When it comes to software development, ensuring optimal performance is critical for the success of any business. Consumers have high expectations for software performance, and any delay or glitch can lead to frustrated users, lost revenue, and damage to the company’s reputation.
Quality assurance (QA) plays a crucial role in improving software performance by identifying and addressing potential performance issues before they become significant problems. However, QA teams often face several challenges in their efforts to improve software performance.
In this blog post, we look at 7 common QA challenges for improving software performance, the consequences of not addressing them, and strategies or best practices for overcoming them. By understanding these challenges and implementing effective solutions, you can improve your software performance and deliver a better overall user experience.
But first, let’s have a quick look at the role of quality assurance in improving the performance of your software products.
QA can play a critical role in improving software performance by identifying and addressing issues related to the quality and efficiency of the software. They are responsible for ensuring that the software meets the desired quality standards and performance expectations.
QA teams can contribute to software performance improvement by conducting comprehensive performance testing, establishing performance baselines, identifying performance bottlenecks, and developing effective performance testing strategies. They can also help in identifying and managing test data, performing performance profiling, and reproducing and analysing performance issues.
By detecting and resolving software performance issues early in the development cycle, QA teams can help to ensure that the software performs efficiently and reliably in real-world scenarios. This can lead to increased customer satisfaction, improved business performance, and a competitive advantage in the marketplace.
A clear performance baseline in QA refers to a set of metrics or benchmarks that represent the expected performance of a software application under normal operating conditions. It’s a reference point that’s used to establish performance goals and requirements and to monitor and measure the performance of the software over time.
A clear performance baseline typically includes metrics such as response time, throughput, and resource usage, which are measured and recorded under various conditions and scenarios. These are used to establish a baseline level of performance for the software, which can be compared to future performance measurements to determine if the software’s performance is improving, degrading or remaining stable.
This is a common QA challenge mainly because it’s difficult to establish a baseline in the first place. Different factors, such as hardware, network, and user behaviour, can impact the software’s performance, making it challenging to establish a consistent baseline. Additionally, performance testing can be time-consuming and resource-intensive, making it challenging to conduct comprehensive testing across all possible scenarios and configurations.
The steps below should help you establish a clear performance baseline to measure, monitor, and optimise the performance of your software over time.
First, define the performance metrics that will be used to measure software performance. These metrics could include response time, throughput, resource consumption, and other relevant metrics.
Next, determine the test scenarios that will be used to measure the performance of the software. These scenarios should be representative of the typical usage patterns and operating conditions encountered during software development.
Then, establish the baseline performance for the software. This involves measuring the software’s performance under typical operating conditions and using the defined performance metrics. The baseline performance should be established on a representative system, network, and data set.
Identify performance goals that will help to improve software performance. These goals should be based on the baseline performance and should be achievable and measurable.
Establish ongoing performance monitoring to ensure that software performance remains at an acceptable level. This should include monitoring the established performance metrics and comparing the results to the baseline performance and performance goals.
Test coverage refers to the extent to which a software application has been tested to ensure that it meets the specified requirements and functional specifications. It measures the completeness of the testing effort and the degree to which the software has been exercised under a range of operating conditions and scenarios.
Test coverage can be measured in terms of the percentage of code or functionality that has been tested or in terms of the number of test cases that have been executed. A high level of test coverage indicates that the software has been thoroughly tested and is more likely to be reliable and robust in operation.
Testing can be time-consuming and resource-intensive, so QA teams may struggle to keep up with the pace of development and may not have the resources to test every aspect of the software thoroughly. Additionally, there may be pressure to release software quickly to meet market demands, which can result in shortcuts being taken in the testing process.
Achieving high test coverage is a critical component of software development QA and requires a comprehensive and well-designed testing strategy that covers all aspects of the software’s functionality and operating conditions. Here are some steps to achieve comprehensive test coverage for improved software performance.
Understand user requirements and the intended use of the software. This helps to identify the critical features and functions that require testing.
Identify the test scenarios that will be used to evaluate the software. These scenarios should cover all critical features and functions of the software, and they should be designed to simulate real-world usage.
Develop test cases that will be used to execute the test scenarios. The test cases should be designed to evaluate the software’s performance under different conditions and use cases.
Use automated software testing tools to execute the test cases. Automated testing helps to reduce the time and resources required for testing while also improving test accuracy and repeatability.
Use test coverage analysis to evaluate the effectiveness of the test cases. This helps to identify areas of the software that have not been adequately tested and allows QA teams to adjust their testing strategy accordingly.
Conduct exploratory testing to identify defects that the test cases may not capture. Exploratory testing allows QA teams to test the software under different conditions and use cases and helps to identify potential performance issues.
