Software Testing Magazine

How to Test Location-Based Software: A Practical QA Guide

Software Testing Magazine — Tue, 15 Sep 2026 14:04:44 +0000

A customer appears on the map, the address looks correct, and the page loads without errors. The test passes. But what happens when that customer sits exactly between two service areas? Which team receives the account? Does the answer change after someone edits a boundary? These are the questions a location-based testing plan should answer. Checking whether a map displays correctly is only the beginning. Quality assurance also needs to verify the data, rules, permissions, and system behavior behind it. The goal is straightforward: confirm that the software makes the right decision when location information is accurate, uncertain, unavailable, or changing. Start With the Decisions That Depend on Location Before choosing a

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Your AI Doesn’t Know What Is “Correct”

Software Testing Magazine — Mon, 14 Sep 2026 16:50:01 +0000

AI-assisted code generation has changed the capabilities of organizations to produce code. In this article, Hélder Ferreira discusses the impact on software quality created by this new situation. The main problem is that this new code is often created by tools that lack the knowledge of the global context of both the application infrastructure and the process being automated. Using functional tests as a baseline might help to limit the risks. Author: Hélder Ferreira, Director of Product Management, Sembi, https://www.sembi.com/ Everyone knows that AI “speeds” up development. Recent research found that developers using AI tools increased their coding activity, measured by commits, by as much as 180%. But producing more code doesn’t necessarily mean better software: AI-generated code often introduces errors that leave developers picking up the pieces. AI-generated code forces software developers to rework and debug the code that “only took a few seconds” to create. What’s missing and keeping AI from accurately producing and shipping code is a written, testable record of how the product should behave. Much of this context lives in disconnected tools or internal knowledge, leaving AI to guess at a developer’s intent. This is where test artifacts help AI-assisted development. Test context breaks down detailed user flows written in step by step detail. Unlike requirements or other dev artifacts that show a snapshot of how features were once built, tests capture your product’s truest expected behavior and your regression suite aggregates the cumulative and latest expected behavior of your product functionality including the multitude [...]

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Qyrus Rebrands Itself to QyrusAI

Software Testing Magazine — Mon, 14 Sep 2026 13:30:40 +0000

Qyrus is transforming into QyrusAI, a single AI platform that unites application modernization, software testing and autonomous IT operations. QyrusAI brings together Qyrus, the company testing platform, and Qinfinite, its AI-driven modernization and IT operations platform, under one name and one shared model of the enterprise application condition. QyrusAI is built to provide three connected workspaces: Qyrus Modernize, Qyrus Assure and Qyrus Operate. They share one enterprise Knowledge Graph, a continuously updated operational memory the company calls Hive, and an agentic orchestration layer, SEER, that coordinates work across all three. “Enterprises don’t have a testing problem, a modernization problem and an operations problem in silos. They just have one problem: they can’t see their own condition clearly enough to change it safely,” said Anil Kumar, Founder and Chief Executive Officer of QyrusAI. “Every migration, every test and every resolved incident now writes back into the same knowledge graph. The system gets safer to change the more you change it.” The platform is built for regulated industries, with SOC 2 Type II certification and controls aligned to GDPR and PCI-DSS, and is designed with the audit and evidence requirements common in financial services, healthcare, and the public sector in mind.

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12 Essentials for QA Work While Abroad

Software Testing Magazine — Mon, 14 Sep 2026 13:13:37 +0000

QA work abroad runs on more than a laptop and a Wi-Fi signal. It depends on secure testing environments, reliable device access, protected data, and systems that keep collaboration steady across time zones. Build that foundation before you travel, and testing quality holds up no matter where you’re working from. 1. Device-Cloud Access Testing apps across multiple devices gets hard when you’re traveling light. Device-cloud platforms solve that by giving you remote access to phones, tablets, browsers, and operating systems without the hardware in your bag. In practice, it typically covers: Real-device testing Multiple operating systems Browser coverage Automated testing support Broader device coverage travels with you without adding bag weight. A hardware-specific bug also stays reproducible this way, even when the actual device isn’t within reach. 2. Expat Tax and FBAR Tools U.S. citizens working overseas usually keep U.S. filing obligations. Foreign accounts can add reporting requirements on top of that. A dedicated tool can help keep track of: Expat tax filings Foreign income records FBAR requirements Supporting financial information A tool such as Expatfile keeps these obligations out of the daily testing workflow. Records kept current through that same tool, all year, are what make filing simple instead of a scramble. 3. Data Masking QA work often touches data that resembles real customer or business records. Testing against that data unmasked exposes sensitive details for no good reason. Masking typically covers: Customer information Account details Contact information Transaction records Masked data keeps the structure a workflow needs without [...]

