Under/oversized dependencies refer to the use of third-party libraries that do not align well with a software project's actual needs, either because they are too large and feature-rich or because they are too minimal and lack necessary functionality. Both extremes can introduce risk and inefficiencies into your development process.

Oversized dependencies typically include libraries that offer far more functionality than your project requires. While this might seem convenient at first, it often leads to bloated software, unnecessary complexity, and a significantly larger attack surface. These libraries can also introduce maintainability challenges, as you're responsible for tracking updates and security issues across a much broader set of code than you actually use.

On the other hand, undersized dependencies may provide only the most basic features. While lightweight, they often require developers to piece together multiple additional components or write custom integrations. This approach can quickly increase development effort and complexity, making the codebase harder to secure and more prone to vulnerabilities.

In both cases, whether the dependency is too large or too small, the result can be software that is harder to maintain, more vulnerable to security threats, and potentially reliant on libraries that are not well-supported or suited to the long-term goals of the project.

In this lesson, you will learn how under/oversized dependencies can affect your application’s security and efficiency. Through an example scenario, you'll understand the risks and practical steps to evaluate and manage your project dependencies wisely.

Stanley, a developer at a fast-moving e-commerce company, is building a new microservice to generate order statistics. To include a basic bar chart, she picks the library MegaChartPro, which is highly rated and widely used.

After integration, the microservice works but the startup time is unusually slow. Stanley runs some diagnostics.

NOTE: The output below is simulated for teaching purposes. In practice, you'd need to add logging and use tools like process.memoryUsage() or profiling tools to collect these metrics.

Later in the sprint, Stanley runs a security check using Snyk. Although her service only uses basic bar charts, Snyk flags a high-severity vulnerability in an unused submodule of MegaChartPro.

To stay compliant, Stanley must update MegaChartPro frequently, even though the updates have nothing to do with her use case. Each update introduces risk. He starts to ask himself, "How much effort is justified for using a feature-rich library when you're only using one small part of it?"

In the end, Stanley decides to switch to SimpleChartsLite. It requires refactoring the chart integration, but the microservice becomes leaner and more perform better.

To understand how under/oversized dependencies affect applications, let’s examine what happened in Stanley’s microservice.

When Stanley integrated MegaChartPro the library included a vast array of features beyond her project’s needs. The library's initialization process loaded modules for animations, 3D rendering, and live data feeds, even though none of these were required. This significantly increased the microservice's startup time and consumed unnecessary memory and processing power, leading to a reduction in the service’s performance.

Although Stanley’s microservice used only a fraction of MegaChartPro’s features, a vulnerability in an unrelated module still posed a compliance risk. Large libraries often bundle unused code, broadening the attack surface. For example, an unused module that handles sensitive data or insecure connections can still be exploited by attackers, even if it is irrelevant to the developer's implementation. This highlights the hidden dangers of using libraries with unnecessarily broad functionality.

Libraries with extensive feature sets often receive frequent updates, many of which address features irrelevant to specific use cases. Each update required regression testing in Stanley’s project to ensure compatibility, creating additional work and slowing development progress. This kind of maintenance overhead can quickly burden development teams and detract from core efforts to build and enhance applications.

Switching to a smaller dependency, like SimpleChartsLite, was a good decision for Stanley’s project, but it wasn’t without cost. Replacing MegaChartPro required significant code refactoring and re-testing to align with the new library's API. This demonstrates how selecting the right-sized dependency from the start is critical to reducing the need for costly and time-consuming transitions later.

In this example, the use of MegaChartPro brings along unnecessary features such as animations, live updates, and 3D rendering, which are irrelevant for a simple bar chart. These additional functionalities consume resources and slow down the microservice.

This Python code uses a library designed for high-complexity visualizations, despite the project only needing basic functionality. The unnecessary options inflate the application size and risk profile.

The Java implementation suffers from inefficiencies, with unneeded options included by default. This creates performance overhead, making the library unsuitable for lightweight applications.

In PHP, the inclusion of a heavyweight dependency like MegaChartPro adds complexity and risks, especially if vulnerabilities exist in unused parts of the library.

This C# example demonstrates multiple inefficiencies. Unnecessary features like Animation, LiveUpdate, and ThreeD are enabled by default, making the dependency overkill for a basic use case.

