Showing posts with label Automation Testing. Show all posts
Showing posts with label Automation Testing. Show all posts

Saturday, August 2, 2025

๐Ÿ” 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.


๐Ÿง  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 eventsidentify malicious activity, and support security incident response.


๐Ÿงฐ My Go-To Tools for Digital Forensics Investigations


๐Ÿ—‚️ 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.


๐Ÿงฌ 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.


๐ŸŒ 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.


๐Ÿ“Š 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 ProcmonPsExec, and Autoruns for Windows incident response.


๐Ÿงช 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.


☁️ 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.


๐Ÿงฐ 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.


๐Ÿงฉ 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

๐Ÿ”น 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)

๐Ÿ”น Memory Forensics

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

  • Rekall – Advanced volatile memory analysis

๐Ÿ”น Network Forensics

  • Wireshark – Protocol and packet analysis

  • tcpdump – Command-line traffic capture

  • NetworkMiner – Extracts files and sessions from PCAP files

๐Ÿ”น 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)

๐Ÿ”น 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

๐Ÿ”น 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

๐Ÿ”น Scripting & Automation

  • Python – For custom plugins, log parsers, automation

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

๐Ÿ”น Methodology

  • Align findings with MITRE ATT&CK

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

๐ŸŽฏ 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.

Wednesday, July 30, 2025

๐Ÿ” How I Used OOPS Concepts in My Selenium Automation Framework (with Real-World Examples)


In today’s test automation world, building scalable, maintainable, and readable frameworks is non-negotiable. One of the key enablers of such robust automation design is the effective use of Object-Oriented Programming (OOPS) principles.

In this post, I’ll walk you through how I have practically applied OOPS concepts like Encapsulation, Inheritance, Abstraction, and Polymorphism in building a modern Selenium automation framework using Java and Page Object Model (POM)—with real-world use cases from a payments application.


๐Ÿงฑ 1. Encapsulation

 – Grouping Page Behaviors & Data

In POM, each web page is represented by a Java class. All locators and associated actions (methods) are bundled into the same class, providing encapsulation.

Example:

LoginPage.java might contain:

public class LoginPage {

    @FindBy(id="username")

    private WebElement usernameInput;


    @FindBy(id="password")

    private WebElement passwordInput;


    @FindBy(id="loginBtn")

    private WebElement loginButton;


    public void login(String user, String pass) {

        usernameInput.sendKeys(user);

        passwordInput.sendKeys(pass);

        loginButton.click();

    }

}

This hides internal mechanics from external classes, exposing only the method login()—a clean interface for test classes.


๐Ÿงฌ 2. Inheritance

 – Reusability of Test Utilities

Inheritance is used to extend common functionality across test components like base test setup, common utilities, or driver management.

Example:

public class BaseTest {

    protected WebDriver driver;


    @BeforeMethod

    public void setup() {

        driver = new ChromeDriver();

        driver.manage().timeouts().implicitlyWait(Duration.ofSeconds(10));

    }


    @AfterMethod

    public void tearDown() {

        driver.quit();

    }

}

Then, individual test classes inherit this:

public class LoginTests extends BaseTest {

    @Test

    public void testValidLogin() {

        new LoginPage(driver).login("user", "pass");

        // assertions

    }

}

๐ŸŽญ 3. Polymorphism

 – Interface-Based Design

Polymorphism allows flexible and scalable design, especially when using interface-driven development.

Use Case: Suppose your framework needs to support both Chrome and Firefox.

public interface DriverManager {

    WebDriver getDriver();

}

Concrete implementations:

public class ChromeManager implements DriverManager {

    public WebDriver getDriver() {

        return new ChromeDriver();

    }

}


public class FirefoxManager implements DriverManager {

    public WebDriver getDriver() {

        return new FirefoxDriver();

    }

}

Now, switching browsers is easy without changing test logic:

DriverManager manager = new ChromeManager(); // or FirefoxManager

WebDriver driver = manager.getDriver();


๐Ÿงฉ 4. Abstraction

 – Hiding Implementation Behind Layers

Abstraction is used in frameworks via utility and wrapper classes to hide the complexity of Selenium commands.

