The difference was that rather than dealing with each machine individually, we could literally just click a start or script run and do what was required. The automation saved a lot of time and effort.

In a lot of the systems that I test, I don't have a real way to do full end-to-end testing. I don't have a way to set up data nicely, watch it flow through the system, give expected results and get to a final answer, and then clean everything up. It's very piecemeal. That's not necessarily why we got Eggplant. But what I have been able to do with it are a lot of smoke tests, which we didn't have, and several others. There are also some look-up screens where we set up look-up values for other areas, and we have been able to do full testing on those. We have actually found some errors in one application, errors that have gone into the backlog, and we hope to get those fixed so that we can keep building out more tests. It has enabled true end-to-end testing. So far, we've been able to get Eggplant to test anything that we've wanted to test.

Even with the mobile app, which consumes data from a different system, we'll be able to do some full testing within that, which is great.

Our use cases are somewhat unique, because we're not a software development shop. We are testing the pieces that we can and, for that purpose, Eggplant has helped. It has been great.

The model-based test automation has helped to reduce the test maintenance process. Now, I don't have to do a lot of the simple smoke tests and some other testing.

And while I wouldn't say that the solution has helped to uncover critical bugs that normal testing would have completely missed, it certainly has helped us find bugs and then verify that they've been fixed. In one particular instance, it found out that the continuous integration pipeline was broken, or that somebody forgot to push out all of the correct files, because pages weren't working. It pointed out that there was an issue with the deploy model.

This solution has helped our organization because we can create test cases faster as compared to using Selenium. There are no language dependencies and people are not required to be trained for two to five months. People can write test cases using Eggplant Digital Automation Intelligence. Using the solution has improved productivity from using Selenium.

We were performing tests on television set-top boxes manually. It would take a person anywhere from 90 minutes to over three hours to perform a given test. It would require them to sit in front of a computer screen and manually perform the test operations. Not only was it resource-intensive, but it resulted in inaccuracies because the tests require certain channel changes or activities to be performed on a set-top box with particular timings. Since there were humans performing the tests, the timing was not always accurate. And there would be issues with missed button pushes. Eggplant allowed us to replace that human work with an automated solution that reduced the time that it took a person to perform the test from upwards of three hours down to a few minutes. It also increased the accuracy to the levels where we are entirely accurate with our timing and tests. We now have an extremely precise and extremely accurate level of testing.

It enables true end-to-end testing of processes that span a wide range of devices and architectures. We have used it for several different scenarios and devices. We use it for automating processes on our products, our company's products are primarily SaaS-type products, and we're able to develop automation that works on our entire suite of products across all of our implementations.

It has also helped to uncover critical bugs that our normal testing would have completely missed. By performing the automated tests that we do, we've been able to perform many more tests than we would have done manually. By being able to cover that much more ground, the tests have resulted in discovering issues that we likely wouldn't have discovered before. We've been able to perform both a broader and deeper level of tests.

The testing that we're doing with Eggplant is in operations, a post-implementation type of validation. The information that we uncover from our tests is used as a feedback loop to our internal technical teams and to our external data partners, allowing them to investigate and resolve the issues that they may have in their production systems. The results of our tests are used to identify potential issues with our data partners' data. They are also used to evaluate the data that we generate in our systems, and those results are fed back to our teams. They take the results of our automated tests and use them to apply fixes and upgrades to our existing systems. We're able to inform our internal and external partners and, in turn, have them improve and stabilize their platforms.

Everything is happening on the layout or display that is used by the user. Eggplant prompts processes, like "click here," or "look for this image." Eggplant makes it possible for QA people and BAs, working in the actual display, to check if the software is providing the right images, the right text, and the right results. They don't have to go inside the code or to the TCP/IP layer. Everything is happening at the highest level. You don't need much coding knowledge to program what can sometimes be complex processes.

Eggplant works very well when it comes to uncovering bugs. In many cases, it has revealed bugs in our tools and modules. That is great.

It has also helped to quickly stabilize updates. It has improved the whole process in acknowledging the images and texts that are shown. Everything is running even better than before.

In addition, it reduces and even eliminates test processes that might have otherwise been necessary for us to do as part of quality assurance. I didn't have to do those processes, Eggplant was doing them. For example, there was a formula used by doctors every day. For a human to check it would take eight hours, but Eggplant does it in two hours and 20 minutes. It's quicker.

We also get better quality because a human eye, after such long and concentrated work, can't see what could be very important images or very rare results. It just doesn't see them anymore, but Eggplant recognizes them. We had an example where a certain text was not Arial 12 but, instead, Arial 9. Human quality assurance people were not able to recognize that, but Eggplant recognized it and told us that something was wrong. We just activated Eggplant and it did the work for us. Eggplant has influenced our release schedules greatly. It showed us errors that we must not have when the doctors are working with our software in real life. It has definitely improved the quality of our software. Eggplant found all the errors before release and gave us the information we needed to look closer at some of our modules, and we improved the quality of our product as a result.

We are able to now automate tests, which so far have been manual. Now, it is possible to automate them.

Prior to using the product, all regression testing was done manually for our POS system. Now regression test packs are 70% automated. We have also integrated eggPlant into HP ALM.