Software Testing Magazine

A Release Review Checklist for Teams Where One Developer Does Most of the Work

Software Testing Magazine — Thu, 29 Oct 2026 14:45:48 +0000

There are many situations in software development where a single software developer is in charge of building or maintaining an application. How do you do code review in this case? In this article Niral Dhameliya suggests that a senior developer perform the release review process with a checklist can be finished in under an hour. Author: Niral Dhameliya, Partner and the CTO of Empiric Infotech LLP, https://empiricinfotech.com Many software products are built by very small teams. A startup hires one developer for its first app, a company extends its team with a single remote engineer, or a product moves into maintenance with one person doing most of the work. In each case, the usual safety net of a large team is missing: there is no second developer reading every change and no dedicated tester running every build. The fix is not to hire a full QA team. It is to put a second, senior pair of eyes on every release, and to make that review fast enough that nobody is tempted to skip it. This article describes a release review that a team lead can run in under an hour, the checklist behind it, and the habits that keep it practical. Why the developer’s own testing is not enough A developer who wrote a feature tests it the way they imagined it being used. That covers the happy path and the cases they thought of while coding. It rarely covers the cases they didn’t think of, because those are, by [...]

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Open Source Bug Tracking Tools

Software Testing Magazine — Tue, 27 Oct 2026 14:00:39 +0000

Bugs, issue, defect, there are many names to define the problems you have with software, either during the code development phase or when the application is already in production. Bug tracking tools are an integrated part of a software quality and software testing strategy and there are many open source tools available. Depending on your needs, you don’t need however to spend a lot of money to use a bug tracking system as there are many open source bug trackers that have many features, a good reputation and a long history. This article lists the main open source bug tracking tools available today. Use our contact form to make us aware of some open source defect tracking software that we should add to this list. Here are some features that you could find in open source bug tracking tool: Is it a web-based or a local bug tracking tool that you can install on a local computer Can you define and manage your own bug workflow Can you add to the bug data some custom fields for bug triage Is there a system to notify people concerned by a bug about its life-cycle What reports and statistics are available Can you take screenshots of the application when you report a bug What are the file attachment and sharing capabilities * Updates October 27 2026 Added: Budoco, BugPilot, phpBugTracker, Ticketsmith July 16 2025 Added: BugTrackly, Debugsy, Ticket Tracker February 2 2024: Added: git-bug, lavagna Renamed: The Bug Genie renamed Pachno Budoco Budoco is [...]

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Choosing a Custom Business Software Development Company: A Testing and Quality Evaluation Framework

Software Testing Magazine — Mon, 26 Oct 2026 15:58:15 +0000

Vendor selection for custom software tends to follow a familiar pattern. A shortlist gets built from search results and referrals. Each vendor gives a polished presentation. Proposals arrive with different scopes, different assumptions, and prices that vary by a factor of three. Then someone has to choose. The problem isn’t a lack of options. It’s that most selection processes compare the wrong things — presentation quality and headline price — while the factors that actually predict success stay hidden until months into the project. This framework is designed to surface those factors before you sign with a custom business software development company. Start With Your Own Clarity Before evaluating anyone, write down three things: The business problem, in business terms. Not “we need a portal,” but “our customers call support 400 times a month to check order status, and each call costs us time we don’t have.” What success looks like in 12 months. Measurable where possible: hours saved, errors reduced, revenue enabled, processes retired. Your constraints. Budget range, deadlines that genuinely can’t move, systems the software must integrate with, compliance requirements. Vendors respond to what you give them. A vague brief produces proposals that can’t be compared, because each vendor fills the gaps with different assumptions. The Evaluation Framework Score each shortlisted vendor across six dimensions. Weight them according to what matters most for your project. Dimension What You’re Assessing How to Assess It Business understanding Do they grasp the problem, not just the requirements? Listen to their questions [...]

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Why AI Models Pass Benchmarks but Fail in Production

Software Testing Magazine — Mon, 19 Oct 2026 14:52:37 +0000

There are many cases where AI models that worked fine in development start to behave badly in production. In this article, Kanika Vatsyayan discusses the causes that lead to this situation. He recommends moving from static assessments to continuous quality engineering for AI models. Author: Kanika Vatsyayan, BugRaptors, https://www.bugraptors.com/ Academic evaluation suites celebrate clean milestones: an 88% score on MMLU, top-percentile GSM8K reasoning accuracy, and flawless Python generation on HumanEval. Engineering signs off, release candidates, clear staging checks, and the model goes live. Within weeks, runtime defects the surface. Support systems hallucinate unauthorized return policies; copilots emit malformed API payloads that freeze downstream services, and retrieval engines confidently serve outdated documentation. These models did not degrade; they operate as designed. The root cause is a fundamental mismatch: static AI benchmark testing measures isolated capabilities in a sterile environment, while live applications demand continuous, full-lifecycle AI model testing across distributed, stateful software. What Causes AI Models to Break in Real Environments? Traditional enterprise software breaks on explicit syntax errors, unhandled exceptions, or database query timeouts. Machine learning applications can fail silently while executing clean code, returning valid HTTP 200 responses and passing simple string-formatting checks. When probabilistic models interface with stateful enterprise architectures, runtime failures typically emerge across four distinct operational surfaces: Fabricated Business Logic: Large language models can generate ungrounded claims with great mathematical certainty, including false warranty exceptions, discounts, or transaction records. Orchestration and Tool Errors: An agent may pick the proper business action but fail downstream execution by [...]

