Observability-Driven Development: The Art of Stalking Your Own Code

We are currently living in the age of the fourth industrial revolution in which groundbreaking designs and applications that implement Artificial Intelligence, the Internet of Things, Web3, Blockchain, Genetic Engineering, and Quantum Computing are being introduced.

The levels at which these projects are innovated require a real-time assessment of the errors in the code to ensure no time is lost in debugging and fixing issues. We need reliable, scalable, user-friendly, and compatible software for all these advancements, as user satisfaction and cost-effectiveness are paramount. Hence we must look at observability and how observability-driven development can help keep up.

Observability-Driven Development is an efficient, time-saving, and practical approach to software development and maintenance in the newer generation of revolutionized technology because the complexity of the more recent software and applications demands more complex developmental strategies.

What is Observability and Observability-Driven Development?

Observability promotes the collection and analysis of data regarding the software’s performance, health, conditions, stack state, and other attributes. It leads to faster identification of problem types and encourages a more streamlined and effective way of debugging and modifying the source code. This collection and analysis are done throughout the development of the application and not only in one stage.

Why is ODD an Efficient Add-on to Standard TDD?

Previously, Test-Driven Development had been the foremost and standardized way of checking for errors in the software. The process also involved a cycle, but a dedicated stage to the development cycle was carried out solely to look for problems in the code. Although this method returned accurate results, the cycle would have to be restarted after applying the desired modifications to reanalyze and fix any other problems that may have rooted after the fixes were implemented.

Observability-Driven Development helps reduce the time complexity of software analysis and modification by making the necessary adjustments throughout coding, debugging, and executing. Paired with the standard TDD, the developmental time of software is greatly reduced, leading to a much faster approach toward the deployment phase.

Also, don’t forget maintenance; maintenance is an essential aspect of software development and involves making changes to a software system after it has been released. It encompasses fixing bugs, improving performance, enhancing features, and upgrading the software to newer platforms or technologies. Observability is an essential aspect of software maintenance that enables developers to monitor the behavior of a software system and detect issues as they arise. It involves collecting and analyzing data from the system to gain insights into how it is performing and identify areas that need improvement.

Observability-driven development paired with test-driven development helps reduce the time taken to finish designing the application so that it may be ready for release sooner while simultaneously making sure the application is tested to be bug-free and easily maintainable.

Some Examples of Observability That Can Be Implemented in Software Development

Many creator and dev tools are very efficient in visualizing and depicting observability in software engineering. A graphical view of every feature of the code makes it easier for the team and developers to understand the make of the system. Here are some ways observability-driven development can be seen in software engineering:

Real-Time Monitoring

Data collection from the application can be logged and depicted in graphical forms using line graphs, histograms, bar charts, heat maps, spectrums, and other diagrams, depending on the engineer's requirement. These graphs can help realize the error percentage, user count, response time, latency between client and host, and much more. Examples of such tools are #datadog , #dynatrace and LogicMonitor.

Distributed Tracing

Some tools allow software engineers to trace the path of a request as it moves through various microservices, components, and systems within a distributed application. The goal of distributed tracing is to provide developers with visibility into the interactions between multiple components in a distributed system and to help identify issues such as performance bottlenecks and errors. Examples of such tools are Zipkin, Lightstep, and Honeycomb.

Alerts

A system could encounter a fatal error while executing a program. To let engineers know that there is something wrong with the process help prompt engineers to take a closer look and identify the real-time problem that appeared.

What are the benefits of Observability-Driven Development?

As seen above, superior observability-driven development has multiple benefits over standalone test-driven development. If we look further into its many merits in this modern era, we'll have a general idea of how much things will get easier for software engineers. The facilitations that observability-driven development brings along are:

Detection of Errors During All Stages of SDLC

With observability-driven development, errors are caught as the software development life cycle stages are executed. This ensures that at the time of execution, there are no errors that need to be addressed. This lightens the burden on software engineers and allows them to focus more on designing and execution rather than debugging and modifications.

Optimized System Performance

Now that software engineers are aware of performance metrics and logs because of the visualization of errors during the different stages, the bottleneck occurrences are whittled down to seldom, and system performance is brought out to its maximum capability. The optimization of system performance is made much simpler and quicker than traditional methods.

Faster Debugging

With new and improved software tools being introduced every week, identifying root cause issues in software becomes much easier and faster.?

Better Team Collaboration

Since the collection and analysis of data are done during all the different stages of software development, the errors in the application can be done at the same time by the operations, development, support, and all other teams involved. Communication strengthens the collaboration of the teams and working together to resolve issues becomes a breeze. This continuous outflux of information allows for a speedy, nonetheless efficient, code polishing from the ground up.