Example: Create a utility method for dropdown handling:

public class DropdownUtils {

    public static void selectByVisibleText(WebElement dropdown, String text) {

        new Select(dropdown).selectByVisibleText(text);

    }

}

Now testers use just:

DropdownUtils.selectByVisibleText(dropdownElement, "United States");

This hides internal logic and improves readability.


๐Ÿ Final Thoughts

OOPS principles are not just theoretical—they are the foundation of real-world, enterprise-grade test automation frameworks. By applying:

  • Encapsulation (clean page classes),

  • Inheritance (shared test logic),

  • Polymorphism (browser/interface abstractions), and

  • Abstraction (utility layers),

you build a test architecture that’s scalable, readable, and easily maintainable.

This approach isn’t limited to Selenium. You can apply the same mindset in API testing frameworks, Appium, Playwright, and beyond.

Monday, July 28, 2025

๐Ÿ”ง Intercepting Android API Traffic with Burp Suite and a Rooted Emulator

Testing the security and behavior of Android apps often requires intercepting and analyzing API requests and responses. In this guide, we’ll walk through setting up an Android emulator to work with Burp Suite, enabling interception of HTTPS traffic and performing advanced manipulations like brute-force attacks.

⚠️ Requirements:

  • Android Emulator (AVD)
  • Root access (via Magisk)
  • Burp Suite (Community or Professional Edition)


๐Ÿ›  Step-by-Step Setup Guide

✅ 1. Install Burp Suite

  • Download Burp Suite Community Edition (2023.6.2) from PortSwigger.

  • Launch the app and navigate to:

    Proxy → Options → Proxy Listeners → Import/Export CA Certificate

✅ 2. Export and Install Burp CA Certificate

  1. Export the CA Certificate in DER format and save it with a .crt extension.

  2. Transfer this .crt file to your emulator (drag and drop works fine).

  3. On the emulator:

    • Open Settings → Security → Encryption & Credentials

    • Tap Install from SD card

    • Choose the transferred certificate.

  4. Confirm installation:

    • Go to Trusted Credentials → User and verify the certificate is listed.


๐Ÿ”“ 3. Root the Emulator

To trust user-installed certificates at the system level (bypassing Android’s certificate pinning), you must root the emulator.

Tools You’ll Need:

Rooting Process:

  1. Ensure your AVD is running before executing the root script.

  2. Unzip rootAVD and run the following command in terminal:

./rootAVD.sh ~/Library/Android/sdk/system-images/android-33/google_apis/arm64-v8a/ramdisk.img

  1. ✅ For Play Store-enabled AVDs, use google_apis_playstore in the path.
  2. Your emulator will shut down automatically after patching.


⚙️ 4. Install Magisk & Trust Certificates

  1. Restart your emulator and open the Magisk app.

  2. Navigate to Modules → Install from Storage → Select AlwaysTrustUserCerts.zip

  3. The emulator will restart again.

  4. Verify the certificate now appears under System certificates, not just User.


๐ŸŒ 5. Connect Emulator to Burp Suite

In Burp Suite:

  1. Go to Proxy → Options → Add Listener

  2. Choose an IP from the 172.x.x.x range.

  3. Set port to 8080 and click OK.

On the Emulator:

  1. Connect to Wi-Fi.

  2. Long press the connected Wi-Fi → Modify Network → Proxy: Manual

  3. Set:

    • Host: Burp Suite IP (e.g., 172.x.x.x)

    • Port: 8080

    • Save the changes.


๐Ÿš€ 6. Intercept Traffic

  • Launch your Android debug app.

  • Open HTTP History in Burp Suite to monitor incoming requests/responses.


๐ŸŽฏ Conclusion

You now have a fully configured Android emulator that allows you to:

  • Intercept and inspect HTTPS API traffic

  • Analyze request/response headers and payloads

  • Perform manual or automated security tests (e.g., brute force attacks)

This setup is ideal for mobile QA, security testing, or reverse engineering Android applications in a safe, isolated environment.


๐Ÿ’ฌ Feel free to bookmark or share this guide with fellow testers or developers diving into mobile app traffic inspection.
Happy hacking!

Wednesday, July 2, 2025

๐Ÿ” Testing an ML Model ≠ Testing Traditional Code


 Testing a Machine Learning (ML) model is very different from testing traditional software because:

  • The output is probabilistic, not deterministic.

