Observability is crucial to software engineering success

Observability is a crucial part of making SaaS applications successful, as it helps ensure they are performing optimally and alerts you when there are potential issues. But despite its importance, observability is typically only considered a necessity after an application has been deployed to production.

Actually, all software engineering organizations can also benefit from implementing observability during the development process, well before an application is deployed. Pre-deployment observability entails monitoring and metering the software development life cycle (SDLC), with the goal of optimizing the speed and output of the process while not negatively affecting the quality of the application being developed.

In this post, you’ll learn more about the benefits of pre-deployment observability and how it can lead to better software development outcomes.

What is observability?

Critical decisions, and maybe all decisions, are made better with data. When managing software applications much of the data comes in the form of logs, traces, and performance metrics. Tools such as Datadog, New Relic, and Dynatrace help DevOps and Support personnel observe how an application is performing and gain deeper insight into the state of their software.

For instance, they can detect problems in application error logs, then determine what triggered the problem, why it happened, and when it happened. And this abundance of data significantly helps when deciding how to fix it.

The key to being able to use an observability platform is data. There needs to be a lot of it and it needs to be gathered, collated, sorted, and presented in a way that provides visibility and assists in the critical decision making process. Used properly, observability platforms provide users with a unified view of their operational data, allowing them to discover the unknowns in their dynamic systems as well as resolve issues with actionable insights.

What is pre-deployment observability?

Pre-deployment observability is a relatively new practice among software development teams, and it delivers many of the same benefits found when observing production applications. At its core, it provides insights into the performance of the application development process. In many enterprises the performance of the SDLC is left to chance, as there is little or no proactive monitoring and even less process optimization.

That leads to the ills that plague most teams building complex software: missed deadlines, over budget projects, error-filled applications and burned out resources. And as software development becomes more federated, with teams and tools spread across time zones, the problems faced by those organizations are even more exacerbated.

Pre-deployment observability allows software development leaders to collect and analyze data from an application while it is being developed, so they can not only ensure it is built in an efficient manner but also so they can determine how the application will behave before it’s deployed. Introducing observability earlier in the process enables teams to identify practices that result in better applications, such as ensuring proper testing in a way that eliminates errors that might otherwise have been missed before pushing a project to production.

How pre-deployment observability works

Pre-deployment observability, just like traditional observability, relies on data: primarily metrics, logs, and software development transactions. However, unlike what most teams do today, instead of gathering that data from the platforms hosting the application and the application itself, the data for pre-deployment observability comes from the tools used to build the application. Specifically the project management, code repository, error tracking, CI/CD, and application monitoring tools used by the various teams involved in producing the software.

There is a significant amount of data created by teams as they build enterprise applications. Think about the number of tasks, lines of code, rounds of reviews, bugs, errors, and the like that occur during a months-long development process. Using an SDLC observability platform, such as Treno, enables all of that raw data to become valuable metrics, trend analyses, observations, and improvement recommendations. For example, SDLC observability can identify the normal pace of development and alert you when a team falls below their norm. Conversely, it can alert you when speed increases but causes an inordinate amount of bugs and errors.

Similarly, leveraging observability during the development process can limit issues found after an application is deployed. By measuring the code review and quality assurance process, organizations can ensure that code is tested before it is merged, helping reduce the number of incidents, downtime, and lost opportunities in production. Visibility into SDLC performance patterns helps software engineering teams identify current problems as well as predict errors and solve them before they become application issues occurring in production.

Implemented correctly, pre-deployment observability can help teams detect risks that are not measured by established metrics and that would regardless be increasingly difficult to understand without additional context.

SDLC observability combines the targeted performance metrics used in monitoring practices with deeper telemetry data to show bigger patterns and linkages between occurrences. That facilitates speedier identification and troubleshooting of process bottlenecks, assists teams in improving their delivery performance, and helps organizations produce better software, on-time and on-budget.

Why pre-deployment observability matters

Pre-deployment observability can make a big difference to the success of a software development organization. When teams only focus on observability after deployment, the result can be an increase in costs and time to deployment, which no engineering organization wants. There are multiple problems that teams may face if they don’t implement observability earlier:

• Missed deadlines: After a team deploys an application they may end up dealing with errors in production. That increases the complexity of the application, making it more difficult for the team to fix the errors and meet the project deadline.
• Less flexibility: If errors are discovered late in the software development process, teams are more limited in terms of what they can do to resolve the errors.
• Lower quality: Unanticipated problems in production can prevent the application or service from meeting its service level objectives (SLOs) and lead to unacceptably poor results.
• Unsatisfied customers: Unexpected errors in production can lead to downtime and unavailable services, lowering the quality of the application and not delivering value to customers.

Using a traditional approach doesn’t just affect the final product. It affects the performance of the team as well, leading to these potential challenges:

• Developer burnout: When an error occurs, developers are pressured to look into the past for the source of the problem and solve it. That increases the complexity of the team’s work, which can become too much for them to handle and result in burnout.
• Missed market opportunities: Because teams need to focus on resolving existing errors or problems, they will spend less time developing software that meets the needs of the market.
• Slower innovations: If teams are spending a lot of time fixing past problems, they won’t be able to focus on improving the product or looking at possible innovations for future versions.
• Increased technical debt: The additional work required to fix past problems can lead to a longer, more difficult workflow. Teams may feel obligated to choose the easier solution right away rather than planning a better approach that takes longer to implement.
• Higher support costs: Teams have to spend extra money to fix an error-prone system by tracing the origin of the error, instead of spending to improve the product for the future.

Teams that use pre-deployment observability will know ahead of time what to expect from their application. They’ll be able to detect problems that might arise in production and will know how to solve those problems. They’ll have a fuller understanding of their service and can focus more time and resources on improving it than fixing it.

Conclusion

Pre-deployment observability is a useful strategy for understanding the overall condition of your infrastructure before production. Using observability practices earlier in your SDLC will help eliminate uncertainty and lead to a better experience for your organization and your customers.

If you’re looking at options for a pre-deployment observability tool, consider Treno. Treno is an observability platform that imports data from all your SDLC tools into one place and enables you to easily find and fix problems by revealing trends in software development speed, quality, output, and developer health. It also offers observations and recommendations to optimize production so that you can release software faster, decrease costs, and increase revenue.