Static Code Analysis Approaches for Handling Code Quality

Key Takeaways

• Static analysis is an important tool in an organization’s arsenal to keep code quality up.

• It can reduce code defects and improve maintainability, but it can also be rife with false positives and might require multiple tools to get the coverage your organization needs.

• Even with those issues, static analysis is an important aspect for any organization to deliver quality code.

• By combining CloudBees Smart Tests with static code analysis, organizations can improve the efficiency and effectiveness of their testing process and ensure the quality and reliability of their software.

Software development has come a long way since the era of punch cards, when developers had to painstakingly punch holes into paper, cross their fingers, and wait to see if their program would run as expected. 

Today, development is fast, continuous, and collaborative. A single line of code can trigger feedback within seconds, reshaping the developer experience and accelerating delivery cycles.

One key enabler of this evolution is static code analysis. By analyzing source code without executing it, teams can catch potential issues early, before they ever reach runtime. However, while static analysis improves code quality, it can also introduce performance trade-offs if not implemented strategically.

To stay efficient at scale, teams need more than just early feedback—they need relevant feedback. That’s where CloudBees Smart Tests capabilities come in. By aligning test execution with actual code changes, engineering teams can run the right tests at the right time, reducing unnecessary test cycles and accelerating time to delivery.

Static analysis is still foundational, but it’s most effective when paired with intelligent tooling that focuses resources where they matter most. Modern DevOps success depends on precision, not just automation.

Static code analysis techniques are used to identify potential problems in code before it is deployed, allowing developers to make changes and enhance the software's quality. Three techniques include syntax analysis, data and control flow analysis, and security analysis.

Syntax analysis involves checking the code for syntax errors and coding standards violations, such as missing brackets, invalid variable names, and improper indentation.

Most modern IDEs have syntax analysis built in. For example, Visual Studio and Visual Studio Code have code analysis built into the IntelliSense feature.

 In the below screenshot, Visual Studio 2022 is surfacing a C# syntax error for a missing semicolon before the code has even compiled. 

Syntax analysis helps developers catch bugs before they even hit the “run” button.

This technique involves tracking the flow of data through the code, in order to identify potential issues such as uninitialized variables, null pointers, and data race conditions. Control flow analysis is similar and helps identify bugs, such as infinite loops and unreachable code.

Many modern compilers include data flow analysis and control flow analysis as built-in features. They surface any findings as compile-time warnings or errors. For example, the Clang toolset for the C-language family automatically performs flow analysis during compilation.

For languages that aren’t compiled, such as Python, you can manually use a data & control flow analysis tool like CodeQL.

Security static analysis involves checking the code for potential security vulnerabilities, such as buffer overflows, cross-site scripting, and injection attacks. They can also scan your 3rd party dependencies for packages with known vulnerabilities and detect credentials checked into your source code.

Static application security testing (SAST) tools include:

• Snyk Code

• Microsoft CredScan

• Github Advanced Security

• SemGrep

Example: Static Application Security Testing With SemGrep

SemGrep is a popular free application security static analysis tool. Running SemGrep’s security analyzer on a project with insecure code, like OWASP Juice Shop, turns up dozens of security vulnerabilities in the code.

1. Improved code quality and reliability. Static analysis augments developers by helping catch issues early. The result is better, more reliable code.

2. Early identification and prevention of issues. Rather than finding a bug when it already is causing customer issues, static analysis can help find them before even running the code the first time.

3. Increased efficiency and cost savings. No need to keep rerunning tests in your CI/CD suite if developers can catch issues early with static analysis. This saves on cloud computing costs and speeds up development cadence.

While Static Code Analysis helps teams catch issues earlier, it is not a perfect approach and can result in false positives, false negatives, and is limited by the toolsets.

If you ask any developer what they dislike about static analysis tools, you’ll hear one answer over and over again: False positives.

Static analyzers utilize heuristics and rule sets to identify findings within a line of code. However, they’re not perfect and frequently turn up results that aren’t actually issues in the context.

