9 Best Java Static Code Analysis Tools Listed

How do developers prevent frequent application glitches? Automated analysis could be the answer. Java static code analysis tools help detect any issues or potential security risks and help enhance the Java application’s performance by automating the code review process.

We’ll explore some of the market’s best tools that can supercharge the Java development services workflow and create more robust code.

What Is Static Code Analysis?

Static code analysis is a technique for examining the source code without executing it. By analyzing sets against coding rules, this advanced static analysis tool can read code for errors and ensure that it adheres to standards and best practices.

Benefits and Drawbacks of Static Code Analysis

In addition to contributing to overall performance enhancements in code, these tools can find SQL injection or cross-site scripting (XSS) vulnerabilities. Identifying inefficient code patterns can further bring performance improvements and maintain code consistency, thus contributing to producing more secure code.

While static analysis tools offer many advantages for analyzing code, there are some drawbacks. Reviewing the compiled data often requires substantial manual effort. Additionally, since static analysis lacks runtime context, it may miss concurrency issues that only arise during actual Java concurrency execution. Static tools can identify common concurrency bugs, but cannot replicate the intricate timing issues and race conditions that live Java concurrency testing reveals.

So while extremely useful, static analysis is best paired with concurrent runtime testing to uncover the full range of multi-threaded defects. Utilizing both static and dynamic techniques provides comprehensive insight into Java concurrency correctness.

Static vs. Dynamic Code Analysis

Static and dynamic methodologies are used for different purposes. The static code analyzer examines the app’s source code against a set of rules before the program is activated.

The process starts with parsing the code to build an abstract syntax tree (AST). A set of predefined rules or patterns is applied to it to examine the code structure, syntax, and semantics. This helps developers spot any glitches, contributing to a steady lifecycle development workflow with different programming languages.

Meanwhile, dynamic code conducts the analysis during execution capturing behavior, memory usage, and Java performance data to detect errors during runtime.

The main difference between static and dynamic code analysis is in its application. Static code analysis is performed while the application is not running, while dynamic code requires the app to be engaged.

Both types of analytics tools have a complementary effect on each other. SCA can catch potential issues early in the development lifecycle, while DCA tools can aid maintenance and provide insight into code behavior once launched.

The Role of SCA in Java

These tools operate before running a program. It reveals any potential glitches in the application architecture, making it easier to understand runtime behaviors.

The most common glitches the tools can pinpoint are dereferenced values on null pointers, code segments that are not properly functioning, duplicate code blocks, and wrong variables.

• Null pointer exceptions where the value may be dereferenced.

public class NullPointerExample {
    public static void main(String[] args) {
        String str = null;
        int length = str.length(); // Potential null pointer exception
        System.out.println(length);
    }
}

• Resource leak identification occurs if any code segments are not properly closed or released.

public class ResourceLeakExample {
    public static void main(String[] args) throws IOException {
        FileWriter writer = new FileWriter("output.txt");
        writer.write("Hello, World!");
        // Missing writer.close() to release the file handle
    }
}

• Detecting duplicated code blocks or methods.

public class CodeDuplicationExample {
    public static void main(String[] args) {
        int num1 = 5;
        int num2 = 10;

        int sum = num1 + num2;
        System.out.println("Sum: " + sum);

        // Duplicate code block
        int product = num1 * num2;
        System.out.println("Product: " + product);
    }
}

• Flagging potential security vulnerabilities—improper input.

public class InputValidationExample {
    public static void main(String[] args) {
        String input = args[0];
        if (input.equals("admin")) {
            grantAccess();
        } else {
            denyAccess();
        }
    }
    
    public static void grantAccess() {
        System.out.println("Access granted!");
    }
    
    public static void denyAccess() {
        System.out.println("Access denied!");
    }
}

• Reporting coding standard violations—wrong variable naming.

public class VariableNamingExample {
    public static void main(String[] args) {
        int x = 5; // Poor variable name

        int result = multiplyByTwo(x); // Poor method name
        System.out.println("Result: " + result);
    }
    
    public static int multiplyByTwo(int number) {
        return number * 2;
    }
}

Java Static Analysis Tools

These tools offer many features that help developers maintain quality and reliability. With so many Java applications, having access to performance-enhancing tools can ensure coding standard compliance and maintain operation..

