Amazon Web Services (AWS) announced the general availability of Amazon Q, a generative artificial intelligence (AI)-powered assistant for accelerating software development and leveraging companies’ internal data.
We outlined how to get started with test automation in parts one and two of this series. Now, we'll finish with what it takes to achieve an advanced level of maturity in your automation practice.
Start with Part 1: Beginning Your Test Automation Journey
Start with Part 2: Building Confidence in Automation
As we've stated throughout this series, the end goal of test automation is to enable frictionless, continuous testing in a high-throughput deployment pipeline. As you progress through the beginner and intermediate stages of test automation, you should notice a gradual increase in efficiency and release velocity. However, following a templated approach to test automation will only take you so far.
The expert stage of test automation is all about continuous optimization. More specifically, this phase is about collecting data about your existing process, analyzing that data to derive quality insights, applying those insights to improve your practice and then measuring these improvements as part of repeating the cycle again. There are three key steps to realize continuous optimization.
Step 1: Do Just Enough Testing at Each Phase of Deployment
To prepare yourself for successful continuous optimization, you should first take a step back to ensure you are doing the right amount of testing at each stage of your deployment process. How much unit and initial integration testing are you doing? How many smoke and sanity tests are running sooner rather than later to ensure which builds are stable, and which warrant additional downstream testing? When are you running your regression tests and your later-stage manual tests? Where does your non-functional or other costly testing fit in your pipeline?
It is important to analyze your pipeline and verify you are doing just enough testing at each stage because it allows you to halt if you have an issue at a particular stage. This testing approach is really the first step of continuous optimization because it is both cost effective and establishes multiple, measurable milestones in your testing pipeline to measure. If you spread the process out and test incrementally, you can start collecting data at every single stage. Make sure to have quantifiable quality gates at each of these stages. This helps drive which measurements to take during the testing process.
Step 2: Collecting Metadata About Your Testing Process
At each phase of testing, think about what data you can collect and feed into a repository so you can mine it later. Focus on at least these key questions while you are implementing your metadata collection strategy:
■ What stage of the testing process are we looking at?
■ What build or milestone is under test?
■ How many tests were run?
■ How long did each test take?
■ What platforms were tested on?
■ Which tests passed and which ones failed?
■ Is the ratio of passed-to-failed tests acceptable for that particular quality gate?
■ How long is it taking to triage automated test failures?
■ Was the build kicked back or is the deployment process continuing?
■ What bugs were associated with this build?
Collecting test metadata that answers questions like these at each phase allows teams to compile substantial insights in the future. Especially when munging this data with data from other teams (e.g. engineering, marketing, etc.).
Step 3: Making Data-Driven Decisions
Now that you have captured data about your testing process, you can organize and visualize it using tools like Splunk or Domo to make it more digestible.
Once you have your data in a dashboard, it’s time to actually do something with it. You may, for example, look at your data and determine that a subset of your automated tests are not providing the right value for your team. This is a common situation where a few very complex tests have been automated but don’t run reliably. By collecting the data mentioned above, you should be able to measure the impact such unreliable tests are having on your release process. You may instead try adding those tests to the manual suite and then measuring how that improves your test times.
To take things a step further, you can also incorporate data from other departments into your insights to further refine your testing strategy. For example, consider munging development code coverage data into your quality decisions. This can help you literally visualize what your testing triangle looks like. Also consider pulling marketing insights into your datasets to cross reference real-time customer usage data with your testing strategy. Your customer’s usage patterns will change and evolve over time as your application grows and new features are introduced. It’s important to stay on top of how those usage patterns change so that you can quickly and continuously adjust your testing strategy appropriately.
Remember that improving your automation practice is an unending process. You are never truly done. There is always more that can be done. Following these steps while incorporating the lessons you’ve learned along the way will help you continually optimize your automation practice.
Industry News
Red Hat announced the general availability of Red Hat Enterprise Linux 9.4, the latest version of the enterprise Linux platform.
ActiveState unveiled Get Current, Stay Current (GCSC) – a continuous code refactoring service that deals with breaking changes so enterprises can stay current with the pace of open source.
Lineaje released Open-Source Manager (OSM), a solution to bring transparency to open-source software components in applications and proactively manage and mitigate associated risks.
Synopsys announced the availability of Polaris Assist, an AI-powered application security assistant on the Synopsys Polaris Software Integrity Platform®.
Backslash Security announced the findings of its GPT-4 developer simulation exercise, designed and conducted by the Backslash Research Team, to identify security issues associated with LLM-generated code. The Backslash platform offers several core capabilities that address growing security concerns around AI-generated code, including open source code reachability analysis and phantom package visibility capabilities.
Azul announced that Azul Intelligence Cloud, Azul’s cloud analytics solution -- which provides actionable intelligence from production Java runtime data to dramatically boost developer productivity -- now supports Oracle JDK and any OpenJDK-based JVM (Java Virtual Machine) from any vendor or distribution.
F5 announced new security offerings: F5 Distributed Cloud Services Web Application Scanning, BIG-IP Next Web Application Firewall (WAF), and NGINX App Protect for open source deployments.
Code Intelligence announced a new feature to CI Sense, a scalable fuzzing platform for continuous testing.
WSO2 is adding new capabilities for WSO2 API Manager, WSO2 API Platform for Kubernetes (WSO2 APK), and WSO2 Micro Integrator.
OpenText™ announced a solution to long-standing open source intake challenges, OpenText Debricked Open Source Select.
ThreatX has extended its Runtime API and Application Protection (RAAP) offering to provide always-active API security from development to runtime, spanning vulnerability detection at Dev phase to protection at SecOps phase of the software lifecycle.
Canonical announced the release of Ubuntu 24.04 LTS, codenamed “Noble Numbat.”
JFrog announced a new machine learning (ML) lifecycle integration between JFrog Artifactory and MLflow, an open source software platform originally developed by Databricks.
Copado announced the general availability of Test Copilot, the AI-powered test creation assistant.