Engineering and quality-assurance posts from the Betica team — written by the people doing the work, not the marketing department.
In this article, we discuss how you can successfully integrate exploratory testing into your existing scripted test suite.
In this article, we will discuss the science behind exploratory testing, explaining why human intuition and flexible thinking are crucial in software quality assurance.
As AI systems become more prevalent and influential in our daily lives, ensuring their transparency and accountability becomes increasingly crucial.
This article explores strategies for creating an effective hybrid QA approach that maximizes the benefits of both manual and automated testing.
In Agile environments, change is constant. This article explores strategies for QA teams to not just cope with, but thrive in the face of rapidly evolving requirements and frequent changes.
As artificial intelligence becomes increasingly central to our economy and society, a new form of literacy is emerging as crucial: AI literacy.
The transformative power of AI is not limited to high-tech sectors; it's also revolutionizing traditional industries like agriculture and manufacturing.
The global landscape of AI development is not limited to traditional tech hubs in the West. Emerging economies, particularly the BRICS nations (Brazil, Russia, India, China, and South Africa), are playing an increasingly significant role in shaping the future of AI technology.
As artificial intelligence becomes increasingly prevalent in our daily lives, the ethical implications of its development and deployment have come to the forefront of public discourse.
A common frustration in the QA world is the perceived lack of shared responsibility for quality. This article explores strategies to break down silos and foster genuine collaboration between development and QA teams.
While tech giants often dominate headlines in AI innovation, small businesses are proving to be nimble and effective adopters of AI technology.
One of the most persistent challenges in QA is the struggle with test environments. As one QA professional put it, "Having the proper testing environments and assets setup" is a constant battle. This article explores strategies for creating and maintaining robust QA infrastructure that supports efficient and effective testing.
As artificial intelligence and robotics continue to automate jobs across various sectors, policymakers and economists are exploring potential solutions to address technological unemployment. One idea gaining traction is Universal Basic Income (UBI), a system where all citizens receive a standard amount of money regardless of their employment status.
One of the most frustrating challenges in QA is working with vague or incomplete requirements. We discuss mitigation actions in this article.
The manufacturing industry is on the brink of a revolution, and at the forefront of this change is Tesla's ambitious Optimus robot project.
As artificial intelligence and automation reshape the job market, the need for widespread reskilling and upskilling has become increasingly urgent. Both governments and corporations are taking steps to prepare workers for this new economic reality, as highlighted in Alexander Mann's paper "Adaptive Equilibrium in the Age of Artificial Intelligence."
The integration of Artificial Intelligence in healthcare is not just a futuristic concept—it's happening now, and it's revolutionizing patient care. From diagnostic tools to personalized treatment plans, AI is transforming the medical field in unprecedented ways.
In this post, we discuss practical strategies for managing time effectively in QA, ensuring quality isn't compromised despite tight deadlines.
Quality Assurance (QA) teams often find themselves caught in a challenging predicament: the pressure to automate everything while struggling to find the time to do so
As artificial intelligence reshapes our economic landscape, traditional economic theories struggle to keep pace.
AI-assisted coding tools promise to boost developer productivity and streamline the coding process. However, they also present unique challenges for quality assurance (QA) teams. This article explores some of the most prominent AI coding tools at this time, and their implications for QA processes.
In 1776, Adam Smith introduced the concept of the "invisible hand" in his seminal work "An Inquiry into the Nature and Causes of the Wealth of Nations." This metaphor for self-regulating markets has guided economic thinking for centuries. However, as we enter the age of Artificial Intelligence (AI), Smith's theories are being challenged and adapted.
As AI-driven tools like GitHub Copilot and ChatGPT become increasingly integrated into development processes, quality assurance (QA) teams face new challenges and opportunities. This article explores the impact of AI on QA and how teams can adapt to ensure software quality in this evolving environment.
What happens when, after a test phase, few or no bugs are found? In this post, we discuss the realities of software testing.
Crowdtesting is revolutionizing software development by leveraging a global network of testers to evaluate software in real-world conditions, offering wider testing scope and diverse perspectives.
Memory overcommit in Linux allows processes to allocate more memory than physically available, which can lead to efficient resource use for many applications but poses significant risks for PostgreSQL, potentially causing database crashes and data corruption if not properly managed.
On July 19 2024, an incident involving CrowdStrike, a leading provider of antivirus software, has brought to light the critical importance of robust software testing and quality assurance practices. This blog post discussesthe details of the incident, explores the steep price of poor quality in software, and emphasizes the value of proper quality assurance in the cybersecurity industry.
Barman and pgBackRest are two popular Postgresql bakcup options that offer robust features for efficient backups. In this post we will discuss the pros and cons of both of them.
Enter Bitcoin SV (BSV), a fork of Bitcoin that aims to fulfill the original vision of Bitcoin as outlined by its creator, Satoshi Nakamoto. BSV's primary objective is to massively scale the Bitcoin blockchain to accommodate higher transaction volumes and increased adoption.
Explore the face-off between ChatGPT and Llama 2, as we dive into performance, creativity, and safety features. Discover which language model best suits your specific needs in our comprehensive guide.
In this post, we will explore the role of AI in automation testing, the benefits it brings to the table, and an in-depth analysis of the top 5 AI-powered test automation tools in 2023. Let's dive in and discover how AI is revolutionizing software testing.
This article delves into the essence of the WLA, the role of the WLA-SCS, and the crucial interplay between the WLA-SCS and the widely recognized ISO 27001 information security standard.
In this blog post, we will explore how LlamaIndex can ingest data from various sources, including PDF, CSV, databases, and APIs, and how to leverage this custom knowledge to create AI applications tailored to your unique domain.
In this blog post, we will explore the benefits of AI-powered automated unit testing tools and their role in revolutionizing software development. We will also discuss some popular tools in this domain, their features, and how they can help developers build robust, reliable software.
This post explores the evolution of Information Technology (IT) in the last decade and the impact of Artificial Intelligence (AI) in both business and consumer settings. It also discusses the potential impact of ChatGPT, a large language model, on IT and business, including its use in customer service, data analysis, automation, and personalization.
What to do when a testing phase is coming, and there is no documentation of any kind whatsoever? In this post, we discuss this very common scenario.
A new trend is emerging in Java programming - Reactive Programming with Java Project Loom. Find out more about it with us!
In this post, we discuss some effective software choices for your projects.
In this article, we'll explore inspiring examples of software development projects.
We explore how Artificial Intelligence (AI) and Machine Learning (ML) can enhance Quality Assurance (QA) productivity. We examine how AI-supported test automation processes can augment test coverage and increase overall quality by analyzing different data types. Additionally, we discuss how improved accuracy in analyzing data allows ML to better detect the origin of software failures and expedite the fixing process. Lastly, we examine how AI and ML can improve regression testing by accelerating testing time and improving automated tests’ resilience to any alterations.
This blog post discusses how machine learning and AI can assist quality assurance. By utilizing these technologies, businesses can increase efficiency, reduce costs, and improve overall product quality. Quality assurance experts can leverage machine learning and AI to automate testing processes, identify patterns, and analyze large amounts of data.