For the following line of code example:

// Set the password policy so that user passwords expire after 365 days.
passwordExpiry = 365;

An unsophisticated security static analyzer sees the string “password” and flags this as a credential in source code. Upon examination, it’s clearly not a secret and requires no code change. It requires extra dev time to investigate this issue and flag it as a false positive, which can be frustrating.

Software code can be complex and static analyzers might miss nuances of a situation. Therefore, you can’t rely on static analyzers to find 100% of the bugs you write.

Your cloud environment has two nearly identical server configuration files: serverprod.config (production) and servertest.config.dev(test environment). 

A static analyzer is configured to scan files with the .config file extension and correctly finds issues in serverprod.config, but it misses the same issues in the servertest.config.dev file because that doesn’t match its filename pattern.

There’s not a single static analysis tool that does it all. Many are specialized for different environments, file types, and types of scanning. An organization might need one tool for security scanning, a different tool for their Typescript frontend, a third tool to scan the Golang backend, and yet another static analyzer for their server configuration Terraform files. Each tool delivers value, but setting up and maintaining all of them can be onerous.

Here are some of the top tools for static code analysis:

1. SonarQube: a popular open-source static code analysis tool that supports a wide range of programming languages and integrates with various build and deployment tools.

2. Checkstyle: an open-source static code analysis tool that checks for coding style and conventions violations in Java code.

3. FindBugs: an open-source static code analysis tool that identifies potential issues in Java code, including performance problems, security vulnerabilities, and coding standards violations.

4. PMD: an open-source static code analysis tool that checks for issues in a variety of programming languages, including Java, C++, and Python.

5. Veracode: a commercial static code analysis tool that offers a range of features for identifying and addressing security vulnerabilities in software.

6. Coverity: a commercial static code analysis tool that is focused on identifying and preventing security defects in code.

7. ESLint: An open source project to help find and fix problems in JavaScript code. If you’re using TypeScript, check out the typescript-eslint variant.

These tools offer a range of features, support different programming languages, and have different types of software licenses. It is important to consider licensing and the specific needs and requirements of an organization when choosing a static code analysis tool.

Here is a simple example of JavaScript static code analysis using ESLint:

This single line of code has two issues that ESLint quickly finds:

1. Strings need to use double quotes

2. A variable is assigned a value, but is never used.

By identifying and addressing these issues through static code analysis, organizations can improve the quality and reliability of their software. Want to try ESLint yourself? You can use the ESLint Playground online.

CloudBees Smart Tests is a technique that uses machine learning to analyze past test results and predict which tests are likely to fail in the future. This can be used in conjunction with static code analysis to improve the efficiency and effectiveness of the testing process.

Static analysis is only one facet of a software quality strategy. Most organizations use static analysis to augment their functional end-to-end software tests. Static analysis tests are only one of the types of tests that might run in a CI/CD pipeline.

One way that CloudBees Smart Tests can help static code analysis is by prioritizing the testing of code that is most likely to contain issues. By analyzing past test results and identifying patterns that are correlated with failure, CloudBees Smart Tests can help focus the testing effort on the most important or problematic areas of the code. This can help ensure that the most critical issues are identified and addressed as soon as possible, while also reducing the time and resources spent on unnecessary testing.

Static code analysis remains a foundational practice for identifying issues early, enhancing maintainability, and reducing defects throughout the software development lifecycle. But as applications grow more complex and release cycles accelerate, development teams need more than just insight—they need intelligent prioritization. 

By layering CloudBees Smart Tests on top of your static analysis strategy, you can transform raw code insights into actionable test execution, ensuring teams focus on what matters most. The result is faster feedback loops, more efficient pipelines, and higher confidence in every release. When paired strategically, static analysis and Smart Tests don’t just improve code—they help scale quality across your entire software delivery process.

Ready to Boost Code Quality and Test Efficiency?

Static analysis is powerful, but it’s even more effective when combined with the right testing intelligence. See how CloudBees Smart Tests can help your team focus testing where it matters most, reduce pipeline noise, and ship with confidence. Request a demo today to see it in action.