Where Every Test Tells a Story!
Where Every Test Tells a Story!
This document provides a detailed analysis of a security vulnerability discovered in a web application that allows for JavaScript injection through concatenated input fields. The analysis covers the severity of the vulnerability, its potential impacts on various stakeholders, technical implications,…
This document outlines the critical issues arising from a bug in the JPEG parser of a surveillance camera system, specifically triggered by the CEO’s patterned shirt. The severity of the impacts spans functional, user experience, business, operational, security, financial, and…
Let us see Impact Analysis for this “Empty SMS” Bug, which in turn is equivalent to RISK ANALYSIS in some way. 1. Functional Impact 2. User Experience Impact 3. Business Impact If you are more interested in RISK ANALYSIS do…
Testing negative scenarios is an art form, especially in 2024, when data dependencies and intricacies in applications are only increasing. With data-driven applications in finance, healthcare, and e-commerce, it’s vital to address edge cases and push applications to their limits—particularly…
In my journey of testing GraphQL APIs, I’ve uncovered a realm that demands deep attention to edge cases that are often overlooked. Unlike traditional REST APIs, GraphQL offers flexibility in querying, but that flexibility brings unique edge case risks: query…
Integrating hybrid cloud environments into enterprise systems has its unique challenges. Edge case testing in hybrid cloud, with its split between public and private infrastructure, is no longer a nice-to-have; it’s a necessity. When you’re dealing with network partitions, data…
Automating test data generation has always been a goal for QA teams, especially for edge cases. Edge cases—those elusive, hard-to-catch conditions that test the boundaries of a system—are notoriously challenging to account for. In recent years, machine learning has been…
Negative testing is often what distinguishes a secure, resilient software from one vulnerable to the unexpected. And if you’re trying to break your app to find all those hidden “what if” scenarios, then synthetic data generation is your best friend.…
When testing large-scale systems, especially in today’s microservices-heavy architecture, managing test data becomes one of the most overlooked challenges. You see, it’s easy to say, “Let’s create realistic test data sets.” But the real challenge is creating data that scales,…
Negative test cases that probe the limits of what an application can handle are among the most valuable tests we can conduct in software development. But while testing with invalid and corrupt data may seem straightforward, it’s an art form…
Parameterized testing has become a go-to method for achieving most of the test coverage. Yet, like all methodologies, it’s not without its nuances. In this article, let’s explore how parameterized data generation can shape our approach to negative testing edge…
Test environments often mirror production settings to ensure the highest accuracy during testing. But using real production data, especially when it includes sensitive information, presents a dilemma: how do we replicate real-world conditions without exposing private data? Here, data masking…
Testing software is as much about pushing boundaries as it is about verifying intended behaviors. With mutation testing applied to test data, we shift from verifying software functionality to scrutinizing its robustness under unexpected data conditions. This approach is particularly…
In the rush of CI/CD automation, managing test data for every release cycle often feels like juggling without a safety net. If you’ve been a tester or developer in the thick of it, you know exactly what I mean! At…
In software testing, we often focus on testing our systems with “known” conditions i.e., valid inputs, typical scenarios, and expected flows. But what happens when your system encounters something truly unpredictable? This is where fuzz testing steps in, adding an…
