Bug Tracking and Resolution

User Acceptance Testing (UAT) is where the real users put the system to the test — and that’s when bugs often pop up like uninvited guests. 🎯 So how should a Business Analyst react? Here’s a practical, real-world approach👇 🔹 𝟏. 𝐒𝐭𝐚𝐲 𝐂𝐚𝐥𝐦, 𝐍𝐨𝐭 𝐃𝐞𝐟𝐞𝐧𝐬𝐢𝐯𝐞 Example: During UAT for a loan origination platform, a tester flagged that loan application forms were crashing on submit. 💡Instead of blaming dev or users, BA should listen carefully, replicate the issue, and documented the exact steps. 🔹 𝟐. 𝐋𝐨𝐠 𝐈𝐭 𝐂𝐥𝐞𝐚𝐫𝐥𝐲 𝐢𝐧 𝐭𝐡𝐞 𝐃𝐞𝐟𝐞𝐜𝐭 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 𝐓𝐨𝐨𝐥 Use tools like JIRA or Azure DevOps. 💡Include: ✅ Clear description ✅ Steps to reproduce ✅ Screenshots/video ✅ Environment ✅ Severity and priority 🎯Tip: Categorize whether it’s a functional defect, UI issue, or data mapping error — devs love clarity! 🔹 𝟑. 𝐓𝐫𝐚𝐜𝐞 𝐈𝐭 𝐁𝐚𝐜𝐤 𝐭𝐨 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬 Was it a missed requirement, misunderstood user story, or a change that wasn’t captured? 💡Let's say, a “Export Report” button isn’t working. It turned out that the requirement wasn’t documented properly. BA should update the user story and collaborated with the Product Owner to include it in the next sprint. 🔹 𝟒. 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 & 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐞 Not all bugs are blockers. 📍BA can work with the QA Lead and Product Owner to determine which bugs were critical for go-live and which could go into post-launch patching. Clear communication = smoother releases. 🔹 𝟓. 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐞 𝐅𝐢𝐱𝐞𝐬 𝐚𝐧𝐝 𝐂𝐥𝐨𝐬𝐞 𝐭𝐡𝐞 𝐋𝐨𝐨𝐩 Once the dev team resolves the bug, the BA ensures it meets the business need — not just technically fixed. 💡BA must always retest or sit with the UAT tester to verify resolution and update stakeholders. ✅ 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 𝐟𝐨𝐫 𝐁𝐀𝐬: Your job during UAT isn’t just to observe — it’s to bridge users and tech when bugs appear, ensuring issues are documented, fixed, and business value is preserved. Let’s normalize the fact that bugs are not failures — they are feedback. Handle them like a pro. 🧠💬 BA Helpline

What if 90% of the bugs in your backlog never made it to Product or Engineering? For the last year, I’ve been not-so-secretly-wishing for AI that could triage and respond to every issue or bug before someone on our team ever sees it. At Luxury Presence, that dream is now very close to reality. 🤩 For context, we are a $75M+ ARR company supporting: • Millions of unique MLS real estate listings • Tens of thousands of websites • Hundreds of mobile apps Because of all this volume, It’s not uncommon for an issue to pop up that affects just one listing on one website. But for that customer, it’s mission-critical to resolve quickly. Our teams have been building two AI agents to make triage and resolution faster than ever. 🤖 🏠 Proppy – our MLS data expert. It lives in Slack, available to everyone in the company. It can scan millions of listings, run over a dozen data integrity checks, and validate authorizations to see why a listing isn’t showing as expected. What often took days can now be done in less than a minute. Next step: integrate with Linear to auto-create well-formatted tickets when engineers need to step in. Huge shoutout to Raj Vegulla MS, MBA Bradford Cook Dayton Tipton Saneel Bidaye and their teams for making this a reality! 🤖🔨 Auggie – short for Augmented Engineering. Zach Wills Andrew C. and team are just getting started at transforming how we do engineering. I'm sure we'll share more publicly on what we are doings soon, but here's the tl;dr It's powered by Claude Code, Devin.ai, and MCP servers. It takes the first pass at Linear bugs, asks clarifying questions, understands our codebase, past tickets, and Slack knowledge, and creates shovel-ready tickets — with suggested fixes. Next step: have it write the PR itself. Once we connect both with Fin from Intercom, customers will get near-instant resolution to even niche and technical issues. 🤯 Feels like the future is already here. Please share if you are doing any similar and have any tips to share. We're all just trying to figure this stuff out together!

