Levels of Automation Excellence

 How effective is your automation test suite?

How impactful is it for your product and your team?
Do you know how to grow your test suite without sacrificing quality and performance?

These questions are surprisingly difficult to answer — especially when your entire suite feels like it’s constantly on fire, your tests are untrustworthy, and production bugs are popping up like they’re going out of style. (Just me?)

To bring some clarity — and because testers love pyramids — I created the Automation Maturity Pyramid as a way to measure automation impact.

First, let’s remember why we write automation tests in the first place. At the end-of-the-day, automation tests should support two simple missions:

• Increase product quality & confidence
• Accelerate development & deployment

So when we think about the pyramid and its phases, everything we do should ultimately align with those missions.

The pyramid has four levels of maturity:

1. Confidence — Trusting your test results.
2. Short-Term Impact — Creating value in daily development.
3. Speed of Development — Scaling automation without slowing down.
4. Long-Term Impact — Sustaining trust, visibility, and continuous improvement.

Each phase builds on the one below it. Later stages only unlock their benefits once the initial foundation is solid. The pyramid is both tool and type agnostic, meaning you can apply it to any automation suite, framework, or testing type that fits your needs.

Remember, this journey takes time. Think of the pyramid as a compass, not a checklist to rush through. If you’re starting fresh, it’ll guide you from the beginning. If you already have a suite, it’s a framework to measure current impact and decide what to tackle next.

Phase 1 — Confidence

A pyramid collapses without a strong base. The same is true with automation. If teams don’t trust the test failures (or even successes), everything else becomes meaningless.

When results are unreliable, people stop acting on them. And when tests are ignored, automation loses its purpose. In many ways, unreliable automation is often worse than not having any at all.

The Tests Must Pass

Failures will happen. That’s not the issue. The danger is when teams normalize broken tests or flaky failures. Every red test should be taken seriously: investigated, understood, and resolved. While there are exceptions, the default culture must be: stop and fix. Adopt the mindset “all tests must pass”, and technical debt will quickly diminish before it starts. A mature automation test suite starts with an accountable mindset.

What Undermines Confidence

• Flakiness: Tests that pass or fail inconsistently without code changes. Common causes include race-condition, non-deterministic app behavior, dependent tests or poor test data management.
• Environment Instability: Where you will run your tests matter, especially if multiple options are needed. Can you guarantee tests will run reliably across all environments?
• Weak Data Strategies: Do tests always have the data they need? Is it static or dynamic? A strong data strategy reduces countless downstream failures. My favorite data management is through programmatic control.

Phase 1 is about establishing trust. Once failures are credible and environments stable, your suite stops being noise and starts being a safety net. A small, confident test suite is more impactful than a large, unstable one. Some actions items to consider:

• Research and implement flake-reduction practices for your tool of choice
• Create a culture of accountability: quarantine flaky tests and resolve them quickly
• Write tests environment-agnostically
• Define a consistent test data strategy that works across environments

If you’ve done these, you’re ready for Phase 2.

Phase 2 — Short-Term Impact

With trust established, the next step is to make automation useful right now. Tests should provide fast feedback and reduce risk during daily development.

If tests only run occasionally or if results arrive too late to act on, they don’t influence decision-making. The goal is to make automation an indispensable partner for developers, not a background chore.

This phase is all about defining an initial CI/CD strategy that suites your team’s development processes.

CI/CD Strategy

A good rule: the closer tests run to code changes, the more valuable they are. Running suites pre-merge ensures failures tie directly to specific commits, not multiple layers of changes. Fewer variables mean quicker triage.

Nightly or scheduled runs still have a place — especially for full regressions, but the longer the gap between code and results, the harder it is to debug.

Some common strategies:

• Pre-merge Tests: Run in under ~10 minutes. Cover critical paths first, then expand with performance in mind.
• Full Nightly Regression: Capture broader coverage where speed isn’t urgent.
• Custom Tag-Based Gates: Sub-groups of tests run based on criteria.

Results Visibility

Running tests is meaningless if no one notices the outcomes. Ensure results are clear, fast, and shared.

Every suite should generate artifacts accessible to all engineers. This includes screenshots, video, error logs and any other additional test information. Without proper artifacts, debugging failures becomes exponentially harder. Additionally, notifications should be immediate and integrated into tools your teams already use.

A professional rule of mine— act like Veruca Salt from Willy Wonka:
“I want those results and I want them now!”