  • The behavior depends on data patterns, not just logic.

To test an ML model effectively, you need a multi-layered strategy combining functionaldata-driven, and performance-based testing.


✅ 1. Unit Testing the ML Pipeline (Code-Level)

๐Ÿ” What to Test:

  • Data preprocessing methods (normalization, encoding)

  • Feature extraction logic

  • Model loading and inference function

๐Ÿ’ก Example:

Monday, June 30, 2025

๐Ÿ•ต️‍♂️ SVG vs Shadow DOM in Selenium: A Tester’s Guide with Real-World Examples


Have you ever clicked an element in Selenium, only to watch nothing happen—again and again? Welcome to the world of SVGs and Shadow DOMs, where traditional locators fail and frustration often begins.

In this post, we’ll demystify these tricky elements, explain how to work with them in Selenium (Java), and walk through real-world examples that every automation engineer should know.


๐Ÿงฉ What Are SVG and Shadow DOM?

Tuesday, June 24, 2025

Performance Metrics Measure

Performance testing is only as effective as the metrics you measure and act on. In distributed systems, it’s not just about response time — it’s about end-to-end system behavior under loadresource utilization, and failure thresholds.


Here’s how I typically categorize and collect key performance testing metrics, based on my real-world experience with high-scale platforms.


✅ 1. Core Performance Metrics

Metric

Why It Matters

Throughput (TPS/QPS)

Measures system capacity — are we handling the expected load?

Latency (P50, P95, P99)

Helps detect tail latencies and slow paths. P99 is critical for user experience.

Error Rate (%)

Any spike under load suggests bottlenecks or instability.

Concurrency

Helps test thread safety and async processing under pressure.

Time to First Byte / Full Response

Important for APIs and UI performance perception.


✅ 2. Resource Utilization Metrics

Resource

Metric

Purpose

CPU

% Usage, context switches

Detect CPU-bound operations

Memory

Heap/Non-heap usage, GC pause time

Tune for memory leaks, OOM risk

Disk I/O

Read/write IOPS, latency

Ensure storage doesn’t become a bottleneck

Network

Throughput, packet loss, RTT

Catch bandwidth saturation, dropped packets

Thread Pools

Active threads, queue size

Avoid thread starvation under load


Tools used: PrometheusGrafanaNew Relictopvmstatiostatjstatjmapasync-profiler

✅ 3. Application-Specific Metrics

Component

Metrics to Monitor

Kafka

Consumer lag, messages/sec, ISR count

DB/Cache (e.g., Redis, Postgres)

Query latency, cache hit/miss, slow query logs

Elasticsearch

Query throughput, indexing rate, segment merges, node GC

Spark Jobs

Task duration, shuffle read/write, executor memory spill

API Layer

Response codes breakdown (2xx, 4xx, 5xx), rate-limited requests

✅ 4. Infrastructure & Cluster Health

Service

Key Indicators

Kubernetes

Pod restarts, node CPU/mem pressure, eviction count

Disk Space

Free space per node, inode usage

GC Behavior

GC frequency, full GC %, pause durations

Auto-scaling Logs

Scale-up/down events, throttle rates


✅ 5. Stability & Reliability Metrics

Category

Why It Matters

Test Flakiness Rate

Detects inconsistent behavior under load

Success % under chaos

How gracefully does the system degrade?

Retry Count / Circuit Breaker Trips

Signals downstream failures under load

Service Uptime %

Validates HA/resilience against failures


๐Ÿ”ง How I Collect & Analyze Metrics

  • Test Harness Integration: I integrate metrics collection directly into test frameworks (e.g., expose custom Prometheus counters in Java test harness).

  • Dashboards: Build tailored Grafana dashboards for real-time observability of test runs.

  • Thresholds & SLOs: Define thresholds for acceptable P95 latency, error rate, and resource usage — any breach flags a performance regression.

  • Baseline Comparison: Run nightly jobs to compare metrics vs. last known good release and flag deltas.

