The increasing complexity of modern software structures steadily puts software developers on deathbeds. Software complexity makes the work of developers exhausting as; planning, building, and testing products become difficult. Instigating new security challenges creates administrator and user frustration. In most cases, complicated and complex tend to be indistinguishable, but not in this case. Complexity suggests that something is difficult to comprehend, but as time goes by, with effort, it can eventually be known. On the other hand, the complex represents the interconnection between several entities. When the number of entities escalates, the interconnections also increase tremendously and get to a point where it’s impractical to know and comprehend them.
Correspondingly, excessive levels of complexity in software escalate the risk of accidentally obstructing the interconnections. Something that increases the chance of introducing defects when implementing changes can make improving the software virtually impossible.
Software Complexity
Software complexity is the difficulty in maintaining, analysing, testing, modifying and designing software. The higher the complexity, the more demanding it is to read and maintain the software, and the more chances of making faults and defects. Software complexity is a technique that expresses a specific set of code features. These features all focus on how your code connects with other pieces of code.
Software complexity is considered an essential determining factor in software maintenance costs. Increased software complexity implies that maintenance and enhancement projects will take longer, cost a lot more, and result in more faults. The software complexity of a particular system is one of the dominant long-term benefactions of whatever tools and approaches were applied in its initial development. For example, utilising new CASE equipment results in the development of lousily structured software. The consequences of that poor structure will be experienced when it’s time to adjust the system.
Types of Software Complexity
Software complexity is becoming more and more troublesome as the engineering industry evolves. As a developer, you frequently spend a lot of time writing, but you spend more time maintaining that code. How generally do you find that the code has become tangled, and you almost can’t understand it? To purely comprehend how to handle the ever-increasing complexity, it is essential to differentiate the three fundamental types of software complexity.
Essential complexity
Essential complexity is usually found in the business domain you’re working in. It’s related to the problem and cannot be removed. You’re associating with business policy rules that drive business procedures in more specific terms. When endeavouring to apply and automate the processing of those policy rules, you come across various levels of complexity. The fact that ventures are usually exceedingly impenetrable environments makes the problem you’re attempting to fix inherently complex. Essential complexity is generated by the attributes of the problem to be solved and cannot be narrowed.
No matter how hard you try, this complexity cannot be crippled because the business policy regulating a claim’s processing call for 15 distinct steps. Therefore, you cannot clarify the rules by skipping some steps and crushing the processing to a few basic steps. Bottom-line, essential complexity is inevitable and primarily the real reason you’re employed as a software engineer.
Accidental Complexity
This complexity relates to solving a problem directly related to how you choose to solve it and is less associated with the nature of the complication. When you take a strategy to fix an issue that requires more complexity and work, accidental complexity. It could be the choice of your software development tools and what you lay on the table when deciphering the problem. This complexity is not inherent to the situation you’re to solve but instead crept in by accident. Accidental complexity manifests in poor design or structure, code, and substandard software development procedures. The only way to remove accidental complexity is by eliminating its source.
You cannot put in place accidental complexity intentionally. Even the name says it all “accidental”. It sometimes comes up when you:
- Want to use a new language or tool to overcome a project that is not appropriate?
- Are forced to utilise a specific tech stack?
- Lack of knowledge of the most acceptable practices to decipher this particular problem.
Causes of Complexity in Software
The fundamental problem of software is complexity. Software is a large-scale system that consists of hundreds of microservices calling and depending on each other. Many aspects of the software may not be very clearly defined. Here we discuss some of the crucial causes that lead to complexity in software development, as listed below.
- Choosing the wrong tools. Occasionally, you’re either in a hurry to get started or just love utilising a particular tool when working on code. Say you’re working on a four-page website that requires Ruby on Rails or a RESTful web API, but you insist on building the site by utilising Objective C. Not to say that Objective C is effective or good enough, but it was just implemented for the wrong problem. This can give rise to complexity in your code.
- Building systems on systems—systems are formulated hierarchically. For example, banking systems are created on Internet software built on telecommunications software. Telecommunication software decentralises purchasing power and decision power internally and externally because of the crucial agile reconfiguration of the cloud. This also causes software complexity.
- Applying the wrong abstractions. From time to time, you’ll discover software about a particular vocabulary, maybe library records—but was written using another language, say a CMSes posts datatype. The abstract is good, but it is not the right one. Everything was made much harder by the need to manipulate the inconsistency between the two concepts.
TOOLING TO REDUCE CODING COMPLEXITY
The functionality incorporated in a system and the integration between its subsystems openly influence the system’s complexity. Ideal stability between the functionality and coordination within a system is needed to attain reduced complexity. In the factual world, you cannot always keep the software simple. Typically, application attributes that offer business value tend to have complexity. However, many ways focus on decreasing complexity for engineers; other approaches can be utilised to minimise the level of complexity in your application beyond leveraging microservice architectures.
Software-assisted development
This is the ability to utilise tools, usually assisted by artificial intelligence and machine learning techniques, to help you write code, diagnose problems in code and control overall code complexity. This approach enables you to execute accurate and fast testing that minimises the complexity rate as bugs easily spot and shrink the development process. Companies like GitHub use AI to help developers write more reliable code with fewer defects.
