Enterprise apps are mission-critical applications used by businesses to solve problems, support and automate business processes, and make decisions. Examples of such software include BI tools, mobile apps, payment processing systems, and ERP solutions.
Since enterprise applications provide the foundation for running many profitable businesses, it’s imperative to comprehensively test these apps before deploying them in a live production environment. Automation plays an important role in testing enterprise apps by allowing testers and developers to assess:
- Whether the applications work as intended
- How applications – and end-to-end business processes – will be impacted if a change is made
- How the different components of distributed applications interact and behave under different conditions
Whatever strategies and types of automated tests you use, test automation metrics fulfil a vital role in helping to evaluate and improve upon testing efforts and thus software quality. In this post, you’ll find out about some of the most important test automation metrics you should track when testing enterprise apps (see this learning section by SeaLights, including a broad list of test automation metrics).
Enterprise Apps: Test Automation Metrics
Equivalent Manual Test Effort (EMTE)
One goal of test automation is to conduct tests more efficiently, which is vital for today’s fast-paced application environments. Since enterprise apps are mission-critical, they need to be tested thoroughly and swiftly.
The EMTE metric provides a simple and meaningful measure of the value that automation provides when testing enterprise apps. EMTE is a measure of the time taken to run a given suite of manual tests.
You can use EMTE in several ways, including comparing it with the speed of running the same automated tests. You can also compare the time taken to build automated tests with the EMTE for manual tests in order to identify at what point automation provides positive ROI.
Defects in Production
Enterprise apps are critical to the smooth functioning of a business—serious defects that escape into production after the release of these apps can spell disaster. Consider the case of RBS, a large British banking group. A failed software upgrade made RBS’ online banking services inaccessible, resulting in a £56 million fine from regulators.
Effective automated test suites must catch all serious issues before they manifest in production, and this metric gives a “bottom line” measure of automated testing effectiveness.
Many test automation tools display a test execution metric in their dashboards, showing the total tests executed as part of a build. While test execution is not ideal for making judgments on software quality, it’s still important that the majority of the tests run in all builds.
Teams can display this metric graphically, highlighting incomplete tests.
An important metric for enterprise apps is cycle time, which is the time it takes to execute a task or process from start to finish. Defect cycle time, for example, is how long it takes to make a change to software in response to a bug and release the newly fixed module into production. The end-to-end cycle time measures the time from enterprise app definition to production release.
Measuring cycle time is extremely useful for enterprise apps, particularly in terms of insights revealed about automated tests. The key is to start tracking cycle times as soon as you can, and investigate variations in those times. For example, if your defect cycle time jumps from one day to seven days, automated tests may be failing and slowing down deployment, becoming a bottleneck process.
Test automation, while useful, is not enough on its own for delivering higher quality enterprise applications with fewer defects.
Automation is not as easy as buying a tool that automatically runs tests and outputs results. Effective test automation efforts should focus on the right test automation metrics in order to drive improvements in testing efficiency and the quality of the underlying enterprise apps.
Lastly, enterprises should think about automation in parallel, instead of the traditional linear automation model, so that QA automation can transfer to all stages of an enterprise app’s project lifecycle for even greater efficiency. This way, automation is an ongoing process, with new test cases being auto-generated each time something changes. This also delivers benefits in the way of automatic compliance reports that can be created after each test run.