Monday, June 16, 2025

Generative AI: Transforming Software Testing

Generative AI (GenAI) is poised to fundamentally transform the software development lifecycle (SDLC), particularly in the realm of software testing. As applications grow increasingly complex and release cycles accelerate, traditional testing methods are proving inadequate. GenAI, a subset of artificial intelligence, offers a game-changing solution by dynamically generating test cases, identifying potential risks, and optimising testing processes with minimal human input. This shift promises significant benefits, including faster test execution, enhanced test coverage, reduced costs, and improved defect detection. While challenges related to data quality, integration, and skill gaps exist, the future of software testing is undeniably intertwined with the continued advancement and adoption of GenAI, leading towards autonomous and hyper-personalised testing experiences.

Main Themes and Key Ideas

1. The Critical Need for Generative AI in Modern Software Testing

Traditional testing methods are struggling to keep pace with the evolving landscape of software development.

  • Increasing Application Complexity: Modern applications, built with "microservices, containerised deployments, and cloud-native architectures," overwhelm traditional tools. GenAI helps by "predicting failure points based on historical data" and "generating real-time test scenarios for distributed applications."
  • Faster Release Cycles in Agile & DevOps: The demand for rapid updates in CI/CD environments necessitates accelerated testing. "According to the World Quality Report 2023, 63% of enterprises struggle with test automation scalability in Agile and DevOps workflows." GenAI "automates the creation of high-coverage test cases, accelerating testing cycles" and "reduces dependency on manual testing, ensuring faster deployments."
  • Improved Test Coverage & Accuracy: Manual test scripts often miss "edge cases," leading to post-production defects. GenAI "analyzes real-world user behavior, ensuring comprehensive test coverage" and "automatically generates test scenarios for corner cases and security vulnerabilities."
  • Reducing Manual Effort and Costs: "Manual testing and script maintenance are labor-intensive." GenAI "automatically generates test scripts without human intervention" and "adapts existing test cases to application changes, reducing maintenance overhead."

2. Core Capabilities and Benefits of Generative AI in Software Testing

GenAI leverages machine learning and AI to create new content based on existing data, leading to a paradigm shift in testing.

  • Accelerated Test Execution: "Faster test cycles reduce time-to-market."
  • Enhanced Test Coverage: "AI ensures comprehensive testing across all application components."
  • Reduced Script Maintenance: "Self-healing capabilities minimise script updates."
  • Cost Efficiency: "Lower resource allocation reduces testing costs."
  • Better Defect Detection: "Predictive analytics identify defects before they impact users."

3. Key Applications of Generative AI in Software Testing

GenAI’s practical applications are diverse and address many pain points in current testing practices.

  • Automated Test Case Generation: GenAI "analyzes application logic, past test results, and user behavior to create test cases," identifying "missing test scenarios" and ensuring "edge case testing."
  • Self-Healing Test Automation: Addresses the significant pain point of script maintenance. GenAI "uses computer vision and NLP to detect UI changes" and "automatically updates automation scripts, preventing test failures." Examples include Mabl and Testim.
  • Test Data Generation & Management: Essential for complex applications, GenAI "creates synthetic test data that mimics real-world user behavior" and "ensures compliance with data privacy regulations (e.g., GDPR, HIPAA)." Examples include Tonic AI and Datomize.
  • Defect Prediction & Anomaly Detection: GenAI "analyzes past defect data to identify patterns and trends," "predicts high-risk areas," and "detects anomalies in logs and system behavior." Appvance IQ is cited for reducing "post-production defects by up to 40%."
  • Optimising Regression Testing: GenAI "identifies the most relevant test cases for each code change" and "reduces test execution time by eliminating redundant tests." Applitools uses "AI-driven visual validation."
  • Natural Language Processing (NLP) for Test Case Creation: Bridges the gap between manual and automated testing by "converting plain-English test cases into automation scripts," simplifying automation for non-coders.

4. Challenges in Implementing Generative AI

Despite the immense potential, several hurdles need to be addressed for successful adoption.

  • Data Availability & Quality: GenAI requires "large, high-quality datasets," and "poor data quality can lead to biased or inaccurate test cases."
  • Integration with Existing Tools: "Many enterprises rely on legacy systems that lack AI compatibility."
  • Skill Gap & AI Adoption: QA teams require "AI/ML expertise," necessitating "upskilling programs."
  • False Positives & Over-Testing: AI models "may generate excessive test cases or false defect alerts, requiring human oversight."

