Sample Essay on Metrics for Measuring Code Complexity

Metrics for Measuring Code Complexity


As a member of the new software production development team, if the boss needs to understand how to estimate the duration taken to complete the project.  In such scenario, I would call for the project analysis and the preparation of the schedules. The project is likely to take one week due to the scope of the work involved. This includes research, analysis and completion of the project.  Therefore, it would be possible to complete the project within the next one week.

To determine code complexity, various metrics will be used. Firstly, code metrics are groups of software measures rules that assist software developers to improve their code insight as they create it. Through the code metrics, the developers are able to understand which methods should be reworked. The software development teams would be able to identify potential risks, examining the current state of projects and assessing the progress of software development.  The organization should have planning tools for configuring the code complexity and code coverage tools (Byers & Shahmehri, 2009). This is appropriate in obtaining the information needed about the progress of the projects at different levels. Maintainability index provides metrics of understanding the code complexity. Maintainability index calculates the index rate from 0 to100, which indicates the relative ease of stabilizing the code.


A high value of index implies improved maintainability. Color-coded evaluations can also be used to identify the troubles points in the software code. Green evaluation is usually identified to between 20 and 100 to show that the code has a good maintainability (Yu & Zhou, 2010). The yellow rating ranges from 10 and 19 and examines that the code is appropriately maintainable. Red evaluation is level of rating between 0 and 10 and examines low maintainability.  The maintainability index will be useful in examining the code complexity of the softwares.


Cyclomatic complexity is a metric that examines the structural intricacy for the code. It involves the calculation of the number of different code paths in the flow of the programs. A program with complex flow will always require more test to get good code coverage and be less maintainable.  The CCN metrics helps in presenting a separate dialog stating methods and functions of the code fixers and developers. In addition, class coupling metric helps in examining the coupling relayed to the unique classes through local variables, parameters, and attribute ornamentation.

High-quality software design indicates that the types and methods must have high level of consistency and low coupling. Enhanced level of coupling implies a design that is complicated to use again and support due to the many interdependences on the various types. Lines of code metrics also would examine the estimated figure of lines in the code (Rajnish &Bhattacherjee, 2007). Metric depends on the IL code and thus, there is not specific number of lines for the code files. A high count indicates that a method always tries to achieve high output and ought to be categorized.

For instance, the McCabe Cyclomatic metric is one of the most useful logical metric. This metric helps in measuring the number of linear independent paths through a software program. The straight-line code can be easy to follow. The programs could have many conditionals and difficult to follow. In programming terms, edges define the code executed from the decision points. MCC is useful in examining the testability of a program in which leads to higher value and reducing risks of testing and maintaining. The MCC metric can also apply with other metrics, as each element has the same weight (Honglei, Wei, & Yanan, 2009). Finally, average block depth is another useful metric of measuring the complexity of the code. This examines the methods and constructors that have various levels of nested blocks. This approach can be difficult to understand due to the difficult of computing the various containers.  Therefore, the metric of average block depth will used in recording the various levels of information blocks within the systems.

The number of statements is metric which represents the number of statements in mode. It is a more robust measure than the lines of code as the lines of code varies with respect to the formatting of the conventions. However, the different types of software metrics lead to different levels of improvement and control of the software complexity. Software assurance supports the most suitable security measures to promote the software process programs. However, security can be integrated within the software development cycle to help in the estimation of the cost of security related programs (Dawson, Burrell, Rahim & Brewster, 2010). The study of software development life cycle will assist in fixing concerns associated with code complexity. This is because the cycle offers a more in-depth understanding of the various information system blocks in the organization (Brewster & Dawson, 2010). Therefore, the cycle can be used as metric of understanding the level of code complexity as complexities slow the entire information system.

Some of the metrics evaluated by this metrics includes constructor call, return, and throw among others. The number of level seeks to examine the number of levels of nesting in a model. A higher number of the levels increase the code complexity.

Some of the follow up questions that could help in the evaluation of the code complexity includes the following;

  1. How many execution channels could be available for the determination of the code complexity?
  2. Does the code complexity affect the function’s reliability and testability?
  3. How the metrics does assist in correcting the issues of code complexity?
  4. What are some of the properties of the code


Brewster, S. & Dawson, M. (2010). Software assurance management best practices. Journal of Information Systems Technology & Planning, 3(6), 72–80.
Retrieved from the Walden Library using the Computers & Applied Sciences Complete database.

Byers, D., & Shahmehri, N. (2009). activities. International Conference on Availability, Reliability and Security, ARES ’09.

Dawson, M., Burrell, D. N., Rahim, E., & Brewster, S. (2010). Integrating software assurance into the software development life cycle (SDLC). Journal of Information Systems Technology and Planning, 3(6), 49–53.

Honglei, T., Wei, S., &Yanan, Z. (2009). The research on software metrics and software complexity metrics. International Forum on Computer Science-Technology and Application, IFCSTA ’09.

Rajnish, K., & Bhattacherjee, V. (2007). Complexity of class and development time: A study. Journal of Theoretical and Applied Information Technology, 3(1), 63–70.
Retrieved from the Walden Library using the Computers & Applied Sciences Complete database.

Yu, S., & Zhou S. (2010). A survey on metric of software complexity. The 2nd IEEE International Conference on Information Management and Engineering (ICIME).