Measuring and Improving Code Performance with Software Development Metrics

In the fast-moving software development industry, product code is constantly changing. Because every change can impact code performance, it’s important for companies to keep an eye on the performance of their software. It’s essential to keep track of performance metrics that are likely to affect users of the software. After all, users don’t want to use a product that runs sluggishly or is constantly crashing.

By tracking software development metrics, companies can ensure that their product is following a trajectory toward high-quality performance. Companies can customise the metrics that they track to ensure that their product offers a peak customer experience. BlueOptima provides tools to track metrics that can be used to improve a company’s software development workflow. 

Choosing Which Metrics to Track

When choosing the metrics that a company should track, it is important to consider that company’s goals. There is no one-size-fits-all choice of metrics to track. Instead, a company should consider software development metrics that correspond directly to what the company aims to achieve.

When examining code performance, it can be helpful to consider how the customer uses the software. What functions are most important to the customer, and which performance metrics can be optimised to improve that customer’s experience? Software development metrics are useless without context—They must provide information that can be acted on in a meaningful way. This means that, when choosing metrics to monitor, it’s important to choose metrics that add value to the project.

By only tracking metrics that add value, companies can focus on tracking and improving code performance within key aspects of their projects. Consistently monitoring a set of important metrics also makes it possible to access the full value of this data by observing the trends in these metrics over time. When considering the metrics to track at your company, there are a number of performance metrics that can be useful for measuring and improving user experience.

Application Performance Index (Apdex)

Apdex is a score that is used to measure the response time of web applications and services. This score is an industry-standard for measuring application performance, and it is designed to emphasise the effects of response time on user experience. Every response time within the application is classified as either leading to a “satisfied,” “tolerated,” or “frustrated” user experience. The exact response time associated with each of these tiers can be defined for the application, making this score easily customisable to a wide range of possible user experiences. The Apdex score is then calculated as a ratio between the number of satisfied and tolerated requests to the total number of requests made.

Mean Time Between Failures

Mean time between failures is a metric that measures the time from when an application starts working to when it reaches a failure state. Although in an ideal world software would never fail, in reality, software failure states cannot be completely avoided. This is especially true in a production environment, where software will eventually encounter an unexpected state. This metric can be a great measure of how an application performs in a production environment. Increased time between failures is an indicator of improved software performance.

Mean Time to Recovery

Mean time to recovery is a metric that works neatly in tandem with mean time between failures. This metric starts when a software failure state is reached and measures the time until the full service is restored. This metric is helpful for examining how quickly software recovers from a failure state. Because the time to recover can have a significant impact on user experience, this metric is especially useful for systems that encounter frequent, unavoidable failures. Even for systems where failure is rare, however, this metric is useful for ensuring that the software recovers as gracefully as possible.

Mean Time to Repair

Mean time to repair is a metric that describes failures that require developer action. Specifically, this metric measures the total time from when a failure is reported to when a solution is fully deployed.  This metric is especially important for security-related breaches or other errors for which quick repair is a priority. Because this metric relies on developer action, it reflects both the ease with which a failure can be fixed within the system and the efficiency of the developer pipeline to address it. The total time for this metric includes the time to troubleshoot the reported failure, repair the problem, test the solution, and deploy the required software.

Application Crash Rate

The application crash rate is a simple metric that can still provide useful information about software performance. This metric can be calculated by dividing how many times an application fails by the total number of times that is used. An increase in the crash rate over time can be a strong indicator that there are significant problems within the code base that should be examined. Although this metric is generally less helpful for tracking improvements over time, it can be a useful way to identify potential performance problems within the software. A low application crash rate is an important component of high software performance.

CPU Usage

For server-based applications, CPU usage can be another important metric to monitor. Very high CPU usage can cause a system and its applications to lag, while low CPU usage might indicate a system is wasting resources. By profiling server CPU usage, companies can monitor how their software is performing and make changes as needed to optimise application performance. Monitoring this metric over time can help to ensure that a product is utilizing resources in an efficient way and can help developers identify potential problems before they have an impact on user experience.

Tracking any of these metrics can unlock useful information about the performance of your company’s product over time. By consistently monitoring these metrics, you can discover new pathways for improving code performance. With products like BlueOptima’s Developer Analytics, you can automate the process of monitoring metrics and ensure that your product is always on the path to improvement.

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