Krishna R.P, PMP®

Healenium empowers automated testing with self-healing capabilities, reducing maintenance by dynamically adapting to UI changes. Encouraging its use leads to more stable, resilient test suites and faster delivery cycles.

27

6 Comments

💡 Totally resonate with Mansoor Shaikh perspective! Quality isn’t something you can sprinkle on at the end — it needs dedicated focus from the start. Expecting a single QA to juggle multiple projects and still ensure deep involvement is rarely sustainable. If we truly want shift-left testing, better quality, and fewer production issues, we need the right developer-to-QA ratio. Let’s not just “test” quietly — let’s speak up for what quality really needs. #QualityMatters #ShiftLeft #QA #SoftwareTesting #Automation

5

Building a GCC in India? You Need a Talent Engine, Not a Recruiter GCC hiring is not volume hiring. It’s not IT services hiring. And it’s definitely not “send 10 resumes and hope one closes.” Global Capability Centers need: ✔ Product-minded engineers ✔ Long-term team builders ✔ Ownership-driven leaders ✔ Scalable hiring systems Yet most agencies still operate transactionally That’s why: • Time-to-hire stretches to 60+ days • Counter-offers kill 30% of closures • Cultural misalignment leads to early attrition We don’t work like that. We can build GCC Talent Engines for you Here’s what that means: 🔹 Assign dedicated recruiter pods aligned to your tech stack 🔹 Build Pre-mapped talent pools before roles even open 🔹 Interview batching to cut hiring cycles by 30–40% 🔹 Structured offer & counter-offer strategy 🔹 Market intelligence on salary & availability We don’t measure success by resumes sent. We measure it by capability built. If you’re scaling a GCC in India and need a serious talent partner — not a resume vendor — let’s talk. Comment GCC or DM me. #GCC #GlobalCapabilityCenter #IndiaExpansion #TechHiring #CaptiveCenters #ProductEngineering #TalentStrategy #LeadershipHiring  

17

💬 Driving Results When the Team Grows Complacent — A QA Leader’s Perspective One of the toughest moments for any QA or project leader is when the team starts showing signs of complacency — missing process steps, ignoring quality guidelines, or losing that sense of ownership. It’s easy to react with frustration, but real leadership is about rebuilding purpose, clarity, and accountability. Here’s the strategy I’ve found effective 👇 1️⃣ Reconnect with the “Why” Reinforce why processes exist — not as checkboxes, but as enablers of quality, predictability, and trust. When the team sees the bigger purpose, compliance follows naturally. 2️⃣ Revisit Goals, Not Just Metrics Sometimes people stop following because the goals feel outdated or disconnected. Revisit KPIs with the team and realign them with project outcomes. 3️⃣ Enable, Don’t Enforce Provide the right tools, templates, and automation to reduce process friction. In QA, even small automation aids (like AI-driven test reports or RCA templates) can reignite motivation. 4️⃣ Create Peer Accountability Empower senior testers to own specific process areas — review checklists, RCA tracking, or test readiness gates. Shared ownership sustains quality culture longer than top-down control. 5️⃣ Recognize Progress, Not Just Perfection Acknowledge when the team improves even partially. Small wins build momentum and trust — two essential ingredients to reverse complacency. Quality leadership isn’t about control; it’s about re-engaging purpose and pride in what we deliver. #Leadership #QualityEngineering #PeopleManagement #TestAutomation #ProjectManagement #ContinuousImprovement

2

A pro tip for QA folks out there getting scared of genAI revolution. Just do not hang your boots and get in dejected mood. Hold on and read and buckle your belts... genAI answers everything but does it do all the time ? answer is, it doesn't !! It can not , as simple as that ! Don't believe it, use your banking AI buddy and ask some non-normal queries, and if your from QA prodigy, watch them fumble or smartly route your to a human representative. Also, observe , how many times they do it when not in comfort zone. Thats the point of failure QA bunch should become smart at to record and calibrate !! it's the same thing, just wrapped in different flavor ! Point of failure checkpoint identification and metrices around it would be game for QA and QC in Agentic genAI world ... .#genai #llm #qa

4

Is Your Software Really Done? Let’s Talk About Acceptance Testing!" We all love ticking off that final checkbox that says: ✅ DONE. But the real question is — done for whom? Acceptance Testing isn’t just another phase in the SDLC — it’s where expectations meet execution. It’s where business value is validated, and it’s what determines if your software is actually ready to go live. Here’s what makes software truly "done": -> All acceptance criteria are met. -> Real-world scenarios are tested from the end-user’s perspective. -> Feedback loops with stakeholders are closed. -> Business value is tangible and delivered. Why it matters now more than ever: With Agile and DevOps accelerating release cycles, teams often confuse "coded and tested" with "done." But until the business signs off through acceptance testing, you’re still incomplete. Acceptance Testing = Alignment between what was envisioned and what was built. How do you define "done" in your projects? #SoftwareTesting #AcceptanceTesting #Agile #QualityAssurance #SoftwareDevelopment #TestAutomation #Leadership #DigitalTransformation #ThoughtFrameworks