This week, I want to talk about something that might not be the most exciting or sexy topic—it might even seem plain boring to some of you. Very impactful, yet even in many large and complex organizations with tons of data challenges this foundational data process simply doesn’t exist: the Data Issue Management Process. Why is this so critical? Because #data issues, such as data quality problems, pipeline breakdowns, or process inefficiencies, can have real business consequences. They cause manual rework, compliance risks, and failed analytical initiatives. Without a structured way to identify, analyze, and resolve these issues, organizations waste time duplicating efforts, firefighting, and dealing with costly disruptions. The image I’ve attached outlines my take on a standard end-to-end data issue management process, broken down below: 📝 Logging the Issue – Make it simple and accessible for anyone in the organization to log an issue. If the process is too complicated, people will bypass it, leaving problems unresolved. ⚖️ Assessing the Impact – Understand the severity and business implications of the issue. This helps prioritize what truly matters and builds a case for fixing the problem. 👤 Assigning Ownership – Ensure clear accountability. Ownership doesn’t mean fixing the issue alone—it means driving it toward resolution with the right support and resources. 🕵️♂️ Analyzing the Root Cause – Trace the problem back to its origin. Most issues aren’t caused by systems, but by process gaps, manual errors, or missing controls. 🛠️ Resolving the Issue – Fix the data AND the root cause. This could mean improving data quality controls, updating business processes, or implementing technical fixes. 👀 Tracking and Monitoring – Keep an eye on open issues to ensure they don’t get stuck in limbo. Transparency is key to driving resolution. 🏁 Closing the Issue and Documenting the Resolution – Ensure the fix is verified, documented, and lessons are captured to prevent recurrence. Data issue management might not be flashy, but it can be very impactful. Giving business teams a place to flag issues and actually be heard, transforms endless complaints (because yes, they do love to complain about “the data”) into real solutions. And when organizations step back to identify and fix thematic patterns instead of just one-off issues, the impact can go from incremental to game-changing. For the full article ➡️ https://lnkd.in/eWBaWjbX #DataGovernance #DataManagement #DataQuality #BusinessEfficiency

Day 21 🐞 Bug Reporting Tips That Developers Actually Appreciate: Effective bug reporting is a crucial skill in Quality Assurance. Clear, concise, and actionable bug reports not only expedite the debugging process but also foster better collaboration between QA and development teams. Here are some best practices to ensure your bug reports are developer-friendly: 1. Craft a Clear and Descriptive Title Avoid: "App crashes" Prefer: "App crashes when clicking 'Submit' on the registration form without entering data" A specific title helps developers quickly understand the issue's context. 2. Provide Detailed Steps to Reproduce List each step clearly and sequentially. Include any specific data used during testing. This enables developers to replicate the issue reliably. 3. Specify the Environment Device: e.g., iPhone 13 OS: e.g., iOS 15.4 App Version: e.g., 2.3.1 Environmental details help in identifying environment-specific issues. 4. Describe Expected vs. Actual Results Expected: "User is redirected to the dashboard after login." Actual: "User remains on the login page with no error message." This contrast clarifies the deviation from intended behavior. 5. Attach Visual Evidence Include screenshots or screen recordings. Highlight relevant areas or error messages. Visuals can expedite understanding and resolution. 6. Assign Severity and Priority Levels Severity: Impact level (e.g., Critical, Major, Minor) Priority: Urgency for fixing (e.g., High, Medium, Low) This helps in triaging and addressing issues appropriately. 7. Use a Consistent Template Maintain uniformity across bug reports. Include all necessary fields: Title, Environment, Steps, Expected vs. Actual Results, Visuals, Severity, Priority. Consistency aids in efficient processing and resolution. 8. Maintain Professional and Collaborative Communication Be objective and avoid assigning blame. Focus on facts and reproducible information. A collaborative tone fosters a positive working relationship. Implementing these practices can significantly enhance the efficiency of the debugging process and improve team collaboration. Remember, a well-documented bug report is a step closer to a robust and reliable product.

