Everything You Need to Know About the 4 Stages of Software Reliability

FYI – The maturity model presented in this post is based on the concept of Continuous Reliability, which you can read more about here.

Software reliability is a big deal, especially at the enterprise level, but too often companies are flying blind when it comes to the overall quality and reliability of their applications. It seems like every week, there’s a new report in the news calling out another massive software failure. Sometimes it’s just a glitch on social media causing usability issues, and other times it’s a serious issue in an aircraft system that leads to deadly crashes.

Clearly, not every software failure is fatal, engineers aren’t heart surgeons. However, a single error can impact more patients than a doctor could ever treat in their lifetime. That’s why maintaining application reliability (basically, making sure nothing breaks) is a top priority for every IT organization. And if it isn’t, it should be.

In this post, we will discuss the concept of Continuous Reliability and use it to define the Continuous Reliability Maturity Model.

This model helps teams understand where they stand in terms of reliability and how they can improve. It can also help engineering leaders to chart a course to reach their goals for reliable and efficient execution. But more on that later, let’s dive in.

Continuous Reliability is the idea of balancing speed, complexity and quality by proactively and continuously working to ensure reliability throughout the software delivery lifecycle (SDLC). It is ultimately achieved by implementing data-driven quality gates and feedback loops that enable repeatable processes and reduce business risk. 

To do this requires strong capabilities in both data collection and data analysis, meaning being able to access all relevant information about your application and then being able to use that data to proactively surface patterns and prevent software failures. 

Achieving Continuous Reliability means not only introducing more data and automation into your workflow, but also building a culture of accountability within your organization. This includes making reliability a priority beyond the confines of operations roles, and enforcing deeper collaboration and sharing of data across different teams in the SDLC.

The Continuous Reliability Maturity Model is comprised of four levels that align with common patterns of obstacles and pitfalls organizations encounter on their reliability journeys. Below we break down the characteristics and challenges that define each level and provide recommended next steps that will help advance your progress.

As organizations progress in their reliability maturity, they increase their signal to noise ratio, automate more processes and improve team culture. With this, they are able to increase productivity and provide a better customer experience, improving the overall bottom line for the business.

Let’s take a closer look at each reliability level:

Organizations at this level are just beginning their reliability journey. This stage is marked by the initial establishment of reliability practices – often leaning toward manual and reactive processes with loose structure. Teams at this stage generally rely on ad-hoc and inconsistent strategies to solve technical issues. Visibility is a major challenge, and most code quality problems are only addressed if a customer complains.

Characteristics: Ad-hoc processes for solving technical issues; early or experimental stages of prioritizing and formalizing reliability strategy; limited visibility into application errors and their root cause.

Primary Challenge: Manual and reactive processes and limited visibility into what’s happening within your applications and services, resulting in late identification of customer impacting issues.

At this stage, teams have established a basic structure with some troubleshooting processes. Application visibility increases as huge amounts of data become accessible through expanded tooling, but the ability to separate the signal from the noise becomes a main challenge as teams seek to better understand which issues have the greatest impact on reliability.

Characteristics: Established processes for incident response and QA; some automation across the SDLC; marked reduction in the number of incidents reported by customers; increased visibility into your system through tooling and processes results in higher volumes of alerts.

At this point, teams are better able to focus their efforts on issues that matter. They have anomaly detection capabilities that help to manage alert fatigue. But despite the seemingly endless amounts of data being collected, issues are still missing context and errors still make it to production. Technical debt remains a mystery.

Characteristics: Reduced alert fatigue due to applied intelligence and added context to existing data; established processes for routing issues to the right people at the right time; increased confidence in processes, tools and team structure; still experience critical production issues that catch you by surprise and you struggle to resolve. 

Primary Challenge: Broken feedback loop between production and pre-production due to data blind spots (unknown unknowns).

This is the most mature stage of reliability, but our work doesn’t end here. At this level, teams have access to nearly all of the relevant data they need to troubleshoot issues quickly and to monitor reliability based on collected metrics.

Quality gates are set up between the stages of development to automatically block the progression of unreliable code. Feedback loops are also streamlined to ensure that software quality is not only stable, but improving over time and easy to measure. Main challenges at this stage are consistent execution by team members based on the available data and analysis capabilities.

Characteristics: Established processes; ability to capture deep contextual data that fuels feedback loops between teams and stages of software development and delivery.

While APMs and log analysis tools take a top-down IT Ops approach for reliability, focusing on trace-level diagnostics (symptoms), OverOps captures bottom-up code-level diagnostics (causes) at a lower-level than was ever thought possible. 

By analyzing all code at runtime in any environment from test to production, OverOps enables teams to identify and prioritize any new errors, increasing errors, and slowdowns using unique code fingerprints. 

Once an anomaly is detected, the exact state of the code and the environment – source code, variables, DEBUG level logs, and full OS/container state are delivered to the right developer, before customers are impacted.

Learn more about how OverOps can help you on your reliability journey.

Source: Overops.com

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