Remember, Phase 2 is about usefulness. Once tests deliver fast, actionable feedback, they directly help teams ship better code, quicker. Developers know within minutes when a real-bug is introduced. Testers know when flake is first introduced, for immediate remediation.

Stick to the mantra: “all tests must pass”.

Once you start getting short-term feedback from your tests, it’s time to optimize them.

Phase 3 — Speed of Development

Once automation is trusted and embedded in the workflow, the focus shifts to efficiency. The question becomes: how can automation help us move faster without cutting corners?

At small scale, almost any automation adds value. But as suites grow, inefficiency turns automation into a bottleneck. Tests that take hours to run or are painful to debug become blockers instead of enablers. This phase has three areas of focus: writing, debugging and executing tests.

Write Tests Faster

Writing tests faster primarily comes down to test organization and structure. Expanding further:

• Standardize Structure: Use any pattern that makes sense to you and don’t worry about perfection. Any organization beats spaghetti-code chaos. Optimize over-time.
• Reuse Aggressively: Create helpers, builders, and shared libraries for scaleability.
• Proactive Test Planning: Review product tickets early to avoid last-minute gaps.
• Use AI-assisted Tooling: Just do it. There’s no excuse not to use AI anymore. Embrace our new overlords!
• Document: Look, we all know it sucks…but providing guides and common gotchas reduce ramp-up time as the team grows. What would past you wish they had when they first onboarded?

Debug Tests Faster

Test failures will happen so response time makes or breaks a suite’s value.

• Prioritize Readability: Choose clarity over cleverness; smaller, focused tests are easier to diagnose. Always write tests with future you in mind. “Will this make sense to me in six months?”.
• Reduce Variables: Run tests as close to the change as possible (prioritize pre-merge if not already implemented).
• Culture of Accountability: Build a habit of immediate triage: treat all fails with the same urgency so at least some resolution occurs.
• Improved Artifact Tools: Interactive runners, browser devtools, and in-depth logs are gold. Improve artifacts as needed.

Run Tests Faster

This one is simple. How fast do our tests run? Repeat after me: “Nobody brags about a three-hour test suite”. As the test suite grows, will the team still get quick value without slowing down the process?

• Parallelize: Split suites across multiple machines or containers. A must for pre-merge pipelines.
• Subset Tests: Run critical paths first; save broader regressions for later. Customize based on need and overall test performance.
• Optimize Code: Remove hard-coded waits, reduce unnecessary DOM interactions, apply tool best practices.

Phase 3 is about efficiency. Automation should accelerate delivery, not drag it down. When done well, it enables rapid iteration and frequent, confident releases. All of a sudden our monthly releases can now be reduced to weekly. Then daily. Then maybe even multiple times a day, if you’re feeling extra daring. All thanks to your automation test suite.

You deserve a raise.

Phase 4 — Long-Term Impact

The final phase is about sustainability. Once automation is fast, useful, and trusted, it must also deliver long-term value.

Teams and products evolve. Without continuous investment, automation rots: tests get flaky, results get ignored, and the pyramid crumbles. Which is all super sad. Professional advice, don’t be sad.

Long-term impact ensures automation remains a source of truth while showcasing just how cool your team is.

Metrics Inform, Not Punish

This phase is purely about responding to metrics, but use them wisely. Metrics should guide investment, not assign blame. Focus on impactful metrics that guide your automation roadmap. Simply, you don’t know what to improve if you don’t know what’s ineffective.

Some Suggestions:

• Test Coverage: Directional, not definitive. Pair with quality checks.
• Pass/fail and flake rates: Indicators of credibility.
• Execution time: Is the suite scaling with the team?
• Time-to-resolution (TTR): How quickly do teams fix failures?
• Defect detection efficiency (DDE): Percentage of bugs caught by automation.

If possible, consider augmenting these with a dashboard where visibility is further increased. Visual trends make it easier to consume historical trends and identify weaknesses. Plus bar graphs are fun and line graphs always look convincing. Don’t even threaten me with a good time and bring up pie charts.

This phase is small but important. It’s the culmination of all the previous phases, and purely intended to bring visibility into how well things went in the previous phases. It drives future revisions and ensures the test suite is never stagnant in it’s impact.

Phase 4 is all about trust at scale. Mature automation creates transparency, informs investment, and continues to improve over time.

Putting It All Together

The Automation Maturity Pyramid is a lot smaller than the Pyramids of Giza but much more relatable since those are real and in Egypt and this is thought-leadership and about testing. Just to clarify any confusion to this point.