One pattern I’ve seen in many startups with limited engineering resources: 🚀 All focus goes into new features. 😬 Errors are only addressed when a user or key stakeholder reports them. 🔥 Engineers scramble in panic mode, context switching away from roadmap work. There’s a better way. Once you’re in operating mode with paying customers, dedicate at least one thread of engineering to fixing recurring errors as they show up in your logs. Tools like Datadog or Sentry make it easy to spot when the same error is happening repeatedly. That’s the signal to act. And “one thread” doesn’t have to mean an engineer full-time on bug fixes. It can simply mean carving out consistent capacity, for example, an hour a day per engineer focused on error resolution. The right amount depends on your resources, but the key is making it a habit rather than an afterthought. The compounding effect of “small” errors is real: they quietly erode conversion rates, user trust, and momentum. Fixing them systematically preserves the roadmap and prevents fire drills. The good news: this has never been faster. With tools like Cursor, you can literally paste in an error trace from Datadog, and Cursor will analyze it and propose a fix. What used to take hours of digging can now be turned around in minutes. 👉 Pre-launch, you can get away with ignoring a lot of these. But post-launch, with paying customers, a disciplined error-fixing thread is just as important as shipping the next feature. #startups #productmanagement #engineering #errortracking #datadog #cursor

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 - I've Built 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗕𝘂𝗴 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁: 𝗔𝗻 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗧𝗿𝗶𝗮𝗴𝗲 𝗔𝗴𝗲𝗻𝘁 𝗨𝘀𝗶𝗻𝗴 𝗔𝗗𝗞 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗥𝗔𝗚, 𝗮𝗻𝗱 𝗠𝗖𝗣 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗕𝘂𝗴 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 acts as a central hub connecting internal PostgreSQL databases, external GitHub repositories, and live web knowledge to triage and resolve software issues using Google's Agent Development Kit (ADK). 🔗 GitHub Repo: https://lnkd.in/dyKpU_Bu 𝗕𝘂𝘁 𝘄𝗵𝘆 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗕𝘂𝗴 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁: I built this to solve fragmented issue tracking bridging internal tickets, code repos, and public knowledge so developers can identify duplicates and find solutions without context switching. 𝗖𝗼𝗿𝗲 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀  • 𝗥𝗔𝗚-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗧𝗿𝗶𝗮𝗴𝗲: Detects duplicates using Cloud SQL & Vertex AI vector embeddings  • 𝗖𝗿𝗼𝘀𝘀-𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗦𝘆𝗻𝗰: Fetches GitHub issues directly via the MCP Server  • 𝗚𝗿𝗼𝘂𝗻𝗱𝗲𝗱 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲: Real-time debugging via Google Search & StackOverflow  • 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Full SQL CRUD capabilities for internal tickets  • 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗮𝗹 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Combines LangChain, MCP, and custom APIs 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸  • Google 𝗔𝗗𝗞 (𝗣𝘆𝘁𝗵𝗼𝗻) → Agent orchestration & tool management  • 𝗚𝗲𝗺𝗶𝗻𝗶 𝟭.𝟱 𝗣𝗿𝗼 → Reasoning & tool selection  • 𝗖𝗹𝗼𝘂𝗱 𝗦𝗤𝗟 (𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟) → Storage with pgvector integration  • 𝗠𝗖𝗣 𝗧𝗼𝗼𝗹𝗯𝗼𝘅 → Standardized Model Context Protocol  • 𝗩𝗲𝗿𝘁𝗲𝘅 𝗔𝗜 → Embedding generation 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 & 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 The agent uses a 4-layer approach:   • Web UI,   • Gemini Brain,   • MCP Tool Layer, and   • Cloud SQL Data Layer 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄: User queries issue → Agent performs RAG Search for duplicates → Checks GitHub status → Searches Web/StackOverflow for fixes → Synthesizes a resolution plan. 𝗞𝗲𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀: • In-database vector embeddings using google_ml_integration • Modular tool architecture using the Model Context Protocol (MCP) • Serverless deployment on Cloud Run for auto-scaling • Automated SQL triggers for accurate timestamp tracking 𝗜𝗺𝗽𝗮𝗰𝘁 Serving as an MVP, this project demonstrates how GenAI moves beyond simple chat to become a functional teammate, significantly reducing TTR (Time to Resolve) by aggregating siloed data into actionable insights for Engineering teams. #AgenticAI #GenAI #RAG #GoogleCloud #Google #ArtificialIntelligence #GeminiAI #SoftwareEngineering #AIAgents #LLM #DevOps #MachineLearning #Python #Developer #CloudNative #VectorDatabase #VertexAI #Innovation #BuildingWithAI #TechTrends

