Upgraded Forecasting: Predicting When Dependencies Become a Liability

Managing dependencies within a software system is one of the most critical aspects for the stability, scalability, and security of software systems in today’s enterprise landscape. As applications grow, dependencies, whether they emerge from libraries, frameworks, or platforms, provide a foundation for further development or can represent a significant risk. Over time, the legacy cost of maintaining, upgrading, or replacing a dependency can accumulate into a risk that can manifest in a system’s vulnerability, performance allocation, and or development overhead. Recognizing and predicting when a dependency is no longer delivering value and has become a liability is essential to effective infrastructure management.

This blog outlines the principles and practices for predicting risks associated with dependencies, including tools, frameworks, and techniques for detecting and classifying infrastructure dependency hazards before devoting time or resources that might later have operational repercussions due to dependency liabilities.

Understanding the dependency lifecycle

Dependencies in software projects generally evolve through a lifecycle. They start with the value-additive functionality of enabling development to happen at speed, which could also mean adding functions or technical gaps. Over time, dependencies can become out of date, no longer supported, or unable to work with new versions of the infrastructure components or systems that are adopted. Understanding dependency life cycle phases is essential for forecasting risk:

Active development: In active development, the dependency is released when features, bug fixes, and security patches are made available. Each time an update is released, the developer benefits from the improvements and can work with the newest version of the platform. As the number of updates continues to trend downwards, and the dependencies continue to get older and potentially more dated, the level of risk is increasing.

Mature Stage: Once a dependency reaches a mature state, its updates may become less frequent. At this point, it still serves its purpose but may not receive as much attention from maintainers. The risk begins to grow as updates slow down, and the dependency starts to age.

End of Life (EOL): At some point, a dependency has been in development long enough that the maintainers deem it as EOL, or no longer viable, support, etc. This is a risk level, as you are no longer receiving updates or patches, or fixes to vulnerabilities we know of. But the next stage is a much higher risk from a security and operational standpoint.

Legacy: Once a dependency reaches the Legacy stage, it is very likely to still be in use, particularly in an unsupported use case. Parts of the support may have been labeled as unsuitable for “active” use - indicating it may work, but is in no way guaranteed to be usable. In a number of cases, a legacy dependency is still working because there are no alternatives available for use, or it has simply been retained out of technical debt. The longer dependencies remain operating as a legacy system, the more difficult it will be to update or replace, particularly in cases where the dependency has become so intertwined with the developed core of the infrastructure.

Determining Signs of a Dependency at Risk

This is a particular challenge, as a dependency might seem useful right up to the point where it deteriorates or is not compatible with our infrastructure anymore, and preparing for that time situation is difficult for all of us. However, there are some signs to look out for, which could tell you how much of a liability the dependency has become.

Update Frequency and Change Logs: Ideally, we want to see active, frequent updates to a dependency, and ideally, there are change logs that signify an active developer. If a dependency suddenly experiences a dramatic decline in updates or stops providing change logs, this could indicate “stopping” or “never-moving” development, and should spark inquiry internally about whether maintainers are still pushing the dependency at all.

Vulnerability Exposure: The longer a dependency has existed before patching an appropriate fix to a known security vulnerability, the higher the need to consider “risk”. For this reason, scanning for known vulnerabilities, as well as an overall scan or review of the vulnerability posture, should be performed regularly. If the dependency maintainers are unresponsive or if no patch exists, it may be time to look into alternatives.

Compatibility with Current Infrastructure: Technology services are constantly changing and evolving. Because of this, dependencies can become incompatible with technology, i.e., versions of programming languages, databases, or operating systems. Incompatibilities lead to poor operational performance or unstable operational environments.

Technical Debt and Maintenance Overhead: A dependency that does, in fact, require a large amount of customization, patching, or manual monitoring can build a large amount of technical debt. Or, if the maintenance cost of a dependency becomes more than its maintenance value, then it is worth considering replacing it. Properly identifying that inflection point earlier creates less effort later.

Community Engagement, Ecosystem Health: A healthy and active community is usually a good sign that a dependency is likely to survive. Community engagement lowers when there are no longer forum posts, GitHub commits, or issues being assigned in the repository, implying the dependency may no longer be supported or evolve.

Data and tools for predicting risk with dependencies

Predicting the point at which a dependency becomes a liability is more of a data process involving continuous monitoring of relevant metrics and predictive tools; an organization could develop a high-level understanding of the lifespan of its dependencies. Numerous tools and methods can assist with the above:

Automated Dependency Management Tools: Dependabot, Renovate, and Snyk are examples of tools that can be automated to monitor dependencies in your code base and notify your team of any potential issues, such as outdated dependencies, out-of-date due to vulnerabilities. Such tools will generate reports and suggest upgrades or replacements for dependencies nearing end-of-life.

Vulnerability Scanners: Security-focused tools such as OWASP Dependency-Check, Trivy, and Anchore will scan dependencies for known vulnerabilities, and will generate alerts should a dependency have an unresolved security issue. These tools will help teams determine risk and determine priority for addressing the risk if that dependency is needed. Regular scans should help forecast potential vulnerabilities before they become significant.

Versioning and Compatibility Checking Tools: Compatibility checkers, such as PyUp, npm audit, or Ruby’s Bundler, will ensure the versions of the dependencies are aligned to the supported versions of the organisation’s infrastructure components by monitoring API changes and expectations for backward compatibility. These tools can help forecast when the dependency will no longer integrate well with the evolving platform.

Dependency Lifecycle Management Platforms: If the infrastructure has grown large enough, component tracking and dependency lifecycle management platforms such as Black Duck or JFrog Xray can track the full supply chain of dependencies and provide visibility into the current state and vulnerabilities, as well as historical information on the misconduct of the dependencies. These types of platforms offer deep analytics and can forecast ongoing risk by using the dependency.

Forecasting and Mitigation Strategies

Once possible risks have been identified, the next step is to create a plan for dependency management. Some suggested strategies could include:

• Implement regular dependency audits as part of your software development lifecycle (SDLC). Regular audits will help your team keep ahead of possible risks, and should be scheduled events as part of the SDLC (i.e, quarterly, biannually) to create a baseline of the current health of all dependencies, and to check if they are still aligned with the project goal.

• Create a deprecation policy for all major dependencies. This policy will help your team manage technical debt and will create expectations and clear road maps for when you will retire or replace dependencies in your codebases. When replacements are clearly defined, it not only reduces the potential for sudden breaks in service but also lowers the costs associated with making the migration.

• Fallback strategies into place, and improve resilience for critical dependencies that will not easily be replaced. Simply maintaining parallel versions, sandbox development environments, or abstracting dependencies, your team will want to avoid tight coupling from the core application logic.

• Conducting regular integration and regression testing against newer dependency versions will better define when breaking changes are made or versions become incompatible. Using continuous integration tools to handle the testing against new dependency versions automatically, your team can route any issues before going into production.

Conclusion

Forecasting when your dependencies become a liability is not an elective, but a required practice for all organizations wishing to manage their infrastructure efficiently and securely. Risks can be proactively identified, apply data-driven tools to forecast dependency life cycles, reproduce mitigation strategies, reduce operational parasitic overhead, and mitigate to risk of failure.

Failing these actions and the potential for organizational disruption, Downtime, breach, repair of growing technical issues, and cost ignored will lead to the inability to maintain operational efficiency and integrity with your customer trust.