5

Testing Via Agents to Testing Agents ------ While we are busy designing agents that can test, ever wondered who/how shall we test the Agents? We are entering a phase where software doesn’t just execute… it decides. And that fundamentally changes how we test. For years, testing was built on one assumption: ➡️ Systems are deterministic. AI Agents powered by LLMs break that assumption. The same utterance can produce different responses. The “correct” answer can vary by context. And failures are no longer bugs — they are behavioral deviations. So the real question is not: “Did it work?” It is: “Did the LLM behave intelligently and responsibly?” Testing AI Agents now requires a shift: • From test cases → to evaluation frameworks • From pass/fail → to confidence and quality scoring • From fixed inputs → to diverse utterance design What actually matters? → Designing meaningful utterance sets to probe reasoning and context → Evaluating hallucination, relevance, and completeness → Building regression datasets that evolve with the LLM → Implementing strong guardrails to control unsafe or incorrect outputs → Using LLM-as-evaluator alongside human judgment Yes, tools like Botium, Playwright, and emerging LLM evaluators are helping… But tools are not the real shift. Mindset is. The future of testing is not about validating outputs. It is about shaping and governing AI behavior. Testers will no longer just verify systems. They will define boundaries, guardrails, and trust. The question is — are we ready to test intelligence instead of logic? #AI #LLM #Testing #AIQuality #Automation #Guardrails #Innovation

3

One thing I’ve learned working in product management... It becomes less about the artifacts you create and and more about how you think and facilitate problem-solving and creativity in the room. Anyone can write a PRD... Not everyone can... • De-escalate tension • Reframe a stuck conversation • Align engineering, design, and business without politics That kind of influence doesn’t show up on Jira boards. But it shows up in execution speed, trust, and product quality...that’s the kind of work I enjoy most.

1

I have asked my top tester friends this question: "If a tool always gives you correct output in every way, are you justified in trusting it?" All of them, so far, have answered it in the same basic way. Interestingly, when I gave this question to ChatGPT, it first answered a related question instead of the one I asked. This is what Daniel Kahneman calls heuristic substitution. It's a common thing that humans do when asked a difficult question. ChatGPT turned the question into "if a tool ALWAYS HAS IN THE PAST given correct output..." which is not the same thing at all. When I held the AI's feet to the fire to answer the original question, it gave me an answer that my friends also gave: "If that is actually true (universally, without exception, across all possible uses, past and future), then yes—you are justified in trusting it, because there is nothing left that could undermine that trust. “Always” + “in every way” removes uncertainty. But the catch is this: You can never be justified in believing that premise is true based on experience or evidence... So the precise answer is: - Yes, if it really always gives correct output in every way. - But you are not justified in concluding that it does. That distinction is doing all the work."

119

66 Comments

🚨 Monday Motivation from a QA Desk ☕💻 Ever noticed how client escalations rarely wait for a “calm” day? They show up unannounced — usually on a Monday morning — right when you’re planning your week. But here’s what I’ve learned: 👉 Escalations are not just pressure moments; they are trust moments. 👉 They test not only the product but also our ownership, communication, and composure. 👉 And most importantly, they reveal the strength of a team that focuses on solutions instead of blame. 🔍 In QA, an escalation is not about “Who missed it?” It’s about “How fast can we understand, respond, and prevent it from happening again?” Instead of fearing escalations, start seeing them as opportunities to: ✅ Strengthen processes ✅ Improve test coverage ✅ Communicate transparently ✅ Build client confidence So if your Monday started with a surprise escalation — take a deep breath. You’re not firefighting. You’re building reliability. 🔥 Stay calm. Stay analytical. Stay solution-focused. That’s real Monday motivation. #MondayMotivation #QualityAssurance #ClientSuccess #QALife #Testing #Leadership #SoftwareTesting

