QASource Blog

Legacy code is a common challenge many organizations face when dealing with outdated software systems. As technology evolves rapidly, businesses must keep their tech stacks up to date to remain competitive. Converting codes to a modern tech stack can be complex and time-consuming, but AI makes it easy and efficient. This blog covers how AI can streamline the legacy code conversion to the latest tech stack, providing numerous advantages such as efficiency, accuracy, savings, and risk reduction.

Voice recognition and voice assistants have become central to the software as a service (SaaS) industry, changing how we interact with SaaS and promising benefits for businesses and users alike. But if you plan to offer a voice assistant or other voice recognition tool, it’s important to thoroughly test your offering before bringing it to market.

Analysts predict AI's market size in software testing will reach an impressive $4 billion by 2024. But the impact of all this AI is tied to the precision and robustness of the underlying models.

Software testing is vital because it allows you to test if an application is working properly. There are various types of tests that you can perform if you work in the software industry. Nevertheless, all these tests fall under one of these two classifications: positive and negative testing. Both categories are equally important, but positive testing is more popular than negative testing.

Tech debt from trade-offs during software development can result in substantial costs for your business. A survey conducted across 200 tech companies disclosed that an average of 25-40% of a developer's time is spent addressing tech debt. And 79% of tech leaders cited tech debt as a significant hurdle in achieving their business objectives, signifying the gravity of this issue. But AI is a game-changer when it comes to reducing it.

Web3 is the third generation of the internet currently being built, where websites and apps will be able to process information in a smart human-like way through technologies like artificial intelligence (AI), machine learning (ML), Big Data, decentralized ledger technology (DLT), and more.

Testing remains a cornerstone for delivering impeccable products in the dynamic world of software development. With AI's emergence in this domain, there's a buzz about its capabilities. But the question remains: Can AI-driven testing truly match the discernment of a seasoned software tester? Let's dive deeper.

In today's digital age, eLearning platforms have become an integral part of education, which offers flexible and accessible learning opportunities to students of all ages. Ensuring their quality is important with the increasing demand for eLearning applications. Quality assurance guarantees that eLearning platforms deliver a seamless and enriching learning experience. Strategies for effective QA on eLearning platforms involve a comprehensive approach encompassing various aspects, including functionality, usability, accessibility, and security.

Gone are the days when skill-set availability and language compatibility were challenging when working with a nearshore partner. The situation has improved, and many companies are embracing offshore and nearshore testing. What is it about nearshore QA testing that makes it so appealing?