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In this blog post, we’ll explore all the angles of software complexity: its causes, the different ways it manifests, and the metrics used to measure it. We’ll also discuss the benefits of measuring software complexity, its challenges, and how innovative solutions like vFunction transform how organizations can manage complexity within the software development lifecycle. First, let’s take an initial go at defining software complexity in more detail.

What is software complexity?

As mentioned previously, at its core, software complexity measures how complex a software system is to understand, modify, or maintain. It’s a multi-dimensional concept that can manifest in various ways, from convoluted code structures and tangled dependencies to intricate and potentially unwarranted interactions between components. Although software projects always have inherent complexity, a good question to ask is how software becomes complex in the first place.

Why does software complexity occur?

Although it sounds negative, software complexity is often an unavoidable byproduct of creating sophisticated applications that solve real-world problems.

A few key factors make software more complex while creating these solutions.

Increasing scale

As software systems grow in size and functionality, the number of components, interactions, and dependencies naturally increases, making the application more complex and more challenging to grasp the overall picture.

Changing requirements

Software engineering is rarely a linear process. Requirements evolve, features get added or modified, and this constant flux introduces complexity as the codebase adapts, which may support the overall direction of the application but introduce complexity.

Tight coupling

When system components are tightly interconnected and dependent on each other, changes to one component can ripple through the system. This tight coupling between components can make the application more brittle, causing unforeseen consequences and making future modifications difficult.

Lack of modularity

Typical monolithic architectures, where all components integrate tightly, are more prone to complexity than modular designs. Modular applications, such as modular monoliths and those built with a microservices architecture, are more loosely coupled and can be modified independently and more efficiently.

Technical debt

Sometimes, software engineers take shortcuts or make quick fixes to meet deadlines. This “technical debt” accumulates over time, adding to the complexity and making future changes more difficult. It can involve both code and architectural technical debt and any piece of the application design or implementation that is not optimal — adding complexity that will generally cause issues down the line.

Inadequate design

A lack of clear design principles or a failure to adhere to good design practices can lead to convoluted code structures, making them harder to understand and maintain. For example, injecting an unrelated data access layer class to read one column of a table instead of the corresponding facade layer/service layer class. Applications should follow SOLID design principles to avoid becoming complex and convoluted.

How is software complexity measured?

Measuring complexity isn’t an exact science, but several metrics and techniques provide valuable insights into a system’s intricacy. By assessing the system in various ways, you can identify all the areas where it may be considered complex. Here are some common approaches:

Cyclomatic complexity

Cyclomatic complexity measures the number of independent paths through a program’s source code. High cyclomatic complexity indicates a complex control flow, potentially making the code harder to test and understand.

Here is a simple example of how to calculate complexity. Given this code:

def example_function(x):
    if x > 0:
        return x
    else:
        return -x

To calculate cyclomatic complexity:

1. Count decision points (if, else): 1
2. Add 1 to the count: 1 + 1 = 2

Cyclomatic complexity = 2

Halstead complexity measures

These metrics analyze the program’s vocabulary (operators and operands) to quantify program length, volume, and difficulty. Higher values suggest increased complexity.

For this metric, given the code below, we can calculate the metric using the following equation:

def example_function(x):
    return x * 2

To calculate Halstead metrics:

1. Distinct operators: def, return, * (3)
2. Distinct operands: example_function, x, 2 (3)
3. Total operators: 3
4. Total operands: 3

Program length (N) = 3 + 3 = 6
Vocabulary size (n) = 3 + 3 = 6
Volume (V) = N log2(n) = 6 log2(6) ≈ 15.51

Maintainability index

This composite metric combines various factors, such as cyclomatic complexity, Halstead measures, and code size, to provide a single score indicating how maintainable the code is.

As an example, let’s calculate the maintainability index using the previous Halstead Volume (V ≈ 15.51), Cyclomatic Complexity (CC = 1), and Lines of Code (LOC = 3):

MI=171−5.2×log⁡2(15.51)−0.23×1−16.2×log⁡2(3)
MI≈171−20.54−0.23−25.58
MI≈124.65

Cognitive complexity

This newer metric attempts to measure how difficult it is for a human to understand the code by analyzing factors like nesting levels, control flow structures, and the cognitive load imposed by different language constructs.

