The Importance of Observability in Software Testing
The demands of today’s application ecosystem and accelerating digital transformation bring Observability into the focus. Observability provides deep visibility into application behavior and tech ecosystems and enables faster and more informed decisions. Lacking observability, software development teams tend to make assumptions about the production system behavior, potential performance bottlenecks, or future failure scenarios. The guesswork leads to wasted time in identifying the cause and impact of failures and their potential fixes. This cost of guesswork increases further if customers get impacted
The observability process becomes helpful here for software teams as it provides complete visibility over the entire tech stack. It allows teams to identify and isolate issues across the software, often from a single application.
What Is Observability?
Software observability gives software teams and testers eyes over the critical telemetry data of events, logs, traces, and metrics. Observability turns the lights on for software testing teams. It allows them to see each component with context that enables performance tuning.
Traditional monitoring systems fail in today’s software environment as the tech stack is constantly evolving and new-age cloud-native applications increase in the application portfolio.
With observability, software teams can measure a system’s current state based on logs, metrics, and traces. This lets them identify how, why, and where an application malfunctions. Moreover, it provides incredible insights into the deep internal parts of applications and systems. As a result, testers can ask complex, improvement-focused questions such as where is the system fragile or what are the common points of failure.
Is There a Difference Between Monitoring and Observability?
From the looks of it, observability and monitoring can seem notoriously similar. But are they? While they are related and complement one another, they are different concepts.
Most monitoring tools identify or troubleshoot IT issues. These, however, only reveal performance issues or abnormalities that a testing team can anticipate. As applications become cloud-native and cloud environments become more commonplace, security and performance issues become more multi-faceted and often unpredictable.
Monitoring answers the simple questions - “What’s broken, and why?”. It enables teams to measure a system’s state by leveraging a predefined set of metrics and logs and allows them to detect a known set of failure modes. Simply put, monitoring is essential as it enables teams to learn how apps are functioning, growing, and being utilized. However, monitoring complex distributed applications can be challenging as production failures are not linear and hence, harder to predict.
Observability, by contrast, uses logs, traces, and metrics collected across the entire IT infrastructure. It allows teams to proactively identify potential issues and debug systems. More profoundly, it allows IT teams to measure all inputs and outputs across applications, microservices, servers, databases, etc. Observability, as such, provides more real-time and actionable insights and helps teams move from a reactive to a proactive stance as compared to monitoring.
Why Is Observability in Software Testing Important?
Reports show that “the global cyber-attacks Rose by 7% already in Q1 2023. It is estimated that 560,000 new pieces of malware are detected every day and that there are now more than 1 billion malware programs circulating”
Observability is essential in software testing today as application ecosystems are becoming increasingly large and distributed. Security threats and vulnerabilities are also increasing in sophistication, and the need for establishing zero-trust security models is on the rise.
Observability leverages the following to gain insights:
- Logging: This provides a record of events that helps developers and testers trace the root cause of issues and identify system behavior.
- Metrics: This provides quantitative measurements of a system’s performance; it helps developers identify system performance in real-time and identify issues before they become critical.
- Traces: These include critical information on the end-to-end flow of requests and transactions in the system. Traces provide a detailed understanding of the system’s behavior that aids in problem identification and resolution.
Some key reasons why observability becomes crucial for software testing are:
Better Response to Incidents and Issues
Observability provides insights into both user behavior and usage to protect systems from unauthorized access. The consistent logging delivers insight into any abnormalities within the system and is not restricted to health or performance-related issues.
Removes the Guesswork from the Process
Observability provides an understanding of the relationships between the systems, the variables, and all influencers. As a result, it delivers a cohesive and complete picture of application health and potential points of failure. Observability identifies the first signs of abnormal performance. Teams can then pinpoint bugs or vulnerable attack vectors without any guesswork and with clear, actionable insights.
Saves the Developer Time
Observability saves developers a lot of time as it combines data from logs, metrics, and traces with rich context. This provides the understanding needed for performance tuning or bug fixes.
Developers can understand why something happened in their environment without having to spend time exploring, filtering, and grouping data and information. As such, developers and testers can get to the root of application performance issues much faster. This includes issues that arise from cloud-native and microservices environments.
Since observability provides deeper visibility into possible and potential challenges, it gives testing and development teams the capacity to automate more processes. Observability also helps development teams to leverage and interpret the observable data during the software delivery life cycle. This accelerates innovation and allows teams to build more secure, resilient, and performance-driven applications.
By delivering deep and clear insights, observability helps increase efficiency and innovation among operations, application development, and testing teams.
Speedier Market Releases
The time-to-market accelerates vastly with observability in place. With observability, development and testing teams can:
- Spot and resolve issues faster
- Identify ways to optimize the end-user experience
- Evaluate the right infrastructure needs to boost performance
It allows organizations to combine business context with full-stack application analytics and performance to understand the business impact and ensure that the software meets business goals. Teams can then make more confident and calculated decisions with these clear insights and release code faster. Speedier market releases become an organic consequence of the benefits delivered by observability.
How to Include Observability in the Testing Process?
Observability in testing allows testers to uncover granular details about system issues, ask deep questions, and explore the system.
It facilitates testing in production and provides comprehensive information on the production environment and the infrastructure.
Observability can be added to the testing process by employing logging to track events before and after any operation in code. Debugging tools also increase observability in testing as they allow testers to add breakpoints and steps through the code to identify issues during testing. This makes it easier to reproduce difficult issues for further testing and performance engineering.
Introducing monitoring tools for real-time insights into application performance report usage, error rate, etc., also improves observability and testing outcomes. The role of an intelligent test automation platform in increasing observability is also paramount, as it allows testers to test fast and test often. Increasing the testing velocity and footprint and complementing that with rich test data and insights also improve observability and, consequently, application performance.
What are Some Challenges to be Aware of?
Observability can be added to the testing process seamlessly to add more velocity to testing. Unlike traditional software testing that focuses on validating the system’s correctness, today’s testing demands services to be run inside the production environment. Therefore, covering and predicting every failure that may occur otherwise is impossible. Observability takes care of this by giving an in-depth view of the infrastructure and production environments.
To do this, however, testing teams need comprehensive test automation platforms. These must facilitate A/B testing and enable fast, continuous testing and monitoring of the production environment. Writing clean, understandable, and testable code at scale is another challenge to navigate.
Test automation platforms also have to ensure that they can easily validate API layers and automate all aspects of the quality lifecycle while also providing API test case management, planning, execution & tracking governance. CI-driven regression suite execution, API testing with regression maturity, cross-device testing capabilities, and expansion of the test cases with minimal effort become critical for driving observability success.
How Can ACCELQ Help Alleviate These Challenges
Including observability in the testing process will soon become critical. Research shows that people using observability will detect any issues 2.1 times more and report a 69% better MTTR. Connect with us to see how our AI-powered, no-code testing platform can allow your teams to build observability into your testing process and deliver robust, reliable, and high-quality applications to the market faster by making your testing three times faster!
Content Specialist at ACCELQ
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