APIs, or Application Programming Interfaces, are the connectors that allow different software systems to communicate. They establish a standardized communication protocol for sending and receiving data, ensuring consistency across platforms. The OpenAPI Specification (OAS) is a popular format used to document these APIs in a machine-readable way (typically in JSON or YAML). Because it’s both comprehensive and machine-readable, OAS powers many automation scenarios in API testing, acting as a blueprint for test generation. 

What is the first thing that crosses your mind when you see the report that a test has failed in the automated run?

• “O-o, we have a bug here,” or
• “What is wrong with this test case now?”

The European Union has just rolled out the InvestAI initiative, a colossal endeavor to rally €200 billion for AI investment. Think of it as Europe's grand plan to become the superstar of AI development and application, sparking innovation and competitiveness across a multitude of industries. But what does this mean for companies in Europe? Let's dive into the potential impact and opportunities that InvestAI brings to the table.

In software development, resolving bugs quickly is a priority. However, the "quick fix" approach often addresses symptoms rather than the actual causes, potentially leading to recurring issues.

QA professionals play a critical role in Root Cause Analysis (RCA) by going beyond testing to understand the reasons behind defects. Unlike developers, who focus on debugging code and fixing technical failures, QA takes a broader view, considering system-wide behaviors and user perspectives.

At System Verification, we are deeply committed to staying at the forefront of technological advancements and delivering the highest quality services to our clients. One of our key contributors to this mission is Dr. Adha Hrusto, an AI Solutions Engineer whose expertise has significantly enhanced our capabilities in quality assurance and software testing.

Development projects often face hidden costs that can derail budgets and timelines, a challenge prevalent not only in the financial services industry but also in sectors such as technology, healthcare, and manufacturing. Identifying and mitigating these inefficiencies is crucial for project success.

Have you ever wondered how a simple blood test can reveal so much about your health?
Healthcare is undergoing a transformative shift, driven by technological innovation. However, challenges persist—healthcare organizations face increasing demands, staff shortages, and mounting administrative workloads for doctors and nurses.

The healthcare industry is on the cusp of a technological revolution, spearheaded by artificial intelligence (AI). These advancements promise groundbreaking developments, including improved healthcare delivery, innovative research, and better health outcomes for all.

Synthetic v.s. anonymized data: what’s the difference? And which is better for your software testing needs?

In the software development lifecycle, testing with reliable and secure data is essential for ensuring quality and compliance. While actual production data may seem like the most natural choice, using it directly when testing comes with significant risks. Production data often contains sensitive information—such as customer details or transaction records—which could lead to privacy breaches and regulatory violations if accidentally exposed. There's also always a risk that actual production data can cause inconsistencies or conflicts when applied across different testing scenarios, potentially impacting test results and reducing repeatability.

Automated test generation has become a critical aspect of modern software development. As software systems grow in complexity, writing exhaustive tests manually becomes time-consuming, prone to errors, and can delay release cycles. Automated unit test generation helps developers create reliable, well-tested code efficiency, saving time and improving code quality.