5

The landscape of QA is undergoing a massive shift, and Gen AI is leading the charge We are moving beyond the era of simply automating manual clicks. Gen AI is no longer just a futuristic buzzword; it is actively becoming a core collaborator in the testing lifecycle. Here is how Gen AI is revolutionizing the QA workflow: Accelerated Test Case Generation: Instead of manually writing out hundreds of scenarios, QA teams can now use Gen AI to instantly translate user stories and acceptance criteria into comprehensive test cases, including edge cases we might naturally miss. Smart Synthetic Data Creation: Need to test a complex scenario but blocked by data privacy laws? Gen AI generates robust, production-like synthetic data, allowing for rigorous testing without exposing sensitive PII. Automated Script Writing & Maintenance: AI copilots are helping testers write automation scripts (for Selenium, Cypress, Playwright, etc.) in a fraction of the time. Better yet, they can help auto-heal broken tests when UI elements change, drastically reducing maintenance overhead. Predictive Bug Detection: By analyzing historical code commits and defect logs, Gen AI can predict which modules are most likely to fail, allowing teams to shift their testing focus to high-risk areas. The takeaway? Generative AI isn't here to replace QA engineers; it’s here to elevate them. The role of the tester is evolving from manual execution to strategic oversight, critical thinking, and AI orchestration. #GenerativeAI #SoftwareTesting #QualityAssurance #QA #TestAutomation #TechTrends #SoftwareDevelopment #AIinTech

2

there's no environment like production. In fact, nothing matters except production. twitter has only one env. A lot of things will never be identical: data combinations, integration environments, disk space and other capacity constraints, third party API response times and uptime, WAF flavor, DNS server, cloudflare config ...

4

1 Comment

If a Project Manager had planned Lord Ram’s return to Ayodhya After 14 years in exile, the stakes were high. Public expectations? Sky-high. Stakeholders? The entire kingdom. Timeline? Non-negotiable - before sunset on Diwali. Here’s how a PM might have handled it: • Scope Definition: Deliver a grand welcome for Lord Ram, Goddess Sita, and Lord Lakshman with lights, joy, and zero delays. • Risk Register: Monsoon risk, lamp oil shortage, overenthusiastic citizens crowding the roads, and one critical dependency: Lord Hanuman’s ETA confirmation. • Stakeholder Management: Lord Bharat as the key sponsor, citizens as end-users, and sages as external auditors for moral compliance. • Resource Allocation: Division of work across local teams - one for diyas, one for décor, one for communications (the town criers had their hands full). • Change Requests: “Can we add fireworks?”, “Can we include a royal procession?”, “What if Lord Ram arrives early?” All logged, prioritized, and tactfully deferred. • Go-Live Plan: Synchronize diyas across all households, coordinate music, ensure zero blackouts, and above all, celebrate alignment. And when everything went as planned, Ayodhya didn’t just light up - it became a case study in flawless execution. Because great project management isn’t just about plans; it’s about bringing people together for something larger than themselves. Wishing everyone a bright and meaningful Diwali filled with light, gratitude, and purpose. #ProjectManagement #Leadership #Agile #Diwali #Ramayan

33

I have seen today one post that says Delivery PMs jobs are going to be obsolete or shrunk by 2028 with AI agents. For bit of moment, I was shocked. then, i understood delivery roles will never ever be shrunk, yet more opportunities will be created in upcoming years. You replace PM with AI Agent? Think about a scenario where agent talks to customer and provide an update, how it builds a relationship with customer? Can It see and read the customer face expression? Can it talk about the culture? Can it initiate the conversation with friendly gestures? Can it have empathetic relationship with customer in crucial times? many more questions Logical/reasoning based RAG agents will never replace PMs but they can supplement PMs efficiency, skill that lead to operational excellence to thrive in uncertain/ambiguous times while navigating the project through various stages. Agents will be buddies/peers to PMs and help them succeed in their role. Any thoughts or comments from fellow PMs?

8

3 Comments

Paralysis by Analysis is the silent killer of delivery momentum. I’ve seen great teams stall — not because they lack capability, but because a decision point got stuck in over-review. 👉 Every extra round of approval can mean: Slipped deadlines Lost customer trust Burned-out teams waiting on direction Good governance doesn’t mean endless loops. It means clear owners, defined deadlines, and the courage to move forward with 70% clarity. In delivery, done often beats perfect. Curious to hear how others balance risk with speed in decision-making? #ProjectManagement #ServiceDelivery #Leadership #AgileDelivery #PMO #ExecutionExcellence #DigitalDelivery #PMP #TeamVelocity