Conduct performance testing to evaluate the software’s performance under different load conditions. This helps to identify potential performance bottlenecks and allows developers to optimise software performance.
A poor performance testing strategy refers to a testing approach that fails to adequately measure and optimise the performance of a software application. This can be due to various factors, such as a lack of performance testing, incomplete or unrealistic test scenarios, inaccurate performance metrics, failure to optimise performance, and a lack of continuous monitoring.
It is important to develop a comprehensive and well-designed performance testing strategy that covers all aspects of the software’s functionality and operating conditions and to continuously monitor and optimise the performance of the software to ensure that it meets the specified performance goals and requirements.
A poor performance testing strategy is a common QA challenge because it can be complex and resource-intensive, difficult to detect and diagnose performance issues, not always recognised as a priority within organisations, and limited by access to appropriate testing environments and data.
A comprehensive and well-designed performance testing strategy means covering all aspects of the software’s functionality and operating conditions and continuously monitoring and optimising the performance of the software to ensure it meets the specified performance goals and requirements.
See below for some steps to create an effective performance testing strategy.
Determine the software’s performance requirements, such as response time, throughput, and scalability. These requirements should be aligned with user expectations.
Identify critical scenarios that must be tested to ensure optimal software performance. These scenarios should be based on real-world usage patterns.
Create a test environment that mimics the production environment to ensure accurate testing.
Develop test scripts that simulate real-world scenarios and cover all critical features of the software. These scripts should be reusable and maintainable.
Conduct testing under different load conditions to identify performance bottlenecks and issues. This testing should be done using different tools and techniques, such as load testing and stress testing.
Analyse the test results to identify performance issues and areas that need optimisation. Use tools to analyse and report test results accurately.
Address performance issues and optimise the software’s architecture, design, and code to improve software performance. Re-run the test scripts after optimisation to validate the improvement.
Monitor performance over time to ensure that the software is performing optimally under changing conditions.
Inefficient test data management means improper or ineffective handling, preparation, storage, and maintenance of test data used during the testing phase of the software development life cycle. Test data management includes defining the test data requirements, creating or generating the test data, storing the data, and maintaining it to ensure it remains relevant throughout the testing phase.
It can result from a lack of planning, proper documentation, or tools. Test data management is a critical component of software testing, and efficient and effective management of test data is necessary to ensure the software application is high-quality and meets the specified requirements.
Inefficient test data management is a common QA challenge because it can be complex and difficult to manage, subject to regulatory requirements, not always prioritised within organisations, and limited by available resources and tools.
Efficient test data management is essential for effective software testing, and there are several strategies that can be employed to achieve this. Some of these strategies include the following.
Test data can become stale over time and may no longer represent real-world scenarios. It is important to regularly review and update test data to ensure that it is relevant and up-to-date.
Test data can be created manually or generated automatically using test data management tools. Since manual test data generation can be time-consuming and laborious, using automation tools can make it quick and efficient.
Protecting sensitive data is important to comply with data privacy regulations. Implementing data masking and anonymisation techniques can help ensure that sensitive data is protected during testing.
Testing with a subset of data can significantly reduce the amount of test data needed, making testing more efficient. Plus, prioritising test data based on its importance can help identify critical data that needs to be tested first, reducing the risk of testing delays.
Ensure that the test environment is properly configured to support testing requirements. This includes making sure that the right test data is available and that the environment can handle the expected workload.
Test data should be regularly reviewed and updated to ensure it remains relevant throughout the testing phase. This can involve removing obsolete data, adding new data or updating existing data.
Performance profiling is the process of analysing software performance by measuring its resource usage and identifying bottlenecks. It involves collecting data on the performance of the software, such as memory usage, CPU usage, and disk I/O, and then analysing this data to identify areas where the software is not performing optimally.
Performance profiling is particularly important for identifying performance issues in large-scale applications or applications that are expected to handle a high volume of data or users. By profiling the software during development and testing, developers and QA teams can identify and address performance issues before they impact users.
This is a common QA challenge mainly because it requires specialist skills and tools, which many businesses may not readily possess. It can also be challenging to simulate real-world usage scenarios, and performance issues can be intermittent or depend on specific usage patterns. Some businesses don’t even recognise or prioritise it due to competing priorities, such as time-to-market or feature development.
Effective QA performance profiling in software development requires a structured and methodical approach, as well as access to appropriate tools and expertise. Here are some best practices you can follow.
Identify the performance metrics that are most relevant to the software being developed. These may include metrics such as CPU usage, memory usage, network throughput, and response time.
Choose a performance profiling tool that is appropriate for the software being developed. There are many performance profiling tools available, including open-source tools such as Perf and commercial tools such as Visual Studio Profiler and Intel VTune.