In Go, the inclusion of MegaChartPro leads to inflated binary size and increased complexity due to the unnecessary inclusion of features irrelevant to the bar chart functionality.

In C++, the use of MegaChartPro includes unnecessary runtime dependencies and introduces potential performance bottlenecks due to the library's focus on advanced features like 3D rendering and live updates.

Oversized dependencies, which include extraneous features, can bloat applications, resulting in slower performance, higher memory usage, and longer startup times. These inefficiencies can be especially detrimental in performance-critical or resource-constrained environments like embedded systems or cloud-based microservices.

On the security front, over-sized dependencies broaden the attack surface by including unused code that may contain vulnerabilities. Even if unused, these vulnerabilities can compromise compliance with security standards or be exploited by attackers. For instance, a vulnerability in a rarely used module of an oversized library could expose sensitive data or enable unauthorized access.

Conversely, undersized dependencies can create functionality gaps, requiring developers to write additional code or integrate supplementary libraries. This increases development time and introduces potential bugs or security flaws. The lack of sufficient functionality can also lead to fragile implementations that are harder to maintain and scale.

Finally, both types of mismatched dependencies impose maintenance burdens. Over-sized dependencies may receive frequent updates for features irrelevant to the project, requiring unnecessary testing and validation. Under-sized dependencies may lack adequate updates or community support, leaving critical issues unresolved.

In the worst-case scenario, these issues can lead to cascading failures, missed deadlines, or even significant security breaches. Developers must carefully evaluate and select dependencies that align with their project’s scope to minimize these risks.

Mitigating the risks associated with under/oversized dependencies requires a proactive and systematic approach throughout the software development lifecycle. The key is to ensure that each dependency is a right-sized fit for the project's requirements while maintaining security and performance standards. Here’s how you can achieve this:

Dependency evaluation

Before integrating any library, evaluate its features and ensure it aligns with your project’s needs. Avoid dependencies that include excessive functionality you don’t plan to use or those that provide too little, requiring additional coding or workarounds.

Dependency minimization

Adopt the principle of "minimal viable dependency." Use libraries that are specifically designed for the functionality you need. Lightweight libraries not only improve performance but also reduce the attack surface, lowering the chances of vulnerabilities affecting your application.

Regular audits

Conduct regular dependency audits to identify unused or over-sized dependencies. Dependency bloat can accumulate over time, so periodic reviews can help streamline your software stack and remove redundant or risky libraries.

Custom libraries

For highly specialized or minimal requirements, consider building custom libraries. While this approach can increase initial development time, it eliminates the risks and overhead associated with external dependencies.

Security practices

Incorporate security practices such as vulnerability scanning, using signed dependencies, and ensuring only trusted sources are used for libraries. This prevents accidental inclusion of malicious or compromised dependencies.

In this example, SimpleChartsLite provides only the essential functionality for rendering bar charts, reducing memory usage and startup time. It avoids unnecessary features like 3D rendering and live updates, aligning perfectly with the project's needs.

By using SimpleChartsLite, the Python code avoids importing unnecessary modules. This streamlined library reduces the risk of vulnerabilities and ensures better performance for basic charting requirements.

The Java code benefits from SimpleChartsLite's lightweight API, which eliminates the overhead associated with the unused features of MegaChartPro. The library’s focus on simplicity ensures that only relevant functionality is included.

In PHP, switching to SimpleChartsLite reduces complexity and mitigates security risks by using a dependency that includes only essential features for bar chart rendering.

With SimpleChartsLite' the C# implementation becomes cleaner and faster. It avoids the maintenance overhead and performance bottlenecks of an over-sized dependency.

In Go, the lightweight SimpleChartsLite library ensures minimal memory and CPU usage, improving the efficiency of the application.

The C++ implementation becomes leaner and more efficient by using SimpleChartsLite, ensuring that only the required functionality is included, without unnecessary overhead.

To deepen your understanding of dependency management and secure software development, explore the following resources:

• Snyk Open Source Scanner Docs: Automatically find and fix vulnerabilities in open source libraries.
• The Pragmatic Programmer (Book): A timeless guide to best practices in software development, including dependency management.