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Ready to elevate your Java development process and drastically reduce bugs before they reach production? Transform your IDE into a powerful ally in the fight against software bugs with live unit testing and live static analysis.
Did you know that 70-80% of issues arise from code changes? It’s essential to have the right tools to validate your code locally before committing it. Discover how live unit testing allows you to validate your code modifications automatically and autonomously in real time to identify new bugs and ensure existing functionality remains intact.
But that’s not all! Learn how live static analysis complements this by continuously scanning your code in the background, providing instant feedback as you work. This seamless integration empowers you to make informed decisions on the fly, enhancing productivity and code quality like never before.
It’s a well-known fact in software development: the later a bug is found, the more expensive it is to fix. This is because finding and fixing defects later in the cycle requires significant detective work, consuming valuable engineering hours and disrupting developer workflows. When developers are pulled away from new development to fix old issues, productivity drops. Context switching and debugging take more time, especially when the code in question isn’t fresh in their minds. This often leads to crunch time right before releases, where features and fixes get mixed, increasing the likelihood of introducing new defects.
Early code validation, through methods like static analysis and unit testing, helps developers catch these issues before they become costly problems. While many teams already practice these methods, a significant risk often lies in code changes. Industry data suggests that 70-80% of defects originate from changed code. Even minor modifications can have ripple effects, causing unexpected behaviors or conflicts.
Modern applications are increasingly complex. Even minor code changes can impact many parts of the application in unexpected ways, making it difficult to test every possible scenario, especially if test suites have coverage gaps. As applications mature, these challenges become even more pronounced. Furthermore, developers are human; experience levels vary, and mistakes happen. Time constraints can exacerbate this, sometimes leading to shortcuts in testing policies.
While it’s a best practice to run tests and code scans after every change, this is often difficult to achieve with today’s rapid development and release schedules. Developers are often focused on delivering features quickly. Running a full code scan or a complete unit test suite can be time-consuming, especially with large codebases. There simply isn’t enough time to correlate individual code changes to specific impacted tests or to execute the entire test suite for a small change. This often leads to developers running only a few known tests instead of a comprehensive set.
Context switching between editing code and running tests also costs valuable time, causing these quality checks to be deferred to later in the workflow, often to CI or nightly jobs. Sometimes, developers might skip checks if a change seems straightforward, relying on automated jobs to catch issues. However, this can lead to missed defects that require later fixes.
Not performing adequate code checks can lead to:
The ideal time to perform checks like static analysis and run unit tests is within the IDE, right as code is being modified and before committing any changes. This requires tests and analysis processes to run very quickly. While some teams create commit suites, these often don’t know which tests are related to specific code changes and can miss unrelated regressions.
CI/CD jobs can run all unit tests and static analysis, ensuring checks are performed even if a developer forgets. However, for quick feedback, these jobs still need to run fairly quickly. This can be challenging with extensive functionality. The subset of tests and static analysis scope needs careful selection to fit within the timeframe. Some projects even separate slow tests into different jobs, but this can reduce confidence in the checks being run.
If a code change causes a regression or violation, the failure might not be apparent until a nightly test run. This is where live unit testing and live static analysis come in. These tools push these processes to the very moment code changes are being made inside the IDE.
The goal of continuous testing in the IDE is to enable teams to validate modified code automatically as changes are made, without manual intervention. This minimizes developer disruption by finding issues before code check-in, reducing the likelihood of problems being found later. These features provide real-time feedback on code modifications directly in the IDE, eliminating the need to wait for nightly or CI/CD runs.
The workflow typically involves:
This cycle continues as more changes are made. Coverage data is also updated, highlighting the need for new tests or showing existing coverage for modified code. By the time all tests are passing and static analysis findings are addressed, developers can be more confident that their changes won’t cause downstream issues. Importantly, these background runs happen concurrently with development, not adding to the overall development time.
During a live demonstration, a developer using Parasoft Jtest within Eclipse (or IntelliJ) showcased the process. After making a code change – specifically, preventing the addition of a “normal” role – the plugin immediately identified two impacted tests. One test passed, while the other failed due to the thrown runtime exception. The static analysis also flagged a violation.
The developer could then:
By leveraging these tools, developers can quickly remediate issues, update tests, and submit code with greater confidence, knowing that their changes have been validated.
Implementing live unit testing and live static analysis offers several advantages:
Does live unit testing only work on Parasoft-generated test cases?
No, it works with any JUnit tests, including pre-existing tests and legacy projects. The tests are written like normal JUnit tests, ensuring compatibility.
How does Jtest know which tests are impacted?
Jtest uses code coverage data to map individual tests to modified lines of code. It also utilizes heuristics and naming conventions to intelligently locate impacted tests, even without code coverage.
What LLMs do you integrate with?
Jtest supports LLM providers compatible with open API standards, including Azure OpenAI, Gemini, and more. It can also connect to custom internal models if they adhere to the open API standard.
Can I use this based on Git history?
Yes, Jtest has an advanced feature that can identify code based on your Git history.
