Over the last few decades, software has become an increasingly important part of everyday life. People depend on it for connecting with friends, managing finances, and advancing their careers. Although simple on the surface, these daily interactions are complex and many have begun to incorporate machine learning and artificial intelligence (AI).
These implementations make system behavior challenging to test and validate. Automated testing revolutionized the software development life cycle (SDLC). However, quality assurance (QA) processes remain largely stagnant. This article will examine the steps it will take to make hands-off automation tools for QA a reality.
Why are automation tools for QA necessary?
Optimization efforts often overlook QA, yet it’s one of the biggest bottlenecks in the testing process. Test managers and developers alike prioritize speed in the deployment cycle. Testing tools have evolved to meet these demands, but feedback can still pose a problem.
Completely testing a system under development could take days or weeks. Compared to the rest of the automated testing process, QA is a snail. This issue is present in all larger software projects where the corpus of tests grows so enormous that runtimes are prohibitive.
Advancing Towards QA Automation
Recent exponential growth in optimization solutions has made quality improvement more accessible than ever. It’s important to note that validating quality doesn’t begin with QA; it’s embedded in the entire SDLC. Your company has likely already made some of the necessary strides towards hands-off automation tools for QA. To understand their applications, let’s break them down into four phases.
Phase I: The CI/CD Pipeline
CI/CD effectively weaves testing into each phase of the SDLC. Identifying bugs earlier in the SDLC is vital to preserving QA’s resources. Correcting defects before they make it to QA is a step in the right direction. Unfortunately, faster bug detection alone doesn’t reach the root of the issue. QA departments often lack resources and testing processes can be exclusionary.
To make the most of your CI/CD implementation, data from all parts of the SDLC must integrate into the QA pipeline. Static analysis can be used to verify code quality and then to compare it against your KPIs. Eventually, the goal should be to create a codebase that you can use to standardize deployment. This standardization would dramatically alleviate the weight of decision-making in QA.
Phase II: Cloud-Native Architecture
One roadblock to automation in QA is that physical test setups must be optimized to guarantee a balance between instant feedback and validation confidence. Cloud resources can partially mitigate this problem by allowing tests to be run in an infinitely parallel manner. Nevertheless, 100% validation continues to be a hurdle in the relay race towards QA automation.
Phase III: Automated Testing
The growing number of automation tools for QA has made automated testing more feasible. In the case of Copado Robotic Testing, test logs are linked to Git changes through Robot Framework. Access to the statistical analysis of test run history inherently facilitates improvements. Historical log data also creates an excellent background to determine the minimum amount of tests needed for statistically sound results.
In this phase, automated integration and deployment don’t exist. The release process likely still requires manual testing to verify the actual release. Release decisions remain in human control because testing data is contained within the test cases. In other words, all the data about system quality is collected in the development process, not the production environment. Automated testing focuses on the technical quality of software rather than its functional or business value. Human intervention is still required to make deployment decisions.
Phase IV: DevOps
This phase requires a high-functioning DevOps implementation. Most modern businesses already have a DevOps strategy in place. But how can it propel your business towards automation tools for QA? The key difference here is that testing optimization needs to shift focus towards software usage in production. The role of automated testing is currently disconnected from the realm of business hypotheses. To connect these two realms, you’ll need a clear understanding of how business goals correspond with software development goals.
Measuring the impact of releases on the quality of the product is already within reach. Applications like Google Analytics can analyze pertinent data like usage logs and billing information. All this valuable data should be aggregated into a metric your company can use to measure the release’s performance relative to business goals.
The Elements that Hold Us Back from Fully Automated QA
Each of the phases above promises to work toward more intelligent automation. But before automation tools for QA are possible, you must address each of these elements:
Validation of business goals. The shift of validation from technical quality to business goals should be held close to the heart of QA. Translating functionality to value should be a primary DevOps objective.
Data integration beyond the testing process. Throughout each phase, data should be collected and linked to deployment. Incorporating analytics into production is integral to automating validation.
Early inclusion of QA. The separation of the testing process from QA can contribute to lengthy validation periods. The collection of data for QA should begin as early in the pipeline as possible.
Automation tools for QA will support decision-making through meaningful data interpretation. The primary goal is to make insight actionable. Reducing the number of choices we must make while simultaneously strengthening the basis for those choices reduces decision fatigue and streamlines the SDLC for all.