Cloud Vendor Lock-In: How to Stay Flexible Across AWS, Azure, and GCP
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Think back on a time when software was released after long, drawn-out waits. From 2007 to 2016, Microsoft released versions of Office every three years. Now, software releases are fast and frequent. But are they better? Those three-year gaps allowed Microsoft to painstakingly QA test their software for months, if not years. However, Office has now gone the path of other apps, which seem to release new versions weekly, if not more often.
If there's a bug in the code, the solution seems to be simply rolling out another version and hoping it fixes it (and doesn't break anything else). But in this fast-paced SDLC world, organizations must make time for QA testing as part of the software pipeline.
If your organization would like to test the validity or build quality of a new product or a significant redesign before it hits the market, consider a beta release. Invite fellow coders and power users to test a piece of software while it's still in the pipeline in order to get UX, UI and general feedback on the product.
This seems to be a logical option, but for many developers, this is easier said than done. It can be easy to say, "Let's just add this one more item before we beta test this," which quickly turns into "Wait, what if we added these three other items?" Planning a beta test following a specific sprint is incredibly important at the onset of the project's roadmap. Don't think that it will happen organically — it probably won't.
With many organizations investing significant time and resources into digital transformations, automation and artificial intelligence are no longer "the next big thing" — because they're here. Even simple apps incorporate predictive technology and predictive algorithms. As a result, testing must follow suit. This is where AI can assist testing in the software pipeline.
AI and predictive technology can run tests far faster than any single person or team could, finding software flaws in minutes rather than days, weeks or months. Exposing these flaws quickly can help developers resolve them before the next iteration, ensuring weekly updates go out with far fewer flaws. This allows the software pipeline to move quickly and smoothly without requiring teams to fix what's broken before moving on to new implementations.