We can calculate the cognitive complexity using the following formula based on the code example below.

def example_function(x):
    if x > 0:
        for i in range(x):
            print(i)

To calculate cognitive complexity:

1. if x > 0: adds 1 point
2. for i in range(x): within the if adds 1 point (nested)

Total cognitive complexity = 1 + 1 = 2

Dependency analysis

This technique is less of a mathematical equation than the others. It visualizes the relationships between different system components, highlighting dependencies and potential areas of high coupling. As dependencies grow, application complexity increases.

Abstract Syntax Tree (AST) analysis

By analyzing the AST, which represents the code’s structure, developers can identify complex patterns, nesting levels, and potential refactoring opportunities. For code that looks like this:

def example_function(x):
    return x * 2

The AST analysis would highlight the code’s structure and allow for an easy-to-understand assessment of its structures and operations within the code.

FunctionDef
└── arguments(args=[x])
└── Return(value=BinOp(left=x, op=Mult, right=2))

Code reviews and expert judgment

Lastly, experienced developers can often identify complex code areas through manual inspection and code reviews when assessing code complexity. Their expertise can complement automated metrics and provide valuable insights.

Object-oriented design metrics

In addition to the general software complexity metrics mentioned above, developers have specifically designed several metrics for object-oriented (OO) designs. These include:

• Weighted Methods per Class (WMC): This metric measures a class’s complexity based on the number and complexity of its methods. A higher WMC indicates a more complex class with a greater potential for errors and maintenance challenges.
• Depth of Inheritance Tree (DIT): This metric measures how far down a class is in the inheritance hierarchy. A deeper inheritance tree suggests increased complexity due to the potential for inheriting unwanted behavior and the need to understand a larger hierarchy of classes.
• Number of Children (NOC): This metric counts the immediate class subclasses. A higher NOC indicates that the class is likely more complex because its responsibilities are spread across multiple subclasses, potentially leading to unexpected code reuse and maintainability issues.
• Coupling Between Objects (CBO): This metric measures the number of other classes to which a class is coupled (i.e., how many other classes it depends on). High coupling can make a class more difficult to understand, test, and modify in isolation, as changes can have ripple effects throughout the system.
• Response For a Class (RFC): This metric measures the number of methods developers can execute in response to a message received by a class object. A higher RFC indicates a class with more complex behavior and potential interactions with other classes.
• Lack of Cohesion in Methods (LCOM): This metric assesses the degree to which methods within a class are related. A higher LCOM suggests that a class lacks cohesion, meaning its methods are not focused on a single responsibility. This could potentially indicate a god class that is harder to understand and maintain.

While no single metric in this list is perfect, combining them is often beneficial for a comprehensive view of software complexity. Using these metrics as tools, teams should combine them with a thorough understanding of the software’s architecture, design, and requirements. By taking a holistic look at the software, a more accurate assessment of complexity and if it is within a necessary level is more straightforward to determine. This is even easier to decide once you understand the different types of software complexity, a subject we will look at next.

Types of software complexities 

As we can see from the metrics discussed above, software complexity can manifest in various forms, each posing unique challenges to developers regarding the maintainability and scalability of these applications. Here are a few ways to categorize software complexity.

Essential complexity

This type of complexity is inherent to the problem the software is trying to solve. It arises from the complexity within the problem domain, such as data complexity and the algorithms required to achieve the functionality needed by the application. Generally unavoidable, essential complexity cannot be eliminated but managed through careful design and abstraction.

Accidental complexity

This type of complexity is introduced by the tools, technologies, and implementation choices used during development. It can stem from overly complex frameworks, writing convoluted code, or tightly coupling components. Engineers can reduce or eliminate accidental complexity by refactoring with better design practices and more straightforward solutions.  For example, in a 3-tier architecture (facade layer, business logic layer, and data access layer), move any data access logic from the business logic layer or facade layer to the data access layer, etc.

Cognitive complexity

This refers to the mental effort required to understand and reason about the implementation within the code. Some common factors, such as nested loops, deeply nested conditionals, complex data structures, and a lack of clear naming conventions, indicate increased cognitive complexity. Engineers can tackle this complexity by simplifying control flow, using meaningful names, and breaking down complex logic into smaller, more manageable pieces. Following coding best practices and standards is one way to dial down this complexity.

Structural complexity

This relates to the software system’s architecture and organization. It can manifest as tangled dependencies between components, monolithic designs involving overly normalized data models, or a lack of modularity. 

