This is the final post in a three-part series on software testing. So far, the series has included topics such as general best practices and strategies for effective software testing, how to model software tests, and how to conduct a risk analysis.
There was also an important discussion on choosing the right test metrics—the main point about test metrics is that good test metrics provide actionable insights while bad ones don’t tell you anything (see this article by SeaLights with a list of useful metrics). The series concludes now by overviewing load testing and trend analysis.
Analyze Your Load Test Results Properly
Even after incorporating a risk-based approach to overcoming potential performance issues, load tests are still critical in finding unexpected issues with how the software performs under different constraints.
Making the wrong decision to release an application only for it to break under high usage could have disastrous financial and brand-related consequences for software development companies.
Thorough analysis of performance test results is where testers can earn their money—generating canned reports from load testing tools is not a useful approach. It’s vital for testers to fully understand some of the main performance test metrics both in isolation and in relation to each other.
Important performance KPIs for load tests include:
- Response times: how long does it take for a user to complete a transaction, for example, using an online application?
- Throughput: how many concurrent users/transactions can the application handle?
- Load distribution: for application servers (Java, PHP, .NET), how well balanced are loads between each engine?
- Resource utilization: for all host web and application servers, how much of the CPU, memory, and disk is being consumed?
Trend analysis, sometimes referred to as predictive analytics, is an important factor in improving software tests with data-driven decisions. The systems and tools used to perform software tests generate lots of data, including defect logs, test results, production incidents, project documentation, and application log files. Feedback provided by customers is another source of data that you can make use of.
The idea behind trend analysis is that instead of leaving all that test data unused on a hard drive, software testers can use analytic tools to extract insights from previous test data to make adjustments to future QA practices for more optimized and efficient software tests.
Conducting a trend analysis from all this data has become more straightforward and accessible due to advancements in machine learning algorithms, which can extract useful information, determine patterns, and learn from those patterns to make accurate predictions about future outcomes. For example, digging deep into the root causes of software defects can highlight defect “hotspots” that are more likely to cause future issues, helping QA teams to prioritize and optimize their tests.
The overall benefits of predictive analytics are more efficient testing, improved customer experience, and quicker time to market for software.
Not deviating too far from established software testing best practices can reduce the risk of testing software ineffectively. Software testers need to analyze load test results correctly so they can turn information about how the software performs into intelligent decisions about when to release the application. A trend analysis further improves software testing by using data artifacts from previous tests to improve future tests.