Continuous Testing: How to Measure & Improve Code Quality
Testing and quality assurance (QA) is an integral part of the software development lifecycle (SDLC). Testing allows you to find and correct defects in the code, reduce the risk of data breaches, and improve the product as a whole. With the help of testing, you can check if the product features work correctly, if the system reacts to specific inputs as expected, and if the end users understand the flow within the software.
As software development methodologies change, testing techniques evolve along with them. In a dynamic, highly competitive world, businesses cannot afford to discover critical bugs right before release. They need to be confident in the high quality of the code right from the start of the writing process.
Continuous testing helps identify and fix bugs as they arise. If you are looking for a sophisticated approach to ensure high code quality, continuous testing is what you should consider.
Continuous testing vs. traditional testing
In traditional software development, testing is often the final stage, preceded by analysis, planning, design, and development. This approach is unsuitable for projects with high time-to-market requirements or complex functionality. Missing bugs in the early stages of development leads to delayed releases, extended deadlines, and increased development costs.
Unlike traditional testing, continuous testing does not wait until the specific functionality is finished but checks the code quality as it is written. In other words, continuous testing involves verifying code quality in parallel with its creation. This allows developers to almost instantly fix bugs when they are discovered and approach the release date with a minimum number of errors and inaccuracies.
Key features of continuous testing include:
Automated testing, which triggers a check upon the occurrence of an event, for example, pushing a change to the code or creating a build
Frequent test execution that occurs after each change made
Seamless integration into the CI/CD pipeline, where development, testing, and deployment automatically replace each other
Collaboration of testers, developers, and other software development stakeholders
Shift-left approach, which involves testing at the early stages of the development lifecycle
Test scaling, which allows increasing or decreasing the number of tests as development progresses
The table below demonstrates the key differences between traditional and continuous testing.
Continuous testing | Traditional testing | |
Continuously throughout the software development lifecycle | Timing | In dedicated phases after development is complete, |
Emphasizes test automation | Automation | Often relies on manual testing |
Frequently, often triggered automatically with each code change or build | Frequency | Less frequently, usually in discrete phases before release. |
Provides rapid feedback to developers | Feedback Loop | Provides feedback later in the SDLC, |
Integrated with the CI/CD pipeline | Integration | Often disconnected from the CI/CD pipeline |
The role of continuous testing in maintaining high code quality
Today, many organizations face tough competition and high customer expectations and, as a result, set strict deadlines to implement software projects. Such conditions force development teams to look for ways to reduce time-to-market and speed up product implementation. This often leads to producing low-quality code and running into technical debt.
Continuous testing is a tool that helps maintain code quality at a high level and deliver projects within a defined timeframe. By working in parallel with developers, testers detect bugs at early stages and help developers troubleshoot issues before they become major problems.
Here is what you can expect to have by implementing continuous testing in your software development lifecycle:
Early bug detection. You can catch problems as soon as they occur and prevent them from evolving into complex issues.
Faster feedback loop. Developers can immediately see if the changes they made break the existing functionality.
Regression testing. QA engineers immediately rerun existing tests to ensure the whole app works as expected after the changes are made.
Better coding practices. Knowing that their code will be immediately checked, developers follow best coding practices.
Code quality metrics. After running continuous testing, QA specialists report code quality metrics, which we will discuss below.
Looking to enhance your code quality? Incorporate continuous testing in your SDLC
Measuring code quality
When a client orders product development, they focus on how it should work and what features it should have. Being interested in the final result, the client does not always think about what is happening behind the scenes and how exactly the software achieves its goals.
The quality of the codebase, which is invisible at first glance, has a huge impact on the product in the long run. If you've used a workaround due to tight release schedules and haven't replaced it with a technologically sound solution later on, it can lead to serious problems in the future.
For example, your workaround could break related functionality, increase technical debt, and delay subsequent releases. Thus, saving in one place you're losing in another, incurring greater costs in the long run.
Continuous testing maintains high code quality and reduces the business risk associated with unprofessional technological solutions. Here are the code quality metrics it reports:
Cyclomatic complexity
Definition: Measures the complexity of the code by counting the number of linearly independent paths through a program's source code.
Importance: High complexity suggests harder-to-maintain code; it helps identify code that might need refactoring.