Addressing structural complexity often involves:

• Refactoring code and architecture towards a more modular approach
• Applying appropriate design patterns
• Minimizing unnecessary dependencies

Temporal complexity

Lastly, this refers to the complexity arising from the interactions and dependencies between software components over time. Factors like asynchronous operations, concurrent processes, and real-time interactions can cause it. Managing temporal complexity often requires careful synchronization mechanisms, easy-to-follow communication between components, and robust error handling.

By recognizing the different types of complexity within their software, developers can tailor their strategies for managing and mitigating each one. Ultimately, understanding the different facets of software complexity allows application teams to make informed decisions and create software that serves a need and is also maintainable.

Why utilize software complexity metrics?

While understanding how software complexity manifests within a system is one thing, and metrics to calculate complexity might seem like abstract numbers, this understanding and analysis offer tangible benefits in the software development lifecycle. Let’s look at some areas where complexity metrics can help within the SDLC.

Early warning system

Metrics like cyclomatic and cognitive complexity can act as an early warning system, flagging areas of code that are becoming increasingly complex and potentially difficult to maintain. Addressing these issues early can prevent them from escalating into significant problems and developer confusion later on.

Prioritizing refactoring efforts

Complexity metrics help identify the most complex parts of a system, allowing development teams to prioritize their refactoring efforts. By focusing on the areas most likely to cause issues, they can make the most significant improvements in code quality and maintainability while leaving less concerning parts of the code.

Objective assessment of code quality

Complexity metrics provide an objective way to assess code quality. They remove the subjectivity from discussions about code complexity and allow developers to focus on measurable data when making decisions about refactoring or design improvements.

Estimating effort and risk

High complexity often translates to increased effort and risk in software development. By using complexity metrics, leaders, such as a technical lead or an architect, can better estimate the time and resources required to modify or maintain specific parts of the codebase without parsing through every line of code themselves. This allows for more realistic estimations, planning, and resource allocation.

Enforcing coding standards

Complexity metrics can be integrated into coding standards and automated checks, ensuring that new code adheres to acceptable levels of complexity. This helps prevent the accumulation of technical debt and promotes a culture of writing clean, maintainable code.

Monitoring technical debt

Regularly tracking complexity metrics can help monitor the accumulation of technical debt over time.By identifying trends and patterns, development teams can proactively address technical debt and, even more importantly, architectural, technical debt built into the software construction before it becomes unmanageable. Tracking the evolution of the application over time can also monitor technical debt and inform developers and architects of areas to watch as development proceeds.

Improving communication

Complexity metrics provide a common language for discussing code quality and maintainability. They facilitate communication between developers, managers, and stakeholders, enabling everyone to understand the implications of complexity and make informed decisions.

Incorporating complexity metrics into the software development process empowers teams to make data-driven decisions and prioritize their efforts. This focus on application resiliency results in a team that can create software that’s not only functional but also adaptable, and easy to maintain in the long run.

Benefits of software complexity analysis 

As we saw above, complexity metrics offer developers and software engineers advantages. But what about the larger subject of investing time and effort in analyzing software complexity? Using software complexity analysis as part of the SDLC also brings many advantages to the software business in general, improving multiple areas. Here are a few benefits organizations see when they include software complexity analysis in their development cycles.

Improved maintainability

By understanding a system’s complexity, developers can identify areas that are difficult to modify or understand. This allows them to proactively refactor and simplify the code, making it easier to maintain and reducing the risk of introducing bugs during future changes and refactors.

Reduced technical debt

Complexity analysis helps pinpoint areas where technical debt has accumulated, such as overly complex code or tightly coupled components. By addressing these issues, teams can gradually reduce their technical debt and, more accurately, improve the overall health of their codebase.

Enhanced reliability

Complex code is often more prone to errors and bugs. By simplifying and refactoring complex areas, developers can quickly improve their ability to debug issues. This increases the software’s reliability, leading to fewer crashes, failures, and unexpected behavior.

Increased agility

When code is easier to understand and modify, development teams can respond more quickly to changing requirements and market demands. Adding new features quickly and confidently can be a significant advantage in today’s fast-paced environment.

Cost savings

Complex code is expensive to maintain and requires more time and effort to understand, modify, and debug. By simplifying their codebase, organizations can reduce development costs and allocate resources more efficiently and accurately.

Improved collaboration

Complexity analysis can foster collaboration between developers, engineers, and architects as they work together to understand and simplify complex parts of the system. Just like code reviews can add to a more robust codebase and application, complexity analysis can lead to a more cohesive team and a stronger sense of shared ownership of the codebase.

