A Simple Understanding of Code Complexity
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Measuring Code complexity is the best tool to predict defect probability. There is an old saying in technology – you cannot manage what you cannot measure. It is no secret that code is difficult to write, debug, and maintain. These aspects, along with the integration of formerly proprietary applications, is what makes code high quality. But these factors are also responsible for making it difficult to measure. However, because the concept is not understood correctly, it is unable to deliver its most significant benefits.
The cost of fixing defects is always highest in the production stage of development. With numerous system interfaces and complex requirements, the complexity of software systems sometimes goes beyond control. The rendering of portfolios and applications become too expensive to maintain and too risky to improve. If left unchecked, code complexity can run rampant in delivered projects. As a result, it leaves behind bloated and cumbersome applications. Recently, development managers have started to put in a lot of interest in measuring code quality. Therefore, code reviews and analysis tools have become very popular. Many organizations have even been able to identify technical debt earlier than before.
The graph shows the relative cost of fixing defects
Metrics of Code Complexity
The complexity of code considerably affects the efficiency of a software project. It is easy for code to become complicated. Sometimes, it becomes so complicated that it can no longer support any significant enhancement. In such situations, the only way to further improve the code is first to reduce complexity. Currently, there is no conclusive research on code complexity. Many analysts are not yet convinced that it is related to security vulnerabilities. However, the number of bugs in a program inevitably has a direct correlation with the number of security vulnerabilities.
Here are some of the metrics used to measure code complexity
- Source Lines of Code (SLOC) – It counts the number of lines in the source code. It is the most straightforward metric used to measure the size of the program. However, functionality and complexity do not relate that well as a skilled developer might be able to deliver the same functionality with a significantly smaller code.
- Cyclomatic Complexity – This measures how much control flow exists in a program. Since programs with more conditional logic are more complex, measuring the level of complexity tells the developers how much needs to be managed. This is done by directly measuring the number of paths through the code. Say a program is a graph of all possible operations. Then, complexity measures the number of unique paths through that graph. Every if, while, or for statement creates a new branch. It is even possible for one branch to double the total number of paths. But the results from this method can sometimes be very deceptive.
For example, a module can be complicated, but only have a few interactions with other modules. On the other hand, a module can be simple but highly coupled with external modules. The higher the communication with external modules, the higher the complexity of the codebase. Complexity metrics will look bad in the first case. In the second case, the metrics will look good, but the code will be quite complex. Therefore, it is crucial to measure the coupling and cohesion of modules. Only then can we get an accurate system-level complexity measure.
- Halstead Volume – This metric measures how much information is in the source code. It looks at how many variables are used and how often they are used in programs and functions. Since these additional pieces of data affect data flow, programmers must learn them.
- Maintainability Index – It calculates the overall score of the maintainability of a program. Maintainability index is more of an empirical measure, unlike Cyclomatic complexity and Halstead volume. This index has been developed over the years with consultants from Hewlett-Packard and its software teams. It weighs Cyclomatic complexity and Halstead volume against the number of lines of code in the program. This analysis gives an overall picture of software complexity.
An overall picture of software complexity
Benefits of Measuring Code Complexity
Measurement of software complexity based on defined algorithms provides a comprehensive assessment of the code. Regardless of the size of the code, measuring it can make your code objective, repeatable, consistent, and cost-effective.
Here are the most significant benefits of measuring software complexity:
- Dependable Testing – Measuring the complexity of the code tells the developer how many paths there are in the code. Therefore, the developer knows how many paths there are to test. This will help them calculate the minimum number of tests required to cover the entire code.
- Reduced Risk of Defects – One of the most famous sayings in the IT industry is – “It’s harder to read code than to write it. Since code is read far more than it is written, this saying is true. By reducing code complexity, developers can reduce the risk of introducing more bugs. After all, a good developer is never assessed by the lines of code they have written, but the quality they have maintained.
- Lower Maintenance Cost – By reducing complexity, we reduce the probability of introducing defects. When the risk of potential bugs is reduced, there are fewer defects to find. Therefore, maintenance cost significantly reduces.
- Greater Predictability – Measuring the complexity of code, helps developers say how long a section of code takes to complete. This knowledge allows organizations to predict better how long a release takes to ship. Hence, businesses dependent on such software can better set their goals and expectations. It also allows development teams to set more realistic forecasts and budgets.
Final Takeaways:
In addition to all these benefits, it also helps developers grow. By understanding why their code is complex, developers learn to improve their code in the future.
Complex code is undoubtedly harder to maintain than simple programs. More security vulnerabilities are likely to creep into it as the software evolves. But this does not mean that we should stop writing complex programs. The biggest software companies continue to develop more complex applications, but with fewer vulnerabilities. This can be done with a robust, secure development lifecycle (SDL) in place. It will perform security and privacy checks throughout the entire process of software development. Therefore, you can write complex code without succumbing to increased vulnerabilities. Any organization attempting to increase their rate of software delivery must implement code complexity measuring algorithms.
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