Average ticket resolution time dropped 40% in one quarter. Leadership celebrated. Customer satisfaction dropped 15% in the same period. Nobody connected the two until I asked. The company had introduced a resolution time target for support tickets. Reasonable intent: customers shouldn't wait days. The metric tracked time from creation to "resolved" status. Engineers responded rationally. Resolved faster. Closed earlier. Dashboard loved it. The problem: "resolved" and "solved" aren't the same thing. A customer reports a recurring sync failure. Engineer applies a config change that clears the symptom, marks it resolved. Forty-eight hours later, same customer, same problem, new ticket number. The metric counts two resolved tickets with excellent response times. The actual cause, a connection pooling issue needing architectural work, never gets fixed because fixing it takes a week and craters that engineer's numbers. Same dynamic across the queue. Engineers treating symptoms and closing tickets instead of diagnosing root causes. Not because they didn't know the difference. Because the system punished depth and rewarded speed. One engineer crystallized it: "I know what's wrong with the sync service. Fixing it takes five days. In five days of closing symptom tickets, I resolve fifteen issues and my numbers look great. Fix the root cause, I close one ticket in five days and get flagged." Every engineer on the team had done the same math. The customer felt the difference. Their problem kept returning. Each time, someone applied a quick fix and closed the ticket. Each time, their confidence in the product eroded. The dashboard showed fast resolution. The customer experienced a company that couldn't actually fix their problem. Goodhart's Law in real time. When a measure becomes a target, it stops being a good measure. Resolution time was supposed to measure how well problems got solved. Once it became a target, it measured how quickly tickets got closed. Different activities entirely. We split the metric. Resolution time for quick fixes. A separate category for root cause investigations with different expectations. Engineers who spent a week eliminating twenty recurring tickets got credit for the elimination, not penalized for the duration. Within two quarters, ticket volume dropped 30%. Not faster resolution. Actual solving. Problems that recurred monthly stopped recurring. Customers who'd been filing the same ticket every few weeks went quiet. The good kind of quiet. Old metric said the team got worse. Fewer tickets resolved. Longer times. New metric told the truth: problems were actually going away. The team was performing better than ever. Resolution is a status change. Solving is a state change. Your metrics can't tell the difference. Your customers can. ♻️ Repost if you've watched a metric improve while the underlying problem got worse ➕ Follow me (Phillip R. Kennedy) for engineering leadership that measures outcomes, not activity

Last night, working with the #hermes agent served as a valuable reminder that the first explanation may not always be the correct one. Initially, a Hermes Agent issue on Windows seemed like it could be attributed to local environment quirks. However, the behavior felt unusual, prompting me to continue tracing the issue rather than dismissing it as “just Windows being Windows.” This decision proved to be beneficial. A deeper investigation revealed an actual bug in how runtime state was being detected and reported. An interesting aspect of this experience was utilizing #Hermes itself to facilitate the investigation—analyzing the behavior, validating assumptions against the code, and accelerating the process from symptom identification to root cause and resolution. Once the issue was identified, the focus shifted from merely “working around it” to implementing a proper fix: tightening the detection logic, verifying the behavior, and submitting the issue and PR upstream. I appreciate bugs like this because they highlight that effective debugging is about thoroughly understanding the problem rather than rushing to the first solution. #ProductManagement #AIProducts #DeveloperTools #AIAgents #OpenSource #HermesAgent