5. The Future of Generative AI in Software Testing

The article forecasts significant advancements leading to more autonomous and integrated testing.

  • Autonomous Testing: Future frameworks will "not only design test cases but also execute and analyze them without human intervention." This includes "Self-healing test automation," "AI-driven exploratory testing," and "Autonomous defect triaging."
  • AI-Augmented DevOps: The fusion of GenAI with DevOps will create "hyper-automated CI/CD pipelines" capable of "predicting failures and resolving them in real time." This encompasses "AI-powered code quality analysis," "Predictive defect detection," and "Intelligent rollback mechanisms."
  • Hyper-Personalized Testing: GenAI will enable testing "tailored to specific user behaviors, preferences, and environments," including "Dynamic test scenario generation," "AI-driven accessibility testing," and "Continuous UX optimisation."

Conclusion

Generative AI is not merely an enhancement but a "necessity rather than an option" for organisations seeking to maintain software quality in a rapidly evolving digital landscape. By addressing the complexities of modern applications, accelerating release cycles, improving coverage, and reducing costs, GenAI will enable enterprises to deliver "faster, more reliable software." While challenges require strategic planning and investment, the trajectory of GenAI in software testing points towards an increasingly automated, intelligent, and efficient future.

Generative AI in Software Testing



Generative AI (GenAI) is poised to fundamentally transform the software development lifecycle (SDLC)—especially in software testing. As applications grow in complexity and release cycles shorten, traditional testing methods fall short. GenAI offers a game-changing solution: dynamically generating test cases, identifying risks, and optimizing testing with minimal human input.

Key benefits include:

  • Faster test execution

  • Enhanced coverage

  • Cost reduction

  • Improved defect detection

Despite challenges like data quality, integration, and skill gaps, the future of software testing is inseparably linked to GenAI, paving the way toward autonomous and hyper-personalized testing.


๐Ÿš€ Main Themes & Tools You Can Use


1. The Critical Need for GenAI in Modern Software Testing

Why GenAI? Traditional testing can’t keep pace with:

  • Complex modern architectures (microservices, containers, cloud-native)

    • GenAI predicts failure points using historical data and real-time scenarios.

    • ๐Ÿ› ️ Tool ExampleDiffblue Cover — generates unit tests for Java code using AI.

  • Agile & CI/CD Release Pressure

    • According to the World Quality Report 2023, 63% of enterprises face test automation scalability issues.

    • ๐Ÿ› ️ Tool ExampleTestim by Tricentis — uses AI to accelerate test creation and maintenance.

  • Missed Edge Cases

    • GenAI ensures coverage by analyzing user behavior and generating test cases automatically.

    • ๐Ÿ› ️ Tool ExampleFunctionize — AI-powered test creation based on user journeys.

  • High Manual Effort

    • GenAI generates and updates test scripts autonomously.

    • ๐Ÿ› ️ Tool ExampleMabl — self-healing, low-code test automation platform.


2. Core Capabilities and Benefits of GenAI in Testing

Capability

Impact

Accelerated Test Execution

Speeds up releases

Enhanced Test Coverage

Covers functional, UI, and edge cases

Reduced Script Maintenance

AI auto-updates outdated tests

Cost Efficiency

Fewer resources, less manual work

Improved Defect Detection

Finds bugs early via predictive analytics


๐Ÿ› ️ Tool ReferenceAppvance IQ — uses AI to improve defect detection and test coverage.


3. Key Applications of GenAI in Software Testing

✅ Automated Test Case Generation

  • Analyzes code logic, results, and behavior to generate meaningful test cases.

  • ๐Ÿ› ️ ToolTestsigma — auto-generates and maintains tests using NLP and AI.

๐Ÿ”ง Self-Healing Test Automation

  • Automatically adapts to UI or logic changes.

  • ๐Ÿ› ️ Tools:

๐Ÿงช Test Data Generation & Management

  • Creates compliant synthetic data simulating real-world conditions.

  • ๐Ÿ› ️ Tools:

    • Tonic.ai — privacy-safe synthetic test data

    • Datomize — dynamic data masking & synthesis

๐Ÿ” Defect Prediction & Anomaly Detection

  • Identifies defect-prone areas before they affect production.

