Parasoft’s well-known company recently integrated its unit testing software with OpenAI and Azure OpenAI providers. This can make it easier for developers and development managers to speed up and improve their Java code testing process, thereby increasing productivity and quality. How fast is AI in unit testing? Here are the insights we got. We have summarized them for you.
Performing Static Analysis and Unit Testing
Code quality is a key concern for development teams, and one of the easiest ways to achieve it is by using static analysis. Static analysis has a low impact on the development process but a high impact on the quality of the code.
However, more than static analysis is needed. Unit testing is another essential practice for ensuring code quality, even though creating and maintaining unit test suites requires more engineering time. Both static analysis and unit testing are important forms of code-level testing that help build a solid foundation for quality in software programs. They have many benefits that justify their costs.
Some of the main benefits of using static analysis and unit testing are:
Cost savings from early bug detection. Code-level testing helps find and fix bugs and defects in the software as soon as possible, reducing the time and money needed for debugging and fixing issues later in the development cycle or production. Finding defects early also reduces the disruption caused by late-stage defects, which forces developers to stop working on new code and go back to old code to find the root cause of a defect reported by their QA team. Code-level testing helps lower the cost of software development, defect correction, and maintenance.
Better code quality. Testing code early helps developers be more responsible and write cleaner, modular, and maintainable code. This leads to better software architecture, faster achievement of code coverage goals, easier compliance with regulations, and more consistent use of coding standards and design patterns.
Regression testing. Code-level tests help ensure that the existing functionality of the code is not affected by new or modified code. This helps avoid regression bugs, where changes in one part of the code unintentionally break other parts. Using a good unit testing practice, development teams can run their unit test suites every time they make significant changes or additions to their code base and catch issues before they become expensive to fix in the later stages of development.
Confidence in code changes. Developers can make confident changes and add new features with confidence that their code-level test suites and static analysis scans will warn them of any problems they create.
Faster debugging. When a code-level test fails, it shows exactly where and what the problem is, making it faster and easier to identify and solve issues.
Static Analysis Challenges in Focus
Static analysis is a powerful way to improve software quality, security, and reliability with minimal impact on code development. Static analysis can be part of the early code development process, allowing developers to run static code analysis scans in their IDEs and find and fix defects before they enter the larger code base.
Static analysis can also be part of the CI/CD pipelines, enabling automatic code scans after every code commits to source control. This lets development teams check their code easily and automatically for defects, memory leaks, maintainability issues, and compliance requirements. It’s a simple best practice with low overhead costs. However, static analysis also has challenges that can interfere with developers and affect productivity.
Reducing Noisy Static Analysis Findings
Although it’s better to adopt the quality and security built-in approach and integrate static analysis into your code development process and workflow from the beginning, many development teams start using static analysis when code development is already underway.
Depending on the rule set and the size of the code base, the static analysis solution can generate many findings or rule violations. For new teams starting to use static analysis, running a code scan and receiving thousands of results can be daunting, discouraging, and confusing, affecting the adoption of static analysis tools.
Knowing What to Prioritize
When static analysis results are delivered to development teams, knowing what to prioritize in the static analysis findings can be difficult. Many tools have severity levels for each static analysis rule. Still, ultimately, violation prioritization also depends on the specific code base, the location of the violation, the type of application, and the software consumer. While static analysis rule severity classifications can provide some guidance, every application is unique, leading to different specific requirements for code guidelines. Knowing what violations are the most important to address for remediation based on the specific needs of your application can be difficult.
Understanding Compliance Requirements
Many development teams use static analysis because of industry-specific or security requirements. While static analysis solutions often have documentation for each rule explaining its relevance to specific standards, knowing how to fix the code can be hard and time-consuming. Only some people are skilled developers, even those who may need help with specific rules related to security or coding standards. They find them hard to follow and fix when a violation is found.
How AI Optimizes Static Analysis Processes
With AI, the static analysis process can be smart for tracking any code, and a list of actions must be taken to fix it. AI also brings other benefits, such as:
- Code coverage analysis across the application.
- Actionable insights with build-to-build comparisons.
- Change analysis.
- Static analysis violations tracking.
- Compliance reporting capabilities.
AI helps the development team determine the most critical violations to their application, find the root cause, assign the most skilled staff members to fix the violations, and speed up the remediation process.
Unit Testing Challenges in Focus
Unit testing is a key practice in software development. A good base of unit tests is an effective way to ensure high-quality software and find defects early, enabling fixing them during the earliest and cheapest stage of the development life cycle. However, setting up a unit testing practice and following a specific code coverage goal requires more engineering hours for testing activities.
Unit testing is expensive, with an average of 40% overhead for development organizations. However, with the recent improvements in AI, software development teams can lower the overhead costs of unit testing activities and get the quality benefits of a good unit test base.
