Apr 6th, 2024
Streamlining Software: Techniques to Reduce Excess and Simplify Complexity
Software development is complicated enough as it is the last thing you want is for your code and repositories to become unnecessarily bloated or complex. Unfortunately, it can be difficult to prevent this from happening, particularly when you have multiple teams working on the same solution, need to incorporate a lot of disparate data sources, or have legacy requirements that need to be considered.
How does software become complex? Why can this complexity be bad for business?
Software is a necessarily complex thing. Every software system has a degree of essential complexity that can’t be avoided. This is complexity introduced by simply writing code. Complexity increases when other factors come into play: technical or cost limitations, the age of the software and how many additions have been made to the codebase, and accidental complexity introduced by new developers or poor planning.
Software complexity can bog down your codebase and make code maintenance and product deployment slow and complicated. Mitigating complexity takes up valuable resources—primarily, the time of your engineers, who must trade off maintenance and refactoring requirements with the demands of developing new features.
How does software complexity make development and the maintenance of the application difficult?
The more moving parts a thing has, the more difficult it is to maintain. This is especially true of software development. Often, we don’t consider the long-term ramifications of building a particular feature, especially how the code for that feature interacts with the rest of the system.
Complexity also affects application security, as multiple studies have shown that more complex systems expose more weak points and make an application more susceptible to attacks and breaches.
This complexity can hinder software development and maintenance efforts by making code harder to understand, modify, test, and optimize. It can also increase the risk of errors, performance problems, and technical debt, ultimately impacting the overall quality and reliability of the application. Therefore, managing and reducing complexity is essential for ensuring software projects’ long-term success and sustainability.
What are the most common types of complexity in software applications?
There are three primary types of complexity in software applications: essential complexity, incidental complexity, and accidental complexity.
Essential Complexity
Every system has an unavoidable degree of essential complexity. This is the complexity introduced by simply writing code and building features. It is measured in the number of entry points, termination points, and nondeductible nodes. The closer to 1 this value is, the more well-structured the program is.
Incidental Complexity
Incidental complexity is another somewhat unavoidable type of complexity. It is introduced by constraints like cost and type of technology available, software limitations and hardware limitations.
Accidental Complexity
This is the type of complexity that can be most easily planned for and avoided. Accidental complexity is introduced through failing to establish metrics and goals for an application or feature, by failing to consider the long-term ramifications of a particular feature, or by failing to take into account the existing parts of a system.
Metrics for measuring software complexity
There are a few different methods for measuring complexity. Some of the most common include:
Cyclomatic Complexity: This is one of the most widely used metrics for measuring the complexity of a program. It measures the number of linearly independent paths through a program’s source code. The higher the cyclomatic complexity, the more complex the code. Tools like McCabe’s cyclomatic complexity metric help calculate this.
Halstead Metrics: Maurice Halstead introduced these measures based on the number of unique operators and operands in a program. The four main measures are program length, program vocabulary, program volume, and program difficulty. These metrics provide insight into the effort required to understand and maintain the code.
Chapin Metric (Data Complexity): Measures the amount of code required to build a new Maintainability Index: This is a composite metric that combines various factors such as cyclomatic complexity, lines of code, and Halstead volume to provide an overall measure of how maintainable the code is. A higher maintainability index indicates better maintainability.
Lines of Code (LOC): This simple metric counts the number of lines of code in a program. While it doesn’t directly measure complexity, larger codebases generally tend to be more complex and harder to maintain.
Halstead Metrics: Maurice Halstead introduced these measures based on the number of unique operators and operands in a program. The four main measures are program length, program vocabulary, program volume, and program difficulty. These metrics provide insight into the effort required to understand and maintain the code.
McCabe’s Complexity: As mentioned earlier, McCabe’s complexity metric, also known as cyclomatic complexity, measures the number of linearly independent paths through a program’s source code. It is calculated using the control flow graph of the program.
These methods can be used individually or in combination to gain a comprehensive understanding of the complexity of software systems and simplify software development. Each method has its strengths and weaknesses, and the choice of method depends on the specific goals of the analysis and the characteristics of the software being measured. A good software product development company can help businesses like yours minimize software complexity and improve efficiency, contact us to know more.