Instrument the software with the performance profiling tool for collecting performance data during development and testing. This involves adding code to the software that collects the relevant performance metrics and sends them to the profiling tool.
Analyse the performance data collected by the profiling tool to identify performance bottlenecks and other issues that may be impacting software performance. This may involve reviewing the data in graphical or tabular form, using statistical analysis tools to identify patterns, or using machine learning algorithms to identify anomalies.
Use the insights gained from performance profiling to optimise the software and improve its performance. This may involve making changes to the code, adjusting system settings, or making other changes to improve performance.
Reproducing performance issues is critical as it lets businesses identify and fix the root causes of issues. When a performance issue is reported, it may be difficult to determine the exact cause without reproducing the issue consistently. So, it can make it difficult to identify the exact point of the problem and then fix the specific issue.
Reproducing performance issues in a controlled environment allows businesses to capture data and metrics that can help identify the source of the issue. This can include data such as system performance metrics, network activity, and user behaviour. By capturing this data, businesses can recreate the conditions that led to the performance issue and then test potential solutions to see if they resolve the issue.
Difficulty reproducing performance issues is a common QA challenge because performance issues can be intermittent, and it can be difficult to simulate the specific usage scenarios that lead to the issue. You may also find it tough to reproduce issues across different environments and identify the root cause.
Reproducing performance issues requires a systematic and methodical approach, as well as access to appropriate tools and expertise. Here are some strategies you can follow to identify and address performance issues, leading to improved system performance and better customer experiences.
When a performance issue is reported, it is essential to document the details of the issue, including the steps to reproduce it, any error messages, and the expected and actual outcomes.
Reproduce the performance issue in the same environment where the problem occurred. This includes using the same hardware, software versions, network settings, and data.
Use representative data that reflects the real-world usage of the software. This can include using data sets representing the typical workload and user behaviour.
Use performance testing tools to capture and analyse system performance metrics, network activity, and user behaviour. These tools can help identify the root cause of the performance issue and validate the effectiveness of solutions.
Collaborate with stakeholders, including software developers, testers, and end-users, to validate the reproduction of the issue and the effectiveness of the solutions. This can help ensure the software is stable and performs optimally for its intended users.
Test potential solutions in a controlled environment and validate their effectiveness in resolving the issue. This can include testing different configurations, software patches, or hardware upgrades.
Finally, validate the solution by retesting and monitoring system performance to ensure the issue has been fully resolved.
Poor performance monitoring and analysis refers to the inadequate tracking, measurement, and analysis of software performance. It means the software development team is not collecting relevant data to monitor and analyse performance issues effectively.
This may include not tracking system response times, errors, or other relevant metrics or not analysing the data collected to identify potential performance bottlenecks or areas for optimisation. As a result, performance issues may go undetected or unresolved, leading to suboptimal system performance, reduced user satisfaction, and potential business impact.
Performance monitoring and analysis require specialised skills and tools and can be time-consuming and resource-intensive. Issues can be difficult to reproduce, making it challenging to identify and resolve them. Hence, many businesses struggle to implement effective performance monitoring and analysis processes, leading to suboptimal system performance and user experiences.
Below are some best practices to establish effective QA performance monitoring and analysis, leading to improved system performance, better user experiences, and increased customer satisfaction.
Define the KPIs that are relevant to the software’s performance and track them regularly. This will help in identifying issues and areas for improvement.
Use performance monitoring tools to track the KPIs in real-time. These tools can provide valuable insights into the software’s performance and help identify issues quickly.
Set up alerts for critical KPIs so the team can be notified immediately if performance falls below a certain threshold.
Conduct regular performance analysis to identify trends and patterns in the data. This will help in predicting potential issues and identifying areas for optimisation.
Leverage predictive analytics to identify potential performance issues before they occur and to optimise performance.
Collaborate with stakeholders, including developers, testers, and business users, to ensure that everyone is on the same page regarding performance goals and to ensure that issues are identified and addressed promptly.
The 7 common QA challenges discussed in this article include a lack of a clear performance baseline, inadequate test coverage, poor performance testing strategy, inefficient test data management, lack of performance profiling, difficulty reproducing performance, and poor performance monitoring and analysis.
The consequences of not addressing them can be severe: from performance issues to higher costs and lost revenue, including a tarnished business reputation. However, even if some QA challenges may be inevitable, you can implement the mentioned strategies and best practices to overcome them and create a more reliable and efficient software product that meets the needs of your users.
Schedule a discovery meeting today to discuss your challenges and goals with a customer experience transformation partner who specialises in improving software performance!
April 04, 2023