But seriously, it’s about measuring your impact, one phase at a time. Building a successful automation test suite is hard without proper guidance. There’s many technical steps and failures can quickly become overwhelming and frustrating.

To recap:

• Confidence First: You have to trust your tests, always. The rest will follow.
• Early Wins: No matter the test suite size, obtain value. Start catching real issues.
• Take small steps: Steady improvements compound into big gains. Efficiency is a learning curve and only obtained through experience.
• Welcome Failures: Hello failures, come on it. Have a seat. Let’s talk about how you’re making my current life bad so we can make my future life good.
• Celebrate Progress: Building a reliable, impactful suite is a team achievement. Be proud of that green test run, those first 100 tests, or the first real-bug your suite caught. You’re a rockstar, genuinely.

Done well, automation isn’t overhead — it’s a strategic advantage. Build a base of trust, create fast feedback loops, optimize for speed, and commit to long-term transparency. That’s how you turn test automation into a driver of product success.

Best of luck in your climb. And as always, happy testing.

🔍 Tools and Technologies I Use for Digital Forensics Investigations

Digital forensics plays a critical role in modern cybersecurity — whether it’s responding to a data breach, investigating insider threats, or performing incident analysis after suspicious behavior. In my work as a security-minded engineer and DevSecOps practitioner, I’ve frequently had to identify, collect, and analyze digital evidence across endpoints, servers, and cloud environments.

In this blog post, I’ll walk you through the tools and technologies I rely on to conduct effective digital forensics investigations — categorized by use case.

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🧠 What Is Digital Forensics?

At its core, digital forensics is about identifying, preserving, analyzing, and reporting on digital data in a way that’s legally sound and technically accurate. The goal is to reconstruct events, identify malicious activity, and support security incident response.

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🧰 My Go-To Tools for Digital Forensics Investigations

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🗂️ Disk & File System Analysis

These tools help examine hard drives, deleted files, system metadata, and more:

• Autopsy (The Sleuth Kit) – A GUI-based forensic suite for analyzing disk images, file recovery, and timelines.

• FTK Imager – For creating and previewing forensic images without altering the original evidence.

• dd / dc3dd – Command-line tools to create low-level forensic disk images in Linux environments.

• EnCase (Basic familiarity) – A commercial powerhouse in forensic investigations, used primarily for legal-grade evidence analysis.

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🧬 Memory Forensics

Memory (RAM) often holds short-lived but critical evidence, like injected malware, live sessions, or loaded processes.

• Volatility Framework – Extracts details like running processes, DLLs, command history, network activity, and more from memory dumps.

• Rekall – An alternative memory analysis framework focused on automation and deep system state inspection.

✅ I’ve used Volatility to trace injected PowerShell payloads and enumerate hidden processes in live incident simulations.

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🌐 Network Forensics

Capturing and analyzing network traffic is essential for spotting data exfiltration, command-and-control activity, or lateral movement.

• Wireshark – Industry standard for packet analysis and protocol dissection.

• tcpdump – Lightweight CLI tool to capture traffic in headless environments or remote systems.

• NetworkMiner – Parses PCAP files to extract files, sessions, and credentials automatically.

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📊 Log & Timeline Analysis

Understanding what happened — and when — is key to reconstructing incidents.

• Timesketch – A timeline analysis tool for visualizing and collaborating on event data.

• Log2Timeline (Plaso) – Converts log files, browser histories, and system events into structured timelines.

• Sysinternals Suite – Includes gems like Procmon, PsExec, and Autoruns for Windows incident response.

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🧪 Malware Analysis (Static & Dynamic)

Understanding what a file does — before or while it runs — helps detect advanced threats and APT tools.

• Ghidra – Powerful open-source reverse engineering tool from the NSA for analyzing executables.

• x64dbg / OllyDbg – Popular debuggers for inspecting Windows executables.

• Hybrid Analysis / VirusTotal – Cloud-based tools to scan files and observe sandbox behavior.

• Cuckoo Sandbox – An open-source automated sandbox for observing malware behavior in a VM.

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☁️ Cloud & Endpoint Forensics

Modern investigations often span cloud platforms and remote endpoints:

• AWS CloudTrail, GuardDuty – Audit user and API activity in cloud environments.

• Microsoft Azure Defender – For cloud-native threat detection and log correlation.

• CrowdStrike Falcon / SentinelOne – Endpoint Detection and Response (EDR) tools for retrieving artifacts, hunting threats, and isolating compromised machines.

