The success of an application today hinges on customer experience. To a large extent, it’s the sum of two components, one being the applicability of the software product features to the target audience and the second, the experience of the customer while using the application. In October 2021, a six-hour outage of the Facebook family of apps cost the company nearly $100 million in revenue. Instances like these underline the need to focus on application performance for a good customer experience. We are witnessing an era of zero patience, making application speed, availability, reliability, and stability more paramount to product release success.
Modern application development cycles are agile, or DevOps led, effectively addressing application functionality through MVP and subsequent releases. However, the showstopper in many cases is application underperformance. This is an outcome of the inability of an organization to spend enough time analyzing release performance in real-life scenarios. Even in agile teams, performance testing happens one sprint behind other forms of testing. With an increase in the number of product releases, the number of times application performance checks can be done, and the window available to do full-fledged performance testing is reducing.
How do you engineer for performance?
Introducing performance checks & testing early in the application development lifecycle helps to detect issues, identify potential performance bottlenecks early on and take corrective measures before they have a chance to compound over subsequent application releases. This also brings to the fore predictive performance engineering – the ability to foresee and provide timely advice on vulnerable areas. By focusing on areas outlined in the subsequent sections, organizations can move towards continuously engineering applications for superior performance rather than a testing application for superior performance.
Adopt a performance mindset focused on risk and impact
Adopting a performance mindset the moment a release is planned can help anticipate many common performance issues. The risks applicable to these issues can be classified based on various parameters like scalability, capacity, efficiency, resilience, etc. The next step is to ascertain the impact those risks can have on the application performance, which can further be used to stack rank the performance gaps and take remedial measures.
An equally important task is the choice of tools/platforms adopted in line with the mindset. For, e.g., evaluating automation capability for high scale load testing, bringing together insights on the client as well as server-side performance & troubleshooting, or carrying out performance testing with real as well as virtual devices, all the while mapping such tools against risk impact metrics.
Design with performance metrics in mind
Studies indicate that many performance issues remain unnoticed during the early stages of application development. With each passing release, they mount up before the application finally breaks down when it encounters a peak load. When that happens, there arises a mandate to revisit all previous releases from a performance point of view, which is a cumbersome task. Addressing this issue calls for a close look at behaviors that impact performance and building them into the design process.
· Analyzing variations or deviations in past metrics from component tests,
· Extending static code analysis to understand performance impacts/flaws, and
· Dynamic code profiling to understand how the code performs during execution, thereby exposing runtime vulnerabilities.
Distribute performance tests across multiple stages
Nothing could be more error-prone than scheduling performance checks towards the end of the development lifecycle. When testing each build, it makes a lot more sense to incorporate performance-related checks as well. At the unit level, you can have a service component test for analyzing at an individual service level and a product test focusing on the entire release delivered by the team. Break testing individual components continuously through fast, repeatable performance tests will help to understand their tolerances and dependencies on other modules.
For either of the tests mentioned above, mocks need to be created early to ensure that interfaces to downstream services are taken care of, without dependency on those services to be up and running. This should be followed by assessing integration performance risk whereby code developed by multiple DevOps teams is brought together. Performance data across each build can be fed back to take corrective actions along the way. Continuously repeating runs of smaller tests and providing real-time feedback to the developers help them understand the code development much better and quickly make improvements to the code.
Evaluate application performance at each stage of the CI/CD pipeline
Automating and integrating performance testing into the CI/CD process involves unit performance testing at the code & build stages, integration performance testing when individual software units are integrated, system-level performance testing and load testing, and real user monitoring when the application moves into a production environment. Prior to going live, it would be good to test the performance of the complete release to get an end-to-end view.
Organizations that automate and integrate performance tests into the CI/CD process are a common practice that runs short tests as part of the CI cycle unattended. What is needed is the ability to monitor the test closely as it runs and look for anomalies or signs of failure that point to a corrective action to be taken on the environment or on the scripts as well as application code. Metrics from these tests can be compared to performance benchmarks created as part of the design stage. The extent of deviations from benchmarks can point to code-level design factors causing performance degradation.
Assess performance in a production environment
Continuous performance monitoring happens after the application goes live. The need at this stage is to monitor application performance through dashboards, alerts, etc., and compare those with past records and benchmarks. The analysis can then decode performance reports across stages to foresee risks and provide amplified feedback into the application design stage.
Another important activity that can be undertaken at this stage is to monitor end-user activity and sentiment for performance. The learnings can further be incorporated into the feedback loop driving changes to subsequent application releases.
Continuously engineer application performance with Trigent
Continuously engineering application performance plays a critical role in improving the apps’ scalability, reliability, and robustness before they are released into the market. With years of expertise in quality engineering, Trigent can help optimize your application capacity, address availability irrespective of business spikes and dips, and ensure first-time-right product launches and superior customer satisfaction and acceptance.