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Testing Random Video Chat Apps: Matching, WebRTC and Load

Software Testing Magazine — Fri, 16 Oct 2026 14:22:44 +0000

Random-matching video chat looks simple from the outside: press a button, meet a stranger, press it again. Underneath, every session needs a queue, a pairing decision, a signaling exchange, an ICE (Interactive Connectivity Establishment) negotiation and a media path that survives whatever network each person happens to be on. Each hand-off between those stages is a place for a defect to hide. It is tempting to start with the WebRTC layer and leave the matching logic to code review. That order postpones the failures users meet first: long waits, the same partner twice in a row, and a video pane that stays black after a successful match. The sections below follow a session in order and name the open-source tools that cover each stage. Matching and queue behavior Treat the matcher as a small distributed system and test its invariants. A user must never sit in two pairs at once, a second browser tab must never match a user with themselves, and a skip pressed during the handshake must release both sides cleanly. Run these checks at the protocol level with scripted clients, before any browser is involved. Measure what users feel as well: time to first match at different queue depths, the rate of repeat partners after a skip, and behavior when the queue holds a single waiting user. A QA lead who has been asked to benchmark an alternative to JerkRoulette against the current product needs those figures measured the same way on both sides, with the same [...]

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ServiceNow’s Autonomous Engineer Puts AI in the Driver’s Seat — With Humans at the Guardrails

Software Testing Magazine — Wed, 14 Oct 2026 13:18:26 +0000

Paulius Pazdrazdys, a longtime observer of automated consumer-facing tech platforms, sees a clear pattern in how consequential automation tends to land in practice. Autonomous Engineer, ServiceNow’s newly announced offering, encodes that pattern directly: the system executes autonomous planning, building, and testing of implementation work, yet developers retain control of critical decisions before anything reaches production. For Pazdrazdys, that governance-by-design is not a novelty specific to enterprise software. Wherever algorithms drive outputs that people act on, independent validation habits form around them. He notes the same dynamic in sports-prediction services, where StakeHunters expert tips function as exactly that kind of outside check, scrutinizing automated outputs before anyone acts on them. Autonomous systems that make consequential calls without a human checkpoint are the exception, not the norm. What ServiceNow has done with Autonomous Engineer is bake the validation layer into the architecture itself — that is the governance principle that makes the difference between automation people trust and automation people fear. ServiceNow Unveils Two New AI Products in Mumbai ServiceNow announced AI Workflow Factory and Autonomous Engineer at its World Forum in Mumbai on October 6, 2026, according to Daily Excelsior. The two offerings represent the company’s most direct attempt to help enterprises move from AI experimentation to operational deployment at scale. The problem both products target is structural. Fragmented, legacy infrastructure creates execution gaps that prevent organisations from realising returns on AI investment already in place. AI Workflow Factory addresses this at the organisational level, helping enterprises identify where AI can [...]

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7 Effective Ways Software Testers Can Retain Technical Knowledge and Testing Concepts

Software Testing Magazine — Tue, 13 Oct 2026 13:12:34 +0000

Software testing moves fast. One day you’re mastering a new automation framework. The next? You’re deep in the weeds of complex API status codes or security concepts, staring at a terminal screen late into the evening. The sheer volume of technical information can feel like a firehose. Reading through dense documentation or skimming tutorials helps in the moment, sure. But retaining that knowledge for months or years down the line is a completely different challenge. Have you ever found yourself searching for the exact same syntax or command for the third time in a single week? I guess we have all been there at some point, staring at a blank prompt wondering where that solution went. You’re definitely not alone. Information retention isn’t automatic. It’s a skill that requires real intent. Honestly, it takes deliberate effort to build memory that lasts. So, how do we actually make things stick without losing our minds? Here are seven effective strategies software testers can use to keep technical concepts fresh and accessible. Use Active Recall Instead of Re-reading Testing Documentation It’s easy to open a testing guide, read through it, and feel like you understand the material. But passive reading creates an illusion of competence. You recognize the information while it’s right there on the page, but what happens when you step away? Your brain struggles to retrieve it. And that’s where active recall changes the game. You know, it forces your brain to actually pull its weight. Instead of reading the same [...]

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Klarent Gets $8 Million Funding to Scale Agentic Software Testing

Software Testing Magazine — Mon, 12 Oct 2026 15:53:02 +0000

Zurich-based Klarent hasraised €7.13M seed money to scale its AI software testing platform and expand into the US. The round is led by Mosaic Ventures, with participation from Moonfire Ventures and a group of prominent angel investors including former Google Director Jürgen Galler. Klarent was founded in 2023 by two ex-Google engineers, Asheem Panakkat, who serves as CEO, and Momchil Ivanov, who serves as CTO. Klarent is an autonomous system of QA agents designed for the era of AI-generated software. The funding will be used by Klarent to support its entry into the United States, building on the momentum seen with its European enterprise customers. The company also plans to grow its engineering team and broaden its cross-platform testing capabilities across web and mobile. Klarent claims to provide autonomous testing for Web, iOS & Android. The platform writes, runs and maintains your tests, so software development team can ship faster, without sacrificing quality. Klarent introduces autonomous agents that use LLMs only to author and adapt deterministic test code. This deterministic approach ensures that tests run reliably and repeatably, delivering the speed of AI with the precision and cost-efficiency of production engineering. The platform is backed by a human-in-the-loop service that verifies every result, so customers only ever pay for outcomes.

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