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🧰 Scripting & Automation

Scripting accelerates collection, triage, and analysis — especially in large-scale environments.

• Python – I use it to build custom Volatility plugins, PCAP parsers, or automate alert triage.

• Bash / PowerShell – For live memory dumps, log gathering, process inspection, and rapid automation.

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🧩 MITRE ATT&CK & DFIR Methodology

I map artifacts and behaviors to MITRE ATT&CK techniques (e.g., T1055 – Process Injection) to align with industry standards and communicate findings effectively.

I also follow established methodologies like:

• SANS DFIR process

• NIST 800-61 Incident Handling Guide

• Custom playbooks for containment, eradication, and recovery

✅ Summary: Digital Forensics Tools I Use

🔹 Disk & File System Analysis

• Autopsy (Sleuth Kit) – GUI-based forensic suite

• FTK Imager – Create and inspect forensic images

• dd / dc3dd – Low-level disk imaging on Linux

• EnCase – Commercial tool for deep disk investigations (basic familiarity)

🔹 Memory Forensics

• Volatility – Extract processes, DLLs, and sessions from RAM dumps

• Rekall – Advanced volatile memory analysis

🔹 Network Forensics

• Wireshark – Protocol and packet analysis

• tcpdump – Command-line traffic capture

• NetworkMiner – Extracts files and sessions from PCAP files

🔹 Log & Timeline Analysis

• Timesketch – Timeline visualization and correlation

• Plaso (log2timeline) – Converts raw logs into a forensic timeline

• Sysinternals Suite – Live system inspection (Procmon, PsExec, Autoruns)

🔹 Malware Analysis

• Ghidra – Static reverse engineering

• x64dbg / OllyDbg – Debuggers for binary inspection

• Hybrid Analysis / VirusTotal – Behavioral analysis and threat intel

• Cuckoo Sandbox – Automated dynamic malware analysis

🔹 Cloud & Endpoint Forensics

• AWS CloudTrail / GuardDuty – Monitor API and security activity

• Microsoft Defender / Azure Logs – Cloud-native alerting and forensics

• CrowdStrike Falcon / SentinelOne – EDR tools for endpoint activity and IOC collection

🔹 Scripting & Automation

• Python – For custom plugins, log parsers, automation

• Bash / PowerShell – For system triage, memory dumps, and log collection

🔹 Methodology

• Align findings with MITRE ATT&CK

• Follow structured DFIR frameworks like SANS, NIST 800-61, and custom playbooks

🎯 Final Thoughts

Digital forensics isn’t just for breach responders — it’s a key skill for DevSecOps, SDETs, and any security-conscious engineer. Whether you’re building incident response workflows, simulating attacks, or validating your EDR, knowing how to collect and interpret evidence makes you far more effective.

📱 Mobile App Testing: 10 Critical Test Scenarios You Can’t Miss (That Go Beyond Web UI Testing)

When it comes to testing mobile applications, the challenges go far beyond what typical web UI testing entails. Mobile apps must work flawlessly across a fragmented ecosystem of devices, screen sizes, OS versions, sensors, network conditions—and still deliver a high-performance experience. That’s why test engineers must design test cases that account for mobile-specific conditions that web-based apps don’t encounter.

In this post, we’ll break down the 10 critical mobile app test cases that every QA engineer should prioritize—and explain how they differ from traditional web UI testing.

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✅ 1. Installation & Launch

Unlike web apps, mobile apps must be installed, upgraded, and uninstalled through OS-specific stores like Google Play or Apple App Store.

Test Cases:

• App installs/uninstalls cleanly on all supported devices.

• Launches successfully after a clean install or version upgrade.

• First-launch behavior (onboarding, permission prompts) works without failure.

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🌐 2. Device & OS Compatibility

Mobile ecosystems are highly fragmented. You must ensure compatibility across OS versions, hardware specs, and screen dimensions.

Test Cases:

• Verify app functionality on Android 10–14 and iOS 14–17.

• Check responsiveness across tablets, foldables, and small-screen phones.

• Test on low-RAM or budget devices (to catch memory issues).

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📶 3. Network Conditions

Mobile users are always switching between 5G, Wi-Fi, and even no network. Your app must handle this gracefully.

Test Cases:

• App behaves predictably with no internet or low bandwidth.

• Test auto-retries for failed API calls due to timeouts.

• Switching from Wi-Fi to mobile data mid-session doesn’t break functionality.