  • ๐Ÿ› ️ ToolAppvance IQ

๐Ÿ” Optimizing Regression Testing

  • Prioritizes relevant tests for code changes.

  • ๐Ÿ› ️ ToolApplitools — AI-driven visual testing and regression optimization.

✍️ NLP for Test Case Creation

  • Converts natural language into executable tests.

  • ๐Ÿ› ️ ToolTestRigor — plain English to automated test scripts.


4. Challenges in Implementing GenAI

Challenge

Description

Data Availability & Quality

Poor data → inaccurate test generation

Tool Integration

Legacy tools may lack AI support

Skill Gap

Requires upskilling QA teams in AI/ML

False Positives

Over-testing may need human review


๐Ÿ› ️ Solution Suggestion: Use platforms like Katalon Studio that offer GenAI plugins with low-code/no-code workflows to reduce technical barriers.


5. The Future of GenAI in Software Testing

๐Ÿค– Autonomous Testing

  • Self-designing, executing, and analyzing test frameworks.

  • ๐Ÿ› ️ ToolFunctionize

๐Ÿ”„ AI-Augmented DevOps

  • Integrated CI/CD with AI-based code quality checks and rollback mechanisms.

  • ๐Ÿ› ️ ToolHarness Test Intelligence — AI-powered testing orchestration in pipelines.

๐ŸŽฏ Hyper-Personalized Testing

  • Tailors tests to real user behavior and preferences.

  • ๐Ÿ› ️ ToolTestim Mobile — for AI-driven UX optimization and mobile test personalization.


๐Ÿงฉ Conclusion

Generative AI isn’t just an enhancement — it’s becoming a necessity for QA teams aiming to keep pace in a high-velocity development environment.

By combining automation, intelligence, and adaptability, GenAI can enable faster releases, fewer bugs, and more robust software.

✅ Start exploring tools like Testim, Appvance IQ, Mabl, Functionize, and Applitools today to get a head start on the future of intelligent testing.


๐Ÿ’ฌ Let’s Discuss:

Have you implemented GenAI tools in your QA process? What has been your experience with tools like TestRigor, Tonic.ai, or Mabl?

๐Ÿ‘‡ Drop your thoughts or tool recommendations in the comments.


#GenAI #SoftwareTesting #Automation #AIinQA #TestAutomation #DevOps #SyntheticData #AItools #QualityEngineering

Thursday, September 2, 2021

API Automation Guidelines

 As an automation engineer, we need to follow a few guidelines.

Few of the guidelines as below:

  • No code change in the master branch directly - work on feature branches

  • Build the project locally before raising a PR

  • Run the test(s) locally before raising a PR

  • There has to be at least 1 person who reviews a PR

    • Post your PR link on the slack channel tagging concerned people and the reviewer would merge the PR and update with a comment on the slack thread

    • Reviewer has to ensure that the newly added tests are passing on the pipeline before merging

  • Ensure we add proper commit message while committing any code

    • Example: “automated customer cancel in order flow” or “modified X to achieve Y“. Basically meaningful commit instead of just writing “commit“ “fixed“ etc

  • Test Method should be 40-50 lines long at max

    • Break it into private methods if needed

    • Name the test method such that there is NO need of documenting its behaviour - test method names should start with "verify******"

  • Do NOT span any PR beyond 3-4 days - either get it merged within this time period or close the current one (if it is spilling over 3-4 days) and create another after local rebase

  • Put all assertions in Test classes (use return in helper methods to get what needs to be compared for assertions)

  • Always add a message with assertions to be logged upon a failure - it gives the good context of the issue in the report upon a failure, upfront

  • Ensure the correct tags are attached to the scenarios/tests before raising a PR (Smoke, Regression, ServiceType)

  • Don’t use “System.out.println” in the code, use TestNG logger only.

  • Add allure annotations properly so test reports can be used effectively.

  • Test your code with all negative cases. Avoid null pointer exceptions in your code.

  • Add logging for each api call (Request Call/Request Payload/Response Json are the minimal ones).

  • Add all other necessary logging for your test case so it can be helpful later for the debugging

  • Avoid adding redundant code and create a helper method instead.