Risk mitigation

Lastly, complex code and unnecessary resource dependencies carry inherent risks, such as the potential for unforeseen consequences when refactoring, fixing, or adding to the application. By proactively managing complexity, teams can mitigate these risks and reduce the likelihood of an error or failure occurring from a change or addition.

Ultimately, software complexity analysis is an investment in the future of the application you are building. By adding tools and manual processes to gauge the complexity of your system, you can ensure that factors such as technical debt accumulation don’t hinder future opportunities your organization may encounter. That said, finding complexity isn’t always cut and dry. Next, we will look at some of the challenges in identifying complexity within an application.

Challenges in finding software complexity

While the benefits of addressing software complexity are evident from our above analysis, identifying and measuring it can present several challenges. Here are a few areas that can make assessing software complexity difficult.

Hidden complexity

Not all complexity is immediately apparent. Some complexity hides beneath the surface, such as tangled dependencies, implicit assumptions, or poorly written and documented code. Uncovering this hidden complexity requires careful analysis, code reviews, and a deep understanding of the system’s architecture.

Subjectivity

What one developer considers complex might seem straightforward to another. This subjectivity can make it difficult to reach a consensus on which parts of the codebase need the most attention. Objective metrics and establishing clear criteria for complexity can help mitigate this issue.

Dynamic nature of software

Software systems are constantly evolving. Teams add new features, change requirements, and refactor code. This dynamic nature means complexity can shift and evolve, requiring ongoing analysis and monitoring to stay on top since it can quickly fade into the background.

Integration with legacy systems

Many organizations have legacy systems that are inherently complex due to their age, outdated technologies and practices, or lack of documentation. Integrating new software with these legacy systems can introduce additional complexity and create challenges in managing, maintaining, and scaling the system.

Lack of tools and expertise

Not all development teams can access sophisticated tools, like vFunction, to analyze software complexity. Additionally, there might be a lack of expertise in interpreting complexity metrics and translating them into actionable insights for teams to tackle proactively. These factors can hinder efforts to manage complexity effectively.

Despite these challenges, addressing software complexity is essential for the long-term success of any software project. By acknowledging these hurdles and adopting a proactive approach to complexity analysis, a development team can overcome these obstacles and create robust and maintainable software.

How vFunction can help with software complexity 

Managing software complexity on a large scale with legacy applications can feel like an uphill battle. vFunction is transforming how teams approach and tackle this problem. By using vFunction to assess an application’s complexity, vFunction will return a complexity score showing the main factors that contribute to the application’s complexity.

Also, as part of this report, vFunction will give a more detailed look at the factors in the score through a score breakdown. This includes more in-depth highlights of how vFunction calculates the complexity and technical debt within the application.

When it comes to software complexity, vFunction helps developers and architects get a handle on complexity within their platform in the following ways:

• Architectural observability: vFunction provides deep visibility into complex application architectures, uncovering hidden dependencies and identifying areas of high coupling. This insight is crucial for understanding an application’s true complexity.
• Static and dynamic complexity identification: Two classes can have the same static complexity in terms of size and the number of dependencies. However, their runtime complexities can be vastly different, i.e., methods of one class could be used in more flows in the system than the other. vFunction combines static and dynamic complexity to provide the complete picture.
• AI-powered decomposition: Leveraging advanced AI algorithms, vFunction analyzes the application’s structure and automatically identifies potential areas for modularization. This significantly reduces the manual effort required to analyze and plan the decomposition of monolithic applications into manageable microservices.
• Technical debt reduction: By identifying and quantifying technical debt, vFunction helps teams prioritize their refactoring efforts and systematically reduce the accumulated complexity in their applications.
• Continuous modernization: vFunction supports a continuous modernization approach, allowing teams to observe and incrementally improve their applications without disrupting ongoing operations. This minimizes the risk associated with large-scale refactoring projects.

Conclusion

Software complexity is inevitable in building modern applications, but it doesn’t have to be insurmountable. By understanding the different types of complexity, utilizing metrics to measure and track it, and implementing strategies to mitigate it, development teams can create software that sustainably delivers the required functionality. Try vFunction’s architectural observability platform today to get accurate insights on measuring and managing complexity within your applications.

Have you ever tried to debug a bit of code and been unable to get the breakpoint to hit it? Is there a variable that exists but is unused at the top of your file? In software engineering, these are typical examples of dead code existing within seemingly functional programs. There are quite a few ways that dead code comes into existence; nonetheless, this redundant and defunct code occupies valuable space and can hinder your application’s performance, maintainability, and even security.

