7Zi