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6 Best Code-Level Forensics Tools for Production Failures in 2026

Software Testing Magazine — Sat, 12 Sep 2026 15:06:04 +0000

Production failures rarely arrive with a clean explanation. A release looks healthy in staging, then fails under real traffic. A background job works for most customers, then breaks for one segment. A service slows down after a deployment, but only when a specific dependency, payload shape, feature flag, or account path appears. A stack trace points to one function, while the real cause sits three calls earlier. Logs show symptoms, traces show timing, and dashboards show impact, but engineers still need to reconstruct what the code actually did. That is why code-level forensics tools have become an important support for engineering teams. Key Takeaways Code-level forensics goes deeper than alerts, dashboards, and generic log search. The strongest tools connect production failures to code paths, functions, commits, traces, runtime values, deployments, and ownership. Hud leads this list because it was built around production runtime intelligence for both engineers and AI coding agents. Traditional observability platforms remain valuable, but code-level forensics requires stronger links between runtime behavior and source-level context. The Best 6 Code-Level Forensics Tools for Production Failures 1. Hud Hud is the best code-level forensics tool for production failures in 2026 because it is built specifically to connect real production behavior back to code-level fixes. That focus matters. Many observability platforms were designed for human operators watching systems. Hud is designed for a newer workflow where engineers and AI coding agents need structured runtime intelligence from production. Its site positions Hud as a runtime layer that runs with code in [...]

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How to Test Security Software for Scalability and Performance

Software Testing Magazine — Fri, 11 Sep 2026 16:31:51 +0000

Security software that works perfectly in a controlled setting can quickly fall apart when used at scale. As organizations grow, their security solutions need to handle a huge increase in data, users, and connected devices. This puts a lot of pressure on software development and quality assurance teams. They have to make sure security platforms don’t just work, but can also scale, stay resilient, and perform reliably under stress. Without a strong testing strategy focused on scalability, a solution meant to protect can actually become a weak point. Growth Demands Scalable Software A security system that can’t grow with an organization becomes a problem. Scalability isn’t just about managing more users. It’s about handling more data, faster, and in larger volumes, without performance dropping. For example, a small office might have a dozen security cameras, but a corporate campus or a city-wide surveillance system could have thousands. The software needs to take in, process, and analyze these data streams in real time. If the system isn’t built to scale, users will see slow video feeds, delayed alerts, and system crashes. This makes the solution useless when it’s most needed. Testing proactively for scalability ensures the system can support future growth, whether that means adding new facilities, integrating more sensors, or expanding user access. This is where scalability testing becomes particularly important, allowing teams to evaluate how software behaves as workloads, users, or data volumes increase. Testing Distributed Systems Effectively Modern security platforms are rarely single, unified blocks. They are distributed [...]

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Why Software Teams Lose Productivity to Tool and Environment Sprawl

Software Testing Magazine — Thu, 03 Sep 2026 10:18:58 +0000

When a company starts to blossom, adding tools to the mix for software teams is a typical move. A company may need code editors, test automation tools, cloud space, and updated project management software. But when a software team keeps adding tools without an overarching strategy in place for those tools, they might go overboard. Having an abundance of tech tools isn’t always an advantage. And tools can interfere with how effectively a team functions. Read on to learn more about why software teams and software testers might lose productivity to tool and environment sprawl. Understanding Tool Sprawl Too much tech can lead to a problem known as tool sprawl. Teams may add different subscriptions or platforms, for instance, leading to tools that overlap in their purpose. With so many software testing platforms, cloud services, and other tech tools in the mix, it’s easy to reach a point where managing everything becomes impossible for businesses. Further, when teams aren’t communicating about tech needs or acquisitions, it’s hard to provide consistent oversight. Data may sit in different silos, and every team will use different workflows and organizational structures. Likewise, with environmental sprawl, a lack of control across SaaS environments can lead to security problems and software performance glitches. Ideally, testing and production environments should be unified to create consistency before systems go live. Creating Extra Work Software teams don’t need extra work. But when they’re adding redundant tools and failing to keep a unified system, extra work comes with the territory. [...]

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How to Review Your Traceability Matrix to Strengthen Test Coverage

Software Testing Magazine — Tue, 01 Sep 2026 19:58:10 +0000

A fully green traceability matrix can make it look as though requirements are properly covered. Yet a link between a requirement and a test does not prove that the test actually verifies every required behavior. Requirements coverage gaps can remain when tests are incomplete, when a requirement evolves without its tests following, or when a test changes while its traceability link remains intact. This article proposes a simple way to detect them: first understand how they arise, then structure the review by comparing explicitly required behaviors with what the linked tests actually demonstrate. Author: Benjamin Chollet, KomAInu, https://komainu-ai.com/ Why a Green Matrix Is Not Enough REQUIREMENT The system shall reject inputs longer than 100 characters. LINKED TEST Verify that the input field is displayed and accepts user input. There is nothing inherently wrong with the link: both artifacts concern the same feature. But the test says nothing about the 100-character limit. Traceability tells us that a relationship exists; it does not tell us what the test demonstrates. Here, I use covered to mean that the tests demonstrate the behavior stated in the requirement. That is a stronger claim than saying that one or more tests are attached to it. Figure 1. A traceability link can be valid even when the required behavior is not verified. (click on image to enlarge) How Coverage Gaps Appear Coverage gaps often hide inside tests that are otherwise relevant, useful and passing. Two causes are particularly easy to miss: incomplete verification of the required behavior [...]

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