Dead code often arises unintentionally during software evolution — feature changes, refactoring, or hasty patches can leave behind code fragments that are no longer utilized. Identifying and eliminating this clutter is crucial for any developer striving to create streamlined and optimized applications. Unfortunately, finding and removing such code is not always so straightforward.

In this blog, we’ll take a deep dive into dead code. We’ll define it, understand how it sneaks into our codebases, explore tools and techniques to pinpoint it, and discuss why it should be on every technical team’s radar. Let’s begin by digging deeper into the fundamentals, starting with a more complete explanation of what dead code is.

What is dead code?

Dead code is a deceptive element that lurks within many software projects. It refers to sections of source code that, even though they exist in the codebase, offer zero contribution to the program’s behavior. The outcome of this code is irrelevant and goes unused. 

To better grasp how dead code can present itself, let’s take a look at a few common ways it pops up:

Unreachable code

Picture a block of code positioned after a definitive return statement or an unconditional jump (like a break out of a loop). Though it exists, this code will forever remain beyond the executing program’s reach. 

Zombie code

A variant of unreachable code, zombie code is one of the hardest types of dead code to identify. This type occurs when code execution branches are simply never taken in production systems. It is also the most dangerous as slight changes to the code may cause this branch to “come alive” suddenly with potentially unexpected results. Listen to the podcast below for a deeper discussion on zombie code.

Unused variables

Imagine variables that are declared and seem to have a reason for existing, perhaps even given initial values, but ultimately left untouched — their existence unjustifiable within computations or expressions, mainly just leading to confusion for the developers working on the code.

Redundant functions

It is not uncommon to encounter functions mirroring the capabilities of their counterparts. These replicas contribute little to the application’s functionality but do add unnecessary bulk to the codebase.

Commented-out code

Fragments of code, often relics from a bygone era of debugging or experimentation, may be shrouded in comments. However, their abandonment, rather than deletion, transforms them into a great mystery for the developers who encounter them later (e.g., “Is this supposed to be commented out or what?”)

Legacy code

As software evolves, feature removals or refactoring can inadvertently cause dead code to remain in the codebase that is no longer relevant. Once-integral elements may become severed from the core functionality and are left behind as obsolete remnants.

It’s important to note that dead code isn’t always overtly obvious. It can manifest subtly, requiring manual audits and potentially specialized detection tools. Now that we know what dead code is and how it presents within code, the next logical question is why it occurs.

Why does dead code occur?

The phenomenon of dead code frequently emerges as a byproduct of the dynamic nature of the software development process. As the breakneck speed of software development continues to increase, knowing what causes it can help you be on the lookout and potentially prevent it. Let’s break down the key contributors:

Rapid development and iterations

The relentless focus on delivering new features or meeting strict deadlines can lead developers to unintentionally leave behind fragments of old code as they make modifications. This is even more common when multiple developers work simultaneously on a single code. Over time, these remnants become obsolete, subtly transforming into dead code.

Hesitance to delete

During debugging or experimental phases, developers often resort to “commenting out” code sections rather than removing them entirely. This stems from believing they might need to revert to these snippets later. Most developers have done this at some point, whether due to a reluctance to utilize source control or just an old habit. However, as the project progresses, these commented-out sections can quickly fade into the background and become forgotten relics, leading to confusion for the developers who later run into them.

Incomplete refactoring

Refactoring, the process of restructuring code to enhance its readability and maintainability, can sometimes inadvertently produce dead code. Functions or variables may become severed from the primary program flow during refactoring efforts. If the refactored code is not well-managed, usually through code reviews and other quality checks, these elements can persist as hidden inefficiencies.

Merging code branches

Redundancies can surface when merging code contributions from multiple developers or integrating different code branches. Lack of clear communication and coordination within the team can lead to duplicate functions or blocks of code, eventually making one version dead weight. Depending on the source control system used, this may not be as big of a concern.

Lack of awareness

Within large or complex projects, it’s challenging for every developer to understand all system components comprehensively. This lack of holistic visibility makes it difficult to identify when changes in code dependencies have rendered certain sections of code obsolete without anyone being explicitly aware of the situation.

You and your team have probably experienced many of the causes listed above at some point. Dead code is a fact of life for most developers. That being said, dead code can still affect an application’s performance and maintainability. Next, let’s look at how we can identify dead code.