11

1 Comment

One underrated problem in eLearning production isn’t 𝘀𝗽𝗲𝗲𝗱 — it’s 𝗶𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆. Give the same approved storyboard to three different developers and you’ll often get: • Slightly different lesson structures • Different block choices • Inconsistent handling of knowledge checks • Extra QA cycles to normalize everything Not because the developers are bad — but because 𝗺𝗮𝗻𝘂𝗮𝗹 𝗮𝘀𝘀𝗲𝗺𝗯𝗹𝘆 𝗶𝗻𝘃𝗶𝘁𝗲𝘀 𝘃𝗮𝗿𝗶𝗮𝗻𝗰𝗲. Storyboard2Rise removes that variable. Approved storyboards are translated into Articulate Rise the same way every time: • Same structure • Same blocks • Same interpretation (none) Developers don’t start from a blank course. They start from the 𝘀𝗮𝗺𝗲 𝗰𝗹𝗲𝗮𝗻 𝗳𝗶𝗿𝘀𝘁 𝗽𝗮𝘀𝘀 — and move straight to refinement. This turns course assembly from a craft into a repeatable process, which matters a lot when: • Multiple developers touch the same program • You’re scaling production • QA time is becoming the bottleneck Storyboard2Rise isn’t trying to be creative. It’s trying to be 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗹𝗲. And in production workflows, that’s usually the bigger win. If you’re running Rise at any kind of scale, I’m curious: 👉 where do inconsistencies show up most in your process? Try it for yourself at: https://lnkd.in/gezD58hm Or contact me for a quick demo.

9

1 Comment

2 mins is not sufficient for any dish or even Instant Noodles 🍜 , only water 💦 boiling happens 🧫 There is no Instant Solution for every problem. I agree on doing a High level Check and Workaround, which works to save time n energy. But the point is Comprised Time does not give or guarantee Quality work. Read the below article explaining how a 'Real QA' is being compared and expected to be an 'Instant QA'. Yes, we do have Exploratory Testing to check on high level but the purpose of it is different. Sanity and Regression Testing have different purposes.

3

Was in a call with some top mgmt. folks and a question arose – Why are there so many defects? I got perplexed at the question thinking what kind of a question was it? Because if defects are not expected to be found - we would not require testing at all? We can promote the product directly into production. The other wrong assumption made in the meeting was that whatever defects the client raised are all mostly misunderstanding of the product behavior. Indirectly and internally the message hinted is that the product is supposed to be a low-defect product. I asked Gemini to provide me that the median stats for a mature product implementation and it reported the below values broadly split into three categories. JIRA Ticket Type & Typical Percentage Split Defects - 50% Change Requests - 35% Misunderstanding - 15%   The highest percentage category is the category of “Defects” that cannot be disputed and needs to be fixed. New Requirements or Change Requests received during SIT or UAT arise from the experience of business users having used a legacy system or having used other applications while working somewhere else before. This forms the second bulk of the JIRA tickets. It is only the third category of “Client’s misunderstanding of product behavior” or in other words "Invalid Defects" which has the lowest percentage share of 15%. These tickets are clarified by the vendor to the client. Other thing that is not ingestible by the top mgmt. folks is the discovery of big defects towards the end of UAT testing by the client as if defects should not be found towards the fag end of testing because we have supposed to have a fairy tale ending. Nothing can be further away from the truth. If the client found big issues towards the end of testing we cannot blame the client and we cannot blame anybody for the ill-timing. We cannot ask the client – why so late? Yes, discovery of big defects towards the fag end of testing is undesirable and makes everybody uncomfortable as we are short on time to get them fixed but crying or lamenting about them is not going to solve the problem. Putting up a brave face before the client in this midst is very much required but while talking about it in internal team meetings needs a bit of realism to be induced. Instead we get to hear the usual rant of how the defects were missed to be found in Internal SIT etc. etc. Let us all respect the testing phases of Onsite SIT and UAT, their purposes, their results and outcomes - it makes the product more mature. The very reason that different people are involved in each testing phase is because they are meant to capture the defects that was missed in the previous testing phase and they will definitely find out what was missed. Otherwise, we will be in for more trouble if the issue comes up in Production.

5

Manual Testers: AI isn’t coming for your job. It’s coming for your boredom. 🤖 There is a common fear that AI will replace manual testing. I see it differently. AI is replacing the boring parts of testing, leaving the creative parts to us. If you are a manual tester, here is how you should be looking at AI tools right now: 1. The "Blank Page" Problem: stuck on writing the first test case? AI generates the baseline in seconds. 2. The Data Headache: Need 50 rows of specific mock data (JSON, CSV, SQL)? AI creates it instantly. 3. The Technical Gap: Understand the logic but struggle with the SQL syntax? AI translates your natural language into queries. We need to stop viewing AI as a competitor and start viewing it as the ultimate Junior Assistant. It handles the grunt work; you handle the strategy, the usability, and the user experience. Are you using AI in your testing workflow yet? If so, which tool is your favorite? #SoftwareTesting #ManualTesting #QA #ArtificialIntelligence #QualityAssurance #TechTrends

7