Enhance Software Performance Testing with Machine Learning
With advances in data collection, processing, and computation, AI has become the latest buzzword in every industry.
Artificial intelligence [AI] is the intelligence of machines or software, as opposed to the intelligence of other living beings, primarily of humans, defines Wikipedia. It leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind. Today, there are many, real-world applications of AI systems including, speech recognition, online chatbots, computer vision, robotics, etc.
Background
Machine learning (ML) is a field of study in Artificial Intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. ML can be used to reduce the amount of routine and tedious tasks in software development and testing.
Wikipedia defines software testing as, an investigation conducted to provide stakeholders with information about the quality of the software product or service under test. Performance testing is a type of testing in which the speed, responsiveness, and stability of a software, product, or network is evaluated under peak workload.
Performance testing checks and validates an application's capacity and ensures that it works well within the acceptable Service Level Agreements [SLAs]. These days, digital customers are provided with a lot of choices and performance testing plays a major role in ensuring customer satisfaction. If the performance of an application doesn’t meet the expectations of customers, they will opt for another application from a competitor, resulting in business loss for companies.
Performance Testing Progression
Performance testing has evolved a lot over time. Earlier, the focus of performance testing teams was mainly on load test executions and sharing the report with details of test results to development teams. These days, the focus has shifted to performance engineering which expects the testing teams to identify bottlenecks, perform error analysis, and provide performance tuning recommendations.
As enterprise software platforms become more complex, performance issues have become a serious risk that results in loss of millions of dollars. To keep up with the agile mode of development, the traditional testing process is no longer adequate, and teams need to bring in automation. Artificial Intelligence [AI] can play an important factor in test automation [reducing the time consumption and manual intervention involved in various test phases] and Machine Learning [ML], a subset of AI can aid in these activities.
Machine Learning in Performance Testing
Machine learning solutions can evaluate and interpret thousands of statistics per second, providing real-time insight into a system's behavior. Machine learning algorithms can identify data patterns, build statistical models, and make predictions. ML-based anomaly detection systems can help to identify performance bottlenecks faster and accurately.
In the performance testing domain, following are some of the benefits of machine learning
Recommended by LinkedIn
Ø Detect anomalies in transaction response time or server utilization matrices
Ø Identify unusually slow transaction response times
Ø Root cause analysis of performance bottlenecks
Ø Forecast application behavior based on previous events or logs
Some of the performance testing use cases that can take advantage of machine learning include :
Closing Reflections
Machine learning models help to automatically identify irregularities in performance tests as well as to predict the server performance for future events. Accordingly, manual efforts required for test monitoring, result analysis, and time taken to identify performance issues can be reduced.
These days, various licensed and open-source toolkits are available that can be easily integrated with testing tools to enable machine learning capabilities. Apache JMeter the major open-source load testing tool can be integrated with the search and analytics engine - Elastic Search, the open-source systems monitoring and alerting toolkit – Prometheus, or the log analysis and monitoring tool – Splunk.
To sum up, organizations with foresight can utilize machine learning technologies to take a proactive approach to performance testing, rather than a reactive approach after performance issues hit the application.
Do you use any of the machine learning tools or capabilities in your project for performance testing improvement? If so, please share in the comments.