While very valuable to software’s health, quality, and reliability, unit testing has its own challenges and cultural barriers that development teams need to overcome. Some of the common challenges that often hinder successful unit testing practices are:
At the end of the day, developers prefer to spend their time writing new code rather than creating and maintaining test cases to check the code they just wrote. When the code is more complex, the time needed to write the test cases also increases.
Isolating the Code Under Test
Making sure that unit tests are isolated from external dependencies, such as databases, external services, or the file system, is essential. Mocking and stubbing these dependencies requires technical knowledge and is time-consuming. It often needs an understanding of mocking frameworks like Mockito. If the code is properly isolated, testing results can be right.
After the test is created, developers still need to maintain that test for regression testing purposes. Test maintenance can be a boring task. When code has changed, test cases need to be changed to support the changes, and the unit testing suite needs to be run again to ensure that the changes to the code base have not broken existing functionality. Keeping regression test suites neat and maintained is necessary to ensure code changes have not broken existing functionality.
Unit Test Code Coverage
Some organizations require reaching a specific code coverage level to measure release readiness. 80% line code coverage is often a common and enforced metric in commercial software. Reaching complete test coverage means testing all code paths and edge cases, which can be hard. Teams often spend long engineering hours pursuing their code coverage metric.
A term often used to describe old code that needs to be written to be easily maintainable or to meet modern quality and security expectations. Often, legacy code has been mainly manually tested, the testing was done occasionally, or the test cases are all in old frameworks that may no longer be useful. When legacy programs are aimed for either refactoring or modernization, it’s important to create a unit testing suite for regression testing to ensure that the code changes the development team makes do not break existing functionality. However, when the code has not been written following best practices, is not easily maintainable, or is very complex, creating unit tests becomes even harder and more time-consuming for the development team.
Resistance to Testing
As unit testing is time-consuming, development organizations often balance the time spent creating and maintaining cases versus focusing on creating new code and increasing productivity. Organizations that give up unit testing for a faster time to market are risking the increased chance of bugs in production.
How AI Reduces Unit Testing Overhead
AI and ML technologies can help developers save time and effort in creating and maintaining test cases throughout the testing pyramid. With AI, developers can quickly generate a unit test suite that covers more than half of the code and enhance the test cases to achieve more coverage, mock or stub dependencies, add assertions, parameterize test cases, and clone or mutate existing test cases.
Moreover, developers can use generative AI technology to tailor the test cases to their specific needs as defined by the developer. AI makes it easy and fast for developers to create effective and relevant test cases that meet the application’s requirements while reducing the burden of unit testing activities.
AI offers the following advantages to developers.
Speeds up unit test creation. When writing new code, creating unit tests along with the development is a good practice. Unit Test Assistant helps developers to generate meaningful individual test cases as they write the code. Tests can be augmented and customized with the Unit Test Assistant’s guidance and suggestions on mock or stub dependencies, add assertions for regression control, or change the test to cover more code. Developers can also use the generative AI LLM integration to customize the test in specific ways based on the user’s natural-language prompts.
Keep test cases updated. Once the unit tests are created, they need to be maintained to be used for regression testing. When the code base changes, the test cases must be updated accordingly. AI speeds up the maintenance process by analyzing the test during runtime and giving the developer recommendations on updating the test case to improve its stability. With AI features, developers can tell the AI to refactor the test case according to their descriptions of the changes they want. This makes the test case more maintainable over time.
Updates legacy test cases. Development teams can easily refactor old test cases to modern frameworks. For example, if a code base has been neglected for a few years and a new team is assigned to modernize the application, using the old test cases for regression control is very useful. However, the test cases may need to be updated in an updated format. Hence, developers need to spend time refactoring the test cases to migrate them to modern frameworks. Developers can tell the AI how the test case should be refactored and simplify the modernization process.
Speeds up test feedback. When the unit test suite is large and contains thousands of test cases, running the test suite can take a long time, so developers have to wait for feedback from their test executions before debugging and maintaining their test cases. AI, development, and testing teams can run only the test cases affected by code changes, reducing the number of test cases that need to be run and speeding up the feedback loop.
With AI’s benefits, development teams can easily improve their unit testing practices and reduce overhead costs by overcoming challenges, automating tedious tasks, and enjoying the benefits of software quality that a robust unit test foundation provides.
Boost Unit Testing & Reduce Code-Level Testing Overhead
It makes testing more enjoyable and efficient for developers, allowing them to swiftly set up, run, and update test cases, perform code analysis, and fix any detected issues. This way, they can devote more time to developing new code.
AI enhances testing and quality-oriented methods so that teams can produce better quality code, improve their development performance, speed up their delivery time, and launch more assuredly. Emveep can help startups achieve these goals with their expert software development services.