🧩 Detailed Bug Life Cycle Phases 1. New / Open -Action: The tester finds a bug during testing and logs it in a defect tracking tool. -Details included: -Bug ID -Summary and Description -Steps to reproduce -Expected vs Actual result -Severity and Priority -Screenshots or videos (evidence) >Responsibility: Tester >Example: “Login button doesn’t respond when clicked.” → Status: New 2. Assigned -Action: Test Lead or QA Manager reviews the defect and assigns it to a specific developer. >Responsibility: Test Lead / Project Manager >Example: Assigned to the Developers for fixing. → Status: Assigned 3. In Progress -Action: The developer begins working on the issue. >Responsibility: Developer >Example: Developer starts debugging the login module. → Status: In Progress 4. Fixed / Resolved -Action: Once the developer identifies the root cause and fixes it, the bug is marked as Fixed. >Responsibility: Developer >Example: Developer fixed the missing click event in the login button. → Status: Fixed 5. Retest -Action: The tester re-tests the functionality in the new build. >Responsibility: Tester >Example: Tester rechecks the login button. If it works fine → next phase. If not → Reopened. → Status: Retest 6. Closed -Action: If the bug no longer exists, the tester marks it as Closed. >Responsibility: Tester >Example: Login button works fine. → Status: Closed 7. Reopened -Action: If the bug still exists after being marked as Fixed, the tester reopens it. >Responsibility: Tester >Example: The login button is still not clickable after the fix. → Status: Reopened 8. Deferred (Postponed) -Action: The bug is valid but not fixed in the current release (maybe low priority or dependent on another module). >Responsibility: Project Manager / Product Owner >Example: UI alignment issue on a rarely used page. → Status: Deferred 9. Rejected -Action: The developer rejects the bug if it’s not valid (e.g., due to a misunderstanding of requirements). >Responsibility: Developer >Example: Tester raised a bug, but the function works as per requirements. → Status: Rejected 10. Duplicate -Action: The bug already exists in the system, so a new report isn’t needed. >Responsibility: Developer / QA Lead >Example: Same login issue reported twice by two testers. → Status: Duplicate #TechTalks #KnowledgeSharing #TestingTips #SoftwareIndustry #ITCommunity #TestingJourney #TesterLife

Steal this bug triage workflow. We won’t tell. The thing is.. most teams say they have a bug triage process. But in practice? It’s more like: 1/ Bug reported. 2/ Slack ping. 3/ A 20-message thread. 4/ Three devs @-tagged. 5/ Someone promises to “look into it.” 6/ Silence. Next thing you know? That same bug resurfaces two weeks later... now blocking a release. Sound familiar? 😬 Here’s the truth: Bug triage is where sprints live or die. But most teams treat it like an afterthought. We learned this the hard way.. and here’s the workflow we use now that actually keeps things tight 👇 1️⃣ Triage daily, no exceptions. Not once a week. Not “when we get time.” Every. Single. Day. Even 15 minutes of daily triage = no surprise blockers later. 2️⃣ Standardize bug info. Every bug must have: -Repro steps -Actual vs expected behavior -Screenshots / recordings -System + environment info If it’s missing that? It does not get triaged. 3️⃣ Prioritize fast + transparently. We tag every bug as: P0 = Drop everything. P1 = Next up. P2 = Icebox (for now). Everyone on the team sees and knows why each bug sits where it sits. No mystery. 4️⃣ Assign ownership on the spot. No “we’ll figure it out later.” Every bug gets an owner immediately... even if it’s just to investigate first. 5️⃣ Track status in one visible place. No hunting through Slack or side threads. One board, one source of truth, no excuses. The result? Fewer bugs fall through the cracks. Devs trust the bug list. QA doesn’t waste time re-raising the same stuff. Releases stop getting hijacked by last-minute chaos. Steal this. Seriously. We won’t tell. 😉 What’s one thing you would add (or cut) from this workflow? Curious how other teams keep bug triage sharp 👇 #bugs #bughunting #betterbugs #qa