How do you identify dead code?

Pinpointing dead code within a codebase is like detective work. As we have seen from previous sections, the way that dead code is introduced into a codebase can make it hard to detect. In some cases, it does a great job of hiding amongst the functional pieces of an application. Fortunately, you have several methods and tools at your disposal.

Manual code review

One way to identify dead code is to use manual code review methods to assess if code is redundant or tucked into unreachable logic branches. While feasible in smaller projects or targeting specific areas, manually combing through code for dead segments can be labor-intensive and doesn’t scale well.

Static code analysis tools

The first automated answer in our list for identifying dead code is static analysis tools. These tools dissect your codebase to detect potential dead code patterns and redundant code. Although different tools in this category have different approaches, most track control flow, analyze data usage, and map function dependencies to flag areas needing closer inspection. With static code analysis, there is always a chance that seemingly dead code is used when the app is executed, known as a false positive. There’s also the fact that static analysis can’t simulate every possible execution path, so “zombie” code will likely be identified as “live”.

Profilers

Code profilers are primarily used to measure performance but can also contribute to dead code discovery. Profiling runtime execution can expose functions or entire code blocks that are never invoked. Static code analysis tools scan the code in a static, not-running state, whereas profilers watch the running program in action so that there is runtime evidence of dead code. Unlike static analysis, profilers are limited to the code running when the application is profiled. This means there is never a way to prove that the profiler robustly covered all the relevant flows.

Test coverage

Building out high-coverage test suites that thoroughly test your code illuminates untouched areas. Many IDEs and testing frameworks can show code coverage, some highlighting areas of the code that tests have not executed. Although unexecuted code may signal poor test coverage and not necessarily denote dead code, it is a potential starting point for further investigation.

vFunction

With the ability to combine many of the abovementioned capabilities, vFunction’s architectural observability platform excels at pinpointing dead code. Using AI, analyzing static code structure and dynamic runtime behavior, vFunction can identify complex and deeply hidden cases of dead code that other tools might miss. If dead code is found, vFunction can provide clear visualizations and actionable recommendations for remediation. More on these exact features can be seen further down in the blog.

Complex codebases and dynamic behavior may still necessitate a developer’s understanding of the underlying application logic for the most effective dead code identification. Although automated methods are great for flagging areas that could be dead code, it still takes a human touch to verify if code should be removed. When it comes to which tools you should use, combining the above approaches is usually required to yield the most comprehensive results, balancing static and dynamic testing methods.

Why do you need to remove dead code?

The presence of dead code, though seemingly harmless, can have surprisingly far-reaching consequences for software health and development efficiency. Here’s why it’s crucial to address:

Keeping technical debt in check

Reducing the amount of dead code within your application remains highly important for reducing technical debt. From the perspective of architectural technical debt, having dead code stay within your project means that quite a few areas of the app can suffer. It’s hard to understand and optimize an application with chunks of code that do nothing but clutter the codebase and potentially skew various architectural metrics such as the size of the application, lines of code, complexity scoring, test coverage, etc.

Maintainability suffers

Dead code clutters your codebase, obscuring essential code paths and impeding a developer’s understanding of core logic. The result is increased difficulty with bug fixes, slower feature development, and increased overall maintenance effort.

Security risks rise

Dead code may contain outdated dependencies or overlooked vulnerabilities. Imagine a scenario where a vulnerable library, no longer used in active code, persists in an unused code section. This can lead to an expanded attack surface that attackers could still exploit.

Performance can degrade

Compilers may face challenges optimizing code with dead segments present since they generally do not detect whether code is actively used or not. Additionally, with the exception of commented-out code, which is normally removed from the compiler output, dead code could potentially be executed at runtime, unnecessarily wasting compute resources.

Confusion reigns

Dead code creates confusion for developers. Since the dead code’s purpose or previous function may not be apparent, developers must waste time investigating its purpose. In other cases, developers may fear that removal could cause unintended breakages or create challenges in the developer’s confidence in refactoring the application’s code.

The above reasons are quite compelling when it comes to taking the time to remove dead code. Of course, when it comes to developing software, dead code creates quite a few issues beyond what we just discussed. These consequences can manifest in the team’s workflow itself, impact the application at runtime, and other issues.

Consequences of dead code in software

Taking a further look at what was discussed in the previous section, let’s further explore the specific consequences of dead code living within a codebase.

