Spectro Cloud completed a $75 million Series C funding round led by Growth Equity at Goldman Sachs Alternatives with participation from existing Spectro Cloud investors.
In today's digitally driven work environment, leveraging technology to improve efficiencies is an essential component of any modern business. This is especially true for those in the business of software.
As a business's software development life cycle (SDLC) continues to speed up, and more code is developed and deployed at a faster rate, testing that code for quality to ensure optimal user experience is critical. The SDLC is also only growing more complex, so finding ways to simplify and automate wherever possible are critical too.
That's why a modern SDLC should start with software test automation.
Inclusive Automation
Software engineers are well-versed in inclusive or universal design, creating a product that is usable by as many people as possible. This should be applied to software test automation too.
Traditionally, a software developer uses code to script automation. Problems can arise with this approach when testers don't have the technical understanding to maintain these tests or grow the scale of these tests as the software pipeline expands. Starting with inclusive codeless automation solves this challenge by removing the complicated coding part of the process.
Facilitating Automation
Validating software on both web and mobile applications can create unique challenges for software test automation. To avoid issues, it's important to create applications with inclusive automation in mind, including details baked into your code.
■ Every element has a unique identifier. Software test automation should act on these IDs, not something else, such as position on a page in mobile vs. web. Unique identifiers enable automation to act and do its job.
■ Content descriptions are used to explain an element's purpose. This helps distinguish between UI elements. This also needs to be part of standard automation testing.
Identifiers and content descriptions are not optional for developers looking to implement functional and advanced testing automation that doesn't break.
Limits to Software Test Automation
Codeless automation can handle complex situations, but it has its limits. Some tests are still better to be done manually. For example, any tests that involve data from two separate sources (like from APIs, which are very common for apps today), make it difficult to automatically validate. This is because individual apps behave differently. Synchronizing two systems into one for testing is challenging for any type of automation, not just codeless testing.
The Potential of Software Test Automation
Software test automation can empower organizations and their software development. But it isn't always easily embraced or added. One big reason behind this is that developers don't want to stop developing new features to pay down existing technical debt. So areas like refactoring or desiloing are put off.
Performance will eventually suffer if technical debt isn't paid down. In the long run, pausing development progress to implement automation will be worthwhile. Advanced automation planning and strategy should go directly into your SDLC and be a consistent effort to identify app elements and improve automation around them.
Industry News
The Cloud Native Computing Foundation® (CNCF®), which builds sustainable ecosystems for cloud native software, has announced significant momentum around cloud native training and certifications with the addition of three new project-centric certifications and a series of new Platform Engineering-specific certifications:
Red Hat announced the latest version of Red Hat OpenShift AI, its artificial intelligence (AI) and machine learning (ML) platform built on Red Hat OpenShift that enables enterprises to create and deliver AI-enabled applications at scale across the hybrid cloud.
Salesforce announced agentic lifecycle management tools to automate Agentforce testing, prototype agents in secure Sandbox environments, and transparently manage usage at scale.
OpenText™ unveiled Cloud Editions (CE) 24.4, presenting a suite of transformative advancements in Business Cloud, AI, and Technology to empower the future of AI-driven knowledge work.
Red Hat announced new capabilities and enhancements for Red Hat Developer Hub, Red Hat’s enterprise-grade developer portal based on the Backstage project.
Pegasystems announced the availability of new AI-driven legacy discovery capabilities in Pega GenAI Blueprint™ to accelerate the daunting task of modernizing legacy systems that hold organizations back.
Tricentis launched enhanced cloud capabilities for its flagship solution, Tricentis Tosca, bringing enterprise-ready end-to-end test automation to the cloud.
Rafay Systems announced new platform advancements that help enterprises and GPU cloud providers deliver developer-friendly consumption workflows for GPU infrastructure.
Apiiro introduced Code-to-Runtime, a new capability using Apiiro’s deep code analysis (DCA) technology to map software architecture and trace all types of software components including APIs, open source software (OSS), and containers to code owners while enriching it with business impact.
Zesty announced the launch of Kompass, its automated Kubernetes optimization platform.
MacStadium announced the launch of Orka Engine, the latest addition to its Orka product line.
Elastic announced its AI ecosystem to help enterprise developers accelerate building and deploying their Retrieval Augmented Generation (RAG) applications.
Red Hat introduced new capabilities and enhancements for Red Hat OpenShift, a hybrid cloud application platform powered by Kubernetes, as well as the technology preview of Red Hat OpenShift Lightspeed.
Traefik Labs announced API Sandbox as a Service to streamline and accelerate mock API development, and Traefik Proxy v3.2.