Maintainability index
Definition: Shows the ease of maintaining the code on a scale from 0 to 100. The gradation is as follows:
20 to 100 - high maintainability
10 to 19 - moderate maintainability
0 to 9 - low maintainability
Importance: A higher maintainability index indicates that the code is easier and less costly to maintain and modify.
Code duplication
Definition: Measures the percentage of duplicate code in the codebase.
Importance: Duplicated code decreases code maintainability. You will have to do major code revision if a change to business logic is required. However, if the code is written without duplications, one change to class or function may be enough.
Dependency number
Definition: Measures the dependencies between the code modules and components.
Importance: Excessive dependencies complicate the codebase and make it harder to maintain and change. Dependency number analysis helps identify tightly coupled components and optimize code before the software release.
Response time
Definition: Measures the time the system takes to respond to user requests.
Importance: Slow response time negatively impacts user experience. Response time analysis helps detect weak spots and identify the reasons for their occurrence.
The power of static code analysis
Because continuous testing occurs at the same time as code is written, it often analyzes the code base before the program is executed. Such analysis helps identify violations of best coding practices that lead to the complications of the code base.
Typically, static code analysis is carried out using special tools - code analyzers. Some of the most common static code analyzers are:
Collaborator
Embold
PVS-Studio
SonarQube
Helix QAC
The main function of static code analysis is early bug detection. This type of testing greatly optimizes the work of QA engineers. The errors identified in the code are eliminated even before the testers begin to check the functionality implemented using this code. Thus, they can focus on more critical issues caused by reasons other than an inefficient code base.
Another advantage of static code analysis is its adherence to shift-left security, which addresses application vulnerabilities from the very beginning of the software development process. This is opposed to the shift-right approach, which introduces security checks at the end of the development lifecycle.
Do not want to rush fixing bugs before release? Let static code analysis detect them at the early stages of SDLC
Automation as a basis for continuous testing
Today, more organizations are switching to the CI/CD framework, which involves automatic change in project implementation stages from development and testing to deployment and production. Automation testing is an integral part of the CI/CD DevOps pipeline. It allows you to immediately run auto tests after the release of specific functionality and identify bugs before the application is deployed and released.
Continuous testing is not just about checking finished functionality. As mentioned earlier, continuous testing goes in parallel with the creation of the system and, among other things, includes checking static code. However, inspecting the functionality after its implementation is a critical point that receives special attention in the software testing process. Test automation significantly speeds up the quality assurance process. Automated testing can quickly identify critical logic-related issues. It also excels at performance testing, integration testing, unit testing, and other testing types.
To make the application perfectly polished and bug-free, QA engineers would also conduct manual testing. For example, functional testing reinforced by manual checks allows you to achieve minimum to zero bugs and ensure the system works as expected.
Enhancing continuous testing with codeless test automation
If you want to make your testing process even more efficient and faster, you can turn to codeless testing. This allows you to run autotests generated by no-code, low-code test tools rather than those written manually. The use of such test automation tools greatly reduces the costs associated with continuous testing due to:
Significant reduction in testing time. Instead of writing test scripts manually, you assemble them from ready-made GUI blocks.
Less stringent requirements for test specialists. You can hire less qualified employees or involve non-testing specialists in the testing process.
Ease of use. QA engineers can quickly understand how no-code testing tools work and use them immediately.
Looking for a comprehensive QA approach?
Continuous testing can significantly optimize the software development process by providing immediate feedback on the quality of the code base and implemented features. Also, using automation testing and no-code / low-code testing tools, you can speed up the time-to-market and reduce the costs of QA.
If you want to improve your testing routine but don't know where to start, consult with senior QA experts. With the support of QA professionals, you can put testing on the right track and take your software development to the next level.
FAQ
Continuous testing contributes to code quality improvement by identifying issues early in the development process. By running tests consistently, it ensures that code changes do not introduce new bugs, leading to more stable, reliable, and high-quality software products.
Test automation significantly enhances the effectiveness of continuous testing by enabling rapid, repetitive, and thorough testing. Automated tests can be executed continuously, ensuring quick feedback on code changes, reducing manual effort, and allowing development teams to identify and fix issues promptly, thereby improving overall code quality and delivery speed.
Challenges in implementing continuous testing for code quality include ensuring comprehensive test coverage, managing complex test environments, and integrating testing seamlessly into the DevOps pipeline. Additionally, balancing speed with accuracy is another challenge that organizations face during continuous testing implementation.