Hidden bugs

Dormant bugs may also be present within dead code, waiting for unexpected circumstances to activate a defunct code path. This leads to unpredictable errors and potentially lengthy debugging processes down the line.

Security vulnerabilities

Obsolete functions or dependencies hidden within dead code can expose security weaknesses. If these remain undetected, your application is susceptible to being exploited through your application’s expanded attack surface.

Increased cognitive load

Dead code acts as a mental burden, forcing developers to spend time parsing its purpose, often to no avail or further confusion. This detracts from their focus on the core functionality and building out further features.

Slower development

Navigating around dead code significantly slows development progress. In projects with excessive dead code, developers must carefully ensure their changes don’t unintentionally trigger hidden dead code paths and affect the applications existing functionality.

Elevated testing overhead

Dead code artificially increases the amount of code requiring testing. This means more test cases to write and maintain, draining valuable resources. If code is unreachable, developers may waste cycles trying to increase code coverage or end up with skewed code coverage metrics since it is usually calculated on a line-by-line basis, regardless if the code is dead.

Larger application size

Lastly, dead code increases your application’s overall footprint, contributing to slower load times, increased memory usage, and increased infrastructure costs. 

Overall, dead code may seem somewhat harmless. Maybe this is due to the abundance of dead code that exists within our projects, unknowingly causing issues that we see as “business as usual”. By reducing or eliminating dead code, many of the concerns above can be taken off the plates of developers working on an application.

How does vFunction help to identify and eliminate dead code?

vFunction is an architectural observability platform designed to help developers and architects conquer the challenges posed by technical debt, including dead code. Its unique approach differentiates it from traditional analysis tools, providing several key advantages:

Comprehensive AI-powered analysis

Automated analysis, leveraging AI, is what we do at vFunction. Our patented analysis methods compare the dynamic analysis with the static analysis in the context of your domains, services, and applications. By compiling a map of everything, you can quickly identify any holes in the dependency graph.

Deep visibility

By understanding how your code executes with dynamic analysis, vFunction can uncover hidden or complex instances of dead code that traditional static analysis tools might miss. This is especially valuable for code only triggered under specific conditions or within intricate execution branches.

Domain dead code

For example, it can be particularly challenging to determine if code is truly unreachable if the class is used across domains, potentially using multiple execution paths. vFunction uniquely identifies this “domain dead code” with our patented comparisons of the dynamic analysis with the static analysis in the context of your domains, services and applications. 

Contextual insights

vFunction doesn’t merely flag suspicious code; it presents its findings within the broader picture of your system’s architecture. You’ll understand how dead code relates to functional components, enabling informed remediation decisions.

Alerting and prioritization

Architectural events provide crucial insights into the changes and issues that impact application architectures. vFunction identifies specific areas of high technical debt, including dead code, which can impact both engineering velocity and application scalability. 

Actionable recommendations

Once identified, vFunction provides clear guidance on safely removing the dead code. vFunction supports iterative testing and refactoring. For example, vFunction can determine whether to refactor a class and eliminate two other classes while maintaining functionality. This minimizes the risk of making changes that could impact your application’s functionality and behavior.

By leveraging vFunction, developers and architects can quickly uncover dead code and see a path to remediation. The capabilities within vFunction allow you to pinpoint and eliminate dead code with accuracy and confidence, promoting a cleaner, more streamlined codebase that is easier to understand and maintain.

Conclusion

Though often overlooked, dead code threatens code quality, maintainability, and security. By understanding its origins, consequences, and detection techniques, you can arm yourself with the knowledge to fight against this common issue. While many tools can help find dead code in various ways, vFunction provides a new level of insight into finding and removing dead code. With architectural observability capabilities on deck, your team can achieve a deeper understanding of your application and codebase, empowering you to make informed and effective dead code removal decisions. Curious about dead code within your projects? Try vFunction today and see how easy it is to quickly identify and remediate dead code.

Enterprises have been implementing digital transformation initiatives for over a decade, but even now, most of those efforts fall short. What gives?

Digital leaders frequently focus the effort on software. Software, however, is not the point of digital transformation. Such transformation is indeed software-powered, but even more so, it is customer-driven.

Customer-driven change as broad as digital transformation is difficult and risky. More often than not, the organization’s software efforts aren’t up to the task.

Instead of focusing digital efforts solely on software, organizations must adopt change as a core competency. Digital transformation represents ongoing, transformative change across the organization, not just its software initiatives.

