A Guide To Software Testing

Software testing is a well known practice in which development teams investigate whether or not the software they’ve built actually does what it is supposed to do, and does not contain any errors that will affect the performance or security of the application. With the ongoing pressure to deliver software faster than ever before, software testing has become even more imperative to a business’ success.

There are many different approaches teams can take for software testing — automated testing, A/B testing, continuous testingtest-driven development and QA testing to name a few. In addition, there are many different areas and stages within the software that teams need to worry about.

The two main focuses associated with software testing are verification and validation. Verification ensures the software is working as specified, and validation ensures the software satisfies the requirements it was intended for. As software continues to be an important part of the business, testing will need to shift left in the lifecycle and be applied throughout the development process earlier and more often.

Parasoft’s latest release offers several new automated features for testing Java, C#, .NET apps

Parasoft recently released the 2024.1 releases of several of its products, including Java testing tool Jtest, C# and .NET testing tool dotTEST, and testing analytics solution DTP.  Jtest now includes test templates in Unit Test Assistant, which is a feature that uses AI to generate a suite of tests. With the new Jtest release, testers … continue reading

Parasoft offers new capabilities for API, microservices, and accessibility testing in latest release

The software testing company Parasoft has announced new updates for API, microservices, and accessibility testing. For API testing, the company is using AI to offer auto-parameterization of API scenario tests generated by the OpenAI integration.  According to Parasoft, this update will streamline the process of developing test scenarios that validate data flow.  In the realm … continue reading

Mabl now offers automated mobile testing

The testing company mabl has announced that it now offers automated mobile testing capabilities in its platform, which already offered testing for web and APIs.  It was designed to give full coverage of all the unique functionalities of varying mobile devices and their operating systems. With this new new offering, tests are created through a … continue reading

Tricentis announces series of AI Copilots for its testing portfolio, starting with Testim Copilot

The testing company Tricentis has just announced the first in a series of AI copilots for its testing portfolio. The first solution is Testim Copilot, which adds AI capabilities to the automated testing platform Testim.  With Testim Copilot, users can input a text description of what they want to test and receive the JavaScript code … continue reading

The power of automation and AI in testing environments

Software testing is a critical aspect of the SDLC, but constraints on time and resources can cause software companies to treat testing as an afterthought, rather than a linchpin in product quality. The primary challenge in the field of testing is the scarcity of talent and expertise, particularly in automation testing, according to Nilesh Patel, … continue reading

Report: How mobile testing strategies are embracing AI

AI has seeped into every corner of the tech space over the last couple of years, and mobile testing is no exception.  Tricentis just published its State of Mobile Application Quality Report 2024, where it found that 48% of testing professionals said that AI is already part of their mobile testing strategy. A further 21% … continue reading

SmartBear adds distributed tracing, developer API portal, and more in latest update

The testing company SmartBear has announced updates to three of its products, with the goal of improving visibility into the software development life cycle. “We continue to put our customers at the center of our strategies and deliver on their needs by expanding our product portfolio through innovative enhancements to our popular solutions used by … continue reading


DevOps success starts with quality engineering

Studies show that DevOps adoption is still a moving target for the vast majority of software development teams, with just 11% reporting full DevOps maturity in 2022. Navigating this transition requires organization-wide metrics that help everyone understand their role. To that end, Google developed the DORA (DevOps Research and Assessment) metrics to give development teams … continue reading

Buyers Guide: AI and the evolution of test automation

Test automation has undergone quite an evolution in the decades since it first became possible.  Yet despite the obvious benefits, the digitalization of the software development industry has created some new challenges. It comes down to three big things, according to Kevin Parker, vice president of product at Appvance. The first is velocity and how … continue reading

A guide to automated testing tools

The following is a listing of automated testing tool providers, along with a brief description of their offerings. FEATURED PROVIDERS APPVANCE is the leader in generative AI for Software Quality.  Its premier product AIQ is an AI-native, unified software quality platform that delivers unprecedented levels of productivity to accelerate digital transformation in the enterprise.   Leveraging generative … continue reading

Take advantage of AI-augmented software testing

The artificial intelligence-augmented software-testing market continues to rapidly evolve. As applications become increasingly complex, AI-augmented testing plays a critical role in helping teams deliver high-quality applications at speed.  By 2027, 80% of enterprises will have integrated AI-augmented testing tools into their software engineering toolchain, which is a significant increase from 10% in 2022, according to … continue reading

Training the models for testing

Code coverage and end-to-end testing – sometimes called path testing – are particularly well-suited for automation, but they’re only as good as the training and implementation. Since AI doesn’t have an imagination, it is up to the model and whoever is feeding in that data to cover as many paths as you can in an … continue reading

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