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🔄 4. Background & Resume Behavior

A mobile app should maintain state and not crash when interrupted by a phone call or switching to another app.

Test Cases:

• App resumes gracefully from background state.

• Data entry is preserved when the user switches away and returns.

• Proper behavior after a cold restart or after device reboot.

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🔋 5. Battery & Performance

Performance testing on mobile goes beyond responsiveness—it’s also about battery and resource consumption.

Test Cases:

• No excessive battery drain during idle or active use.

• Monitor CPU/memory usage over time (watch for leaks).

• Measure cold and warm start times.

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🔐 6. Permission Handling

Mobile apps rely on permissions to access hardware features. You must test both granting and denying permissions.

Test Cases:

• App only requests necessary permissions.

• Behavior is graceful when permissions are denied or revoked.

• Scoped storage compliance (Android 11+) is in place.

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🔔 7. Push Notifications

Push notifications are a core engagement channel and must work across all app states.

Test Cases:

• Push received when app is in background or killed.

• Tapping the notification leads to correct app screen.

• Notifications respect user opt-in/opt-out settings.

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📲 8. Gestures & UI Flexibility

Mobile users interact via gestures and virtual keyboards, making UX more dynamic than web.

Test Cases:

• UI responds correctly to swipes, taps, long presses, and pinch-to-zoom.

• Keyboard overlays don’t hide important input fields.

• Smooth adaptation to dark mode, orientation changes (portrait ↔ landscape).

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🔐 9. Security Testing

Security is non-negotiable, especially with personal data or financial transactions involved.

Test Cases:

• Secure storage for sensitive data (e.g., keystore/token vault).

• No sensitive logs left in logcat or crash logs.

• Behavior on rooted/jailbroken devices is safely restricted.

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📊 10. Analytics & Store Compliance

Apps often embed SDKs for analytics and crash reporting, and must comply with store policies.

Test Cases:

• Verify Firebase, GA, or Crashlytics events are firing correctly.

• App follows Play Store / App Store policy (e.g., no deprecated APIs).

• Correct versioning and metadata shown in store listing.

  
  

  🧪 Final Thoughts

  If you’re only testing your mobile app like a web app, you’re missing half the picture. Mobile brings unique challenges and requires a deeper, device-aware test strategy. The 10 critical mobile test areas above should form the core of your test planning, especially for high-scale production apps used across a variety of devices and conditions.

Appium Architecture - Core Concepts

What is Appium?

It’s a NodeJS based open-source tool for automating mobile applications. It supports native, mobile web, and hybrid applications on iOS mobile, Android mobile, and Windows desktop platforms.

Using Appium, you can run automated tests on physical devices or emulators, or both.

Let’s understand the above Appium architecture diagram.

• Appium is a client-server architecture. The Appium server communicates with the client through the HTTP JSONWire Protocol using JSON objects.
• Once it receives the request, it creates a session and returns the session ID, which will be used for communication so that all automation actions will be performed in the context of the created session.
• Appium uses the UIAutomator test framework to execute commands on Android devices and emulators.
• Appium uses the XCUITest test framework to execute commands on Apple mobile devices and simulators.
• Appium uses WinAppDriver to execute commands for Windows Desktop apps. It is bundled with Appium and does not need to be installed separately.

Appium - Android visual interaction flow

Let’s understand the interaction flow between the code and the Android device via the Appium server.

• The client sends the request to the Appium server through the HTTP JSONWire Protocol using JSON objects.
• Appium sends the request to UIAutomator2.
• UIAutomator2 communicates to a real device/simulator using bootstrap.jar which acts as a TCP server.
• bootstrap.jar executes the command on the device and sends the response back.
• Appium server sends back the command execution response to the client.

Appium - iOS visual interaction flow

Let’s understand the interaction flow between the code and the iOS device via the Appium server.

• The client sends the request to the Appium server through the HTTP JSONWire Protocol using JSON objects.
• Appium sends the request to XCUITest.
• XCUITest communicates to a real device/simulator using bootstrap.js which acts as a TCP server.
• bootstrap.js executes the command on the device and sends the response back.
• The Appium server sends the command execution response to the client.

Whiteboard Sessions

• IOS flow architecture

• Android flow architecture

• Drivers which appium supports
• UI Automator2 (Android)
• Espresso (Android)
• WinApp (Windows)
• MAC Driver (Mac OS)
• XCUITest (IOS above 9.3 version)
• UI Automation (IOS below 9.3 version)
• Tizen (for samsung)

Happy Learning :)