Architecture is at the Center of Digital Transformation

Nevertheless, software must power the transformation — and if software fails to deal well with change, then the organization will fail as well.

Technical debt, therefore, can become the primary roadblock to digital transformation – the ball and chain that impedes software change, and with it, the organization’s broader digital transformation efforts. As a result, digital transformation raises the bar on resolving technical debt. No longer is such debt solely an IT cost concern. It now impedes digital transformation broadly – and with it, the organization’s competitive advantage.

Digital transformation success depends upon resolving issues of technical debt beyond software itself. Such debt, after all, includes obsolete ways of thinking and doing things, not just obsolete code.

The missing element that links software to these broader business transformation concerns is architecture.

Architecture is the discipline that connects the intricacies of software design to the broader digital priorities of the organization. In particular, architecture is at the heart of any organization’s efforts to adopt change as a core competency.

For this vision of transformation to become a reality, however, organizations must move beyond the architectural techniques of the past. Instead, they must take an ‘architect for change’ approach that is inherently iterative and constantly recognizes and manages architectural debt.

Architectural Debt beyond Software

As I explained in a previous article, architectural debt is a special kind of technical debt that indicates expedient, poorly constructed, or obsolete architecture. Reducing architectural debt is an important goal of most modernization initiatives.

Modernization, however, is only a part of the digital transformation challenge, as modernization alone doesn’t help an organization become more adept at change generally. Digital transformation requires that the entire organization adopt change as a core competency, not solely the software development team or even the entire IT department.

As a result, organizations must rethink the role that software architecture plays. Before digital transformation became a priority, software played a support role for the business, with software architects ensuring that applications aligned with business needs.

Digital transformation raises the bar on this support role. For digitally transformed organizations, software becomes the business. Software architecture, in turn, must support the entire organization’s efforts to ensure that business transformation drives change as a core competency.

For successfully transformed organizations, software architecture becomes a facet of enterprise architecture (EA), rather than a separate discipline.

EA concerns transformation at all levels of the organization, from the lines of business to the applications to the underlying infrastructure. For EA to drive change as a core competency across the organization, it must focus on revamping business silos to be customer-focused.

Such organizational change never comes easy – and in fact, siloed organizational structures represent enterprise architectural debt.

Conway’s Law gives us a path out of this predicament. Conway’s Law states that organizational silos parallel software silos. Furthermore, changing one kind of silo will drive change in the other – and vice-versa.

Digital transformation provides all architects with an important architectural tool: rethink the software as customer-driven end-to-end, and the organization will eventually follow suit.

Resolving Architectural Debt during Digital Transformation

Organizations must not only leverage software architecture to better manage change but also change their fundamental approach to architecture overall.

We’ve been down this road before, as various generations of software best practices have revamped how organizations architect their software.

From Agile to DevOps, and now to DevSecOps, cloud-native computing, and mobile-first digital initiatives, every step in the inexorable progression of modern software techniques has required increasingly flexible, dynamic architectural approaches.

We’ve long since moved past the waterfall approach to architecture, which called for near-complete software design artifacts before anyone wrote a line of code.

Now, we have just-in-time, just-enough approaches to architecture that avoid overdesign while responding to changing requirements over time.

Taking this modern approach to architecture is necessary for digital transformation initiatives, but it isn’t sufficient – because digital transformation is as much a business transformation as a technological one.

As Conway’s Law suggests, efforts to reduce architectural debt within the software organization will lead to a corresponding reduction in enterprise architectural debt. Resolving architectural debt across the enterprise can thus facilitate the necessary architecture-led transformation.

Architectural visibility is essential for such debt reduction and, therefore, for the success of the digital transformation effort overall.

Architectural observability gives software teams visibility into their architectural debt, thus providing a path to reducing it. When organizations are digitally transforming, this visibility gives insight into how best to resolve issues of enterprise architectural debt that threaten to interfere with the transformation effort.

Once an organization becomes proficient in managing all forms of architectural debt, they will finally become able to achieve the essential goal of establishing change as a core competency, not only in the software department, but across the enterprise.

The Intellyx Take

There are many interrelated architecture disciplines in any enterprise – software architecture, enterprise architecture, solution architecture, data architecture, and many more.

Architects within each specialty focus on the proper design and organization of the architectural elements within their purview – but all such professionals align those elements with the priorities of the business.

Modern, iterative, just-in-time architectural approaches are an important part of this story – but so is an ongoing commitment to reducing architectural debt across the board.

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