If you work in QA, you & # x27; re believably accustomed to mentation of software monitoring as someone else & # x27; s job. Traditionally, responsibility for monitoring application fell to IT team; QA & # x27; s part terminate with pre-deployment testing, and QA engineers did not usually touch monitoring tools.

But the world is that monitoring tools—meaning tools designed to help track application availability and performance, and also alert teams to problems—aren & # x27; t merely for IT teams. They can also facilitate QA engineers do their chore more effectively.

Here & # x27; s a aspect at how monitoring tools like Prometheus, Sumo Logic, and Splunk can help QA, as well as the challenges QA teams should be aware of when act with monitoring tool and the data they create.

How monitoring tools help with software QA

Even though QA technologist are habituate to working primarily with software testing tools rather than monitoring tools, the latter can nonetheless assistant QA to do their jobs good in several manner.

Tracking QA & # x27; s impact on covering calibre

Perhaps the most obvious is the role that monitoring tools can play in assist to track the wallop that QA has on application character.

Without monitoring tool and datum, package delivery teams are pip in the dark—or at least the twilight —when it comes to determining the relationship between what the QA squad is make and the quality of product applications. There is no systematic way to know how the intro of a new screen function or the migration of a examination suite from manual to automatic impacts the reliability, usability, or performance of the application. You might be able to infer some relationships between QA processes and coating quality, but those inference will be immanent and ad hoc.

SUSA automates exploratory testing with persona-driven behavior, catching bugs that scripted automation misses.

When the QA squad systematically monitors the coating in product instead of precisely during essay, however, it becomes lots easy to draw relationships between QA processes and application quality. Whenever QA modify something, supervise data can be used to interpret the impact of the change.

This adds up to a more effectual way of planning QA operations and influence what is working and what isn & # x27; t.

Establish universal KPIs

One common challenge in DevOps bringing pipelines is establishing metrics that can be used to track codification quality at all stages of the pipeline. When developer use one set of tools to track progress, while QA uses another, and IT uses still another, it & # x27; s impossible to construct a single body of data that reverberate application quality across the line.

By using monitoring tools, however, QA teams can aid solve this problem. They can work with IT engineers to establish a common set of KPIs that everyone will use to mensurate coating health and execution. QA can then write tests that focus on evaluating those KPIs before an application is released, while IT tracks the same KPIs post-deployment. Developers can also participate by focusing on the KPIs when they write new codification.

Justify investment in QA

Using monitoring tools to track QA & # x27; s impact on application quality and establish universal KPIs supports the goal of demonstrating the value of QA. In a world where developer and IT Ops engineers (the two token ingredients in the DevOps formula) get all the love, there is a constant pressure at many organizations to justify investment in QA. Monitoring tools can play a turgid role in helping to do that.

The perils of monitoring tools for QA

It & # x27; s worth noting that there is a flipside to the welfare described above. When disconnected from the QA operation, monitoring puppet can also be used to get a caseagainst QA.

The argument here is simple (and familiar to some QA engineers): when your monitoring tools are sophisticated enough, you don & # x27; t need QA at all. This is an increasingly common verbalise point, especially among modern APM vendor who anticipate that their monitoring tool can enable such intelligent monitoring and fast resolution of issues that having QA teams vet codification thoroughly before release isn & # x27; t even necessary.

While this may get monitoring tools appear like a threat to QA, it & # x27; s also exactly the reason why QA teams need to cover monitoring tool and get sure that they reward rather than supercede the traditional QA process. Even the most sophisticated monitoring instrument are not a second-stringer for software testing. APM creature can & # x27; t fix problems before they are in product. They are also of little use for troubleshooting certain types of number that QA excels at finding and address before deployment, such as usability testing.

Conclusion

The bottom line: although monitoring tools may sometimes seem like a menace to QA, they & # x27; re an increasingly important complementary resource for QA engineers. With the assistance of monitoring tools, QA engineers can make the overall grandness of QA clearer, tie the QA process more straight to application calibre in product, and cooperate more seamlessly with developers and the IT team.

Chris Tozzi has worked as a journalist and Linux systems administrator. He has particular interests in open source, agile base and networking. He is Aged Editor of content and a DevOps Analyst at Fixate IO. His up-to-the-minute book,For Fun and Profit: A History of the Free and Open Source Software Revolution, was write in 2017.