Setup an internal platform
The increasing complexity has steered many companies to embrace an inner platform model. The internal platform team is entrusted with assessing the most vital tools by engineers, structuring templates and easing their production process. The team also focuses on functions like security, financial operations and governance to reduce the cognitive load on individual developers.
The solution to an acceptable internal developer platform is to observe the balance between self-service for developers who want to get on with the job and abstracting the minor practical tasks without restricting developers.
SOFTWARE COMPLEXITY METRICS
The higher the complexity, the more strenuous it is to maintain and read code, and the more faults and defects you’ll encounter. Measuring software complexity can help you understand and tackle the problem while still minor. The following metrics can be utilised to evaluate complexity.
Cyclomatic Complexity
This metric is based on graph theory and other mathematical definitions. Cyclomatic complexity measures the structural complexity of the code. This metric estimates the number of linearly independent paths through a program. A linearly independent approach is a fancy way of saying a unique path where you count loops only once. Operations such as IF, DO, and SELECT constitute conditional logic, making the program more difficult to comprehend. The more such procedures exist in the code, the more logical branches they accommodate and the higher cyclomatic complexity.
This metric can be determined using the formula below
V(G) = e – n + 2p
Where: e = is the number of edges in the graph
n = number of notes in the graph
p = number of connected components
Maintainability Index
This calculates the index value between 0 and 100, constituting the relative ease of maintaining code. In other words, this metric measures how maintainable your code is. The higher the value, the better maintainability of your code. The maintainability index is computed as a factored formula of SLOC (Source Lines Of Code), Cyclomatic complexity and Halstead volume.
Halstead Metrics
The metric estimates how much factual information exists in the source code, including the number of variables and how they are utilised in the source code, functions and methods. The speculation is that the more independent elements in the code, and the more they are employed, the more complex the program. This metric depends on code execution and its measures, which are statically calculated from the operators and operands in the source code.
Halstead’s metric can be calculated with the following formula
Difficult = L / V
Where: L = Program Level
V = Program Volume
The formula for determining program level L is:
L = (2 + n2*) *log2 (2 + n2*)
Where: n1 = number of operators
N2 = number of operands
The program for determining program volume V is:
V = (N1 + N2)* log2 (n1 + n2)
Where: N1 = total number of operator occurrences
N2 = total number of operand occurrences
Object-Oriented Design Metrics
Quantitative metrics for object-oriented design usually focus on class and design features. They license developers to evaluate software early in development and comprehend how to minimise complexity and enhance maintainability.
EFFECTS OF SOFTWARE COMPLEXITY
Complexity impact on Error rates.
The objective of why we developers are troubled about complexity is related to more important features of the software development process. The more difficult code is to understand, the more likely programming errors will go undiscovered. When a program is difficult to test, errors will not be caught before the software operates.
You expect high error rates in programs that manifest high decision densities (with their equally distributed number of decision paths to test for errors). New mistakes are likely to generate from such systems.
Complexity impact on maintenance costs
Software complexity has a direct impact on maintenance costs. The more demanding a complex application system is to maintain, the more expenses you’ll incur during maintenance, which consumes most of the development process. Software maintenance is perceived as a production procedure whose inputs are computing resources and labour, and the output is improved code. Labour hours are considerably more significant than computer resources, and the two have insubstantial replacement possibilities. Attention is put on labour hours as the considerable expense incurred.
A REMEDY FOR COMPLEXITY IN SOFTWARE DEVELOPMENT
Even though microservice architectures create more extensive and complex applications, they disentangle work for rank-and-file developers. Microservice architectures generate a more complex application than a correspondent application built as a monolith. Still, this doesn’t mean the developer’s job is more complicated.
Microservice Architecture Complexity.
Microservices architecture is a DevOps that provides a more dynamic and agile technique for executing, developing and managing applications by working with modular components versus a monolithic build.
To many, microservices are assumed to cure all your software complexity problems. However, they can only rehabilitate fifty per cent of those problems alone. To solve the other half, you must merge microservices with the latest DevSecOps practices and alter your company into the ultimate killing machine.
Microservices sanction companies to execute a million deployments daily, scale the system up to infinity, minimise the codebase complexity and save resources. Microservices move the complexity from the services to the platform, ignoring the factual results in suboptimal results. Many companies have designed monolithic applications only to find that they get too complex. As a developer operating in a single codebase, it’s burdensome to add features and rectify defects independently. Usually, this limits the number of projects you can work on in a single application.
Microservices make an application less complex by removing particular pieces of it. When you split the app into different modules, you’re trying to divide that complexity to minimise the number of developers working in a single code base. Splitting can be done so that you develop an organised complexity that is easy to understand.
The software complexity model
The causes of complexity have been discussed in the genesis of this chapter. The complication gives the project inherent complexity, and then more complexity arises in the course of design and the phase of the software development process. Based on this model, complexity unites with other factors to determine the error-proneness and size of the program. The discussion of the nature, sources and effects of software complexity and a theoretical framework for a graphical disclosure of the concept of software complexity has also been provided.
CONCLUSION
Many companies have to deal with software complexity, and the approaches you utilise when dealing with it will affect your company’s future. There are various ways to manage software complexity. Approaches, such as establishing an internal platform and standardising external services across the company, could be good strategies.
Also, though microservice architectures increase the software complexity, they offer value in minimising the cognitive load and visual complexity to individual developers.