  • Always add health check verification for the new APIs.

NFR Template/Checklist for JIRA


To make NFR as predefined template/checklist, we came up with few critical points to start with and it would be auto-populated as and when someone creates any story to the project.

Idea is to pushing NFR in initial phase discussion like designing and developing and as a cross check goes to QA. Apart from predefined template/checklist, anyone can work on other points too for which checklist already been published in Confluence under Guidelines and having predefined checklist in each story would ensure we are having NFR discussions too along with functional towards any deliverables to production.


NFR ListChecklist_PointsComments if any
Logging
Have we ensured we are not logging access logs?Access logs represent the request logs containing the API Path, status code, latencies & and any information about the request. We can avoid logging this since we already have this information in the istio-proxy logs
Have we ensured we didn't add any sort of secrets in logs (DB passwords, keys, etc) ?
Have we ensured that payload gets logged in the event of an error ?
Have we ensured that logging level can be dyanamic configured ?
Have we ensured that entire sequence of events in particular flow can be identified using an identifier like orderId or anything- The logs added should be meaningful enough such that anyone looking at the logs, regardless of whether they have context on the code should be able to understand the flow.
- For new features, it maybe important that the logs are logged as info to help ensure the feature is working is expected in production. Once we have confidence that the feature is working as expected, we could change these logs to debug unless required. Devs could take a call based on the requirement.
Have we ensured that we are using logging levels diligently ?
Timeouts
Have we ensured that we have set a timeout for database calls ?
Have we ensured that we have set a timeout for API call ?
Have we ensured that timeouts are derived from dependent component timeouts ?An API might have dependencies on few other components (APIs, DB queries, etc) internally. It is important the overall API timeout is considered after careful consideration of the dependent component timeouts.
Have we ensured that we have set a HTTP timeout ?Today, in most of our services we set timeouts at the client (caller). But we should also start looking at setting timeouts for requests on the server (callee). This way we ensure we kill the request in the server if it exceeds a timeout regardless of whether the client closes the connection or not.
Response Codes
Have we ensured that we are sending 2xx only for successfull scenarios ?
Have we ensured that we are sending 500 only for unexpected errors (excluding timeouts) ?
Have we ensured that we are sending 504 for a timeout error ?
Perf
Have we ensured that we did perf testing of any new API we build to get benchmark of the same we can go as per the expectations and can track accordingly going forward ?
We should identify below parameters as part of the perf test & any other additional info as per need:
- Max number of requests a pod can handle with the allocated resources
- CPU usage
- Memory usage
- Response times


Have we ensured we did perf testing of existing APIs if there are changes around it to make sure we didn’t impact existing benchmark results ?
Feature ToggleHave we ensured that we have feature toggle for new features to be able to go back to the old state at any given point until we are confident of the new changes. We may need to have toggles like feature will be enabled for specific users or city ?
ResiliencyHave we ensured that we are resilient to failures of dependent components (database, services ) ?
MetricsHave we ensured that we are capturing the right metrics in prometheous ?Below are some of the metrics that could be captured based on need or criticality:
- Business metrics (example: number of payment gateway failures)
- Business logic failures (example: number of rider prioritization requests that failed)
- Or any other errors which would be important to help assess the impact in a critical flow could be captured as metrics.
Security
Have we ensured that right authentication scheme is active at the gateway level ?This is applicable when we are adding any end point on Kong(Gateway). 
- any of the authentication plugins (jwt,key-auth/basic-auth) must be defined either at the route level or on the service level
- for gateway kong end points, acl plugin must be added and same group must be present on the consumer definition.
Have we ensured that proper rate limiting applied at the gateway level ?This is applicable when we are adding any end point on Kong(Gateway).Team leads are the code owners, so one of them have to check this when approving the PR. 
- rate limiting plugin needs to be enabled on the route / service level on the PR raised against kong-config. 
Have we ensured that we are retreiving the userId from JWT ?if requests is coming from kong, userid in requestbody should be matched with headers. Or for fetching any user related information, we have to read the userId only from the header populated by kong (x-consumer-username).

 


It would be populated in all Jira stories across projects as a predefined NFR checklist as given below screenshot.




My Profile

My photo
can be reached at 09916017317