#1 Checkstyle

Checkstyle is a static code analysis tool that confirms the Java source code meets the expected standard. It has automated features identifying layout, formatting, class, and method design issues.

The configuration of Checkstyle is versatile and can support almost any coding standard. It can perform standard checks (included in the base distribution) that apply to the general Java coding style without needing external libraries, and holder checks are usually performed through a specialized filter to gather information.

Checkstyle is easy to use and can be installed in an IDE as a plugin integrated into build management tools like Maven, Eclipse, and IntelliJ IDEA. It provides an overview of issues the check identifies, highlighting the need for attention or enhancements for specific files. By addressing the listed concerns, developers can ensure that the code maintains operational with minimal risk to the codebase.

Product highlights:

• Enforced specified naming conventions for package names, variables, interfaces, constants, etc.
• Different report types of size violations.
• Reporting missing Javadoc comments.

#2 Spotbugs

A fork of the previous FindBugs, Spotbugs identifies bug patterns in code that are likely to be errors. Spotbugs is an open-source Java code review tool and static analyzer that lists all inconsistencies. Still, the developer must determine the next course of action.

The tool ranks warnings into four categories: of concern, troubling, scary, and scariest. It’s built withGradle and can be used independently or through Apache Ant and Maven integrations, Eclipse and IntelliJ IDEA.

Product highlights:

• A wide-ranging set of bug patterns for detecting mistakes in Java code.
• A plugin architecture that allows users to extend its capabilities.
• Seamless integrations with popular build tools with automated scans and report generation options.

#3 PMD Java

PMD is a source code analyzer that detects recurring programming mistakes, code styles, and performance violations. In addition to Java, it supports Apex, PLSQL, Apache Velocity, and more.

By enforcing coding conventions and best practices, PMD ensures code consistency and readability. Seamless integration with build tools and IDEs allows an automatic analysis during the build process with real-time feedback. In addition, PMD offers a copy-paste-detector that finds duplicated code in multiple languages.

Product highlights:

• Rule-based analysis fpr detecting potential issues in code.
• CPD prevents duplicate code occurrence.
• Efficient integration with popular development tools that support automated performance.

#4 Coverity

Coverity is a scalable static analysis tool (SAST) that aids in identifying and resolving vulnerabilities and issues in code. With its advanced detection practices, it can evaluate bug and security pattern problems.

One of the selling points of Coverity is the Code Sight IDE plugin that allows developers to write code as the analysis runs in the background with minimal disruption. Coverity can be built into the pre-existing DevOps pipelines with CI, SCM, and issue-tracking integrations.

Product highlights:

• Advanced defect detention for complex bugs and code vulnerabilities.
• Vigorous security scans against potential threats.
• Smooth integration with CI/CD pipelines allowing analysis and code improvement automation.

#5 JUnit

JUnit is a popular unit testing framework. It makes it easier to write and execute test cases, reducing the chance of mistakes and increasing code reliability. JUnit enables developers to define and test cases through APIs and annotations.

JUnit is one of the best test frameworks for Java-based applications because it assesses complex code. Its basic structure makes it easy to use. The test range is vast and can save time and money in the long run. The only downside is that the program can fail messages on reporting line numbers and error codes.

Product highlights:

• Simple and intuitive test case creation.
• A robust framework for test execution and assertions.
• Detailed reference documentation is available for users.

#6 Infer

Infer static analyzer for mobile and desktop applications was developed to detect Java bugs in code before it ships. The main goal of this static analysis tool is to report a potential list of bugs from Objective-C, Java, or C code before release.

By identifying memory leaks and null pointer exceptions in the programming language, Infer allows developers to trace the glitches through its static analysis techniques.

The static program analyzer is deployed within Meta, running continually in all Facebook apps for Android and iOS. Infer has extensive codebase scalability, but it may require reconfiguration to align with specific project requirements.

Product highlights:

• Advanced analysis technique for detecting a range of bugs.
• A scalable static-analysis tool suitable for different-sized projects.
• Seamless integration with building systems like Gradle or Maven.

#7 jQAssistant

jQAssistant is an open-source tool that helps with the analysis and control of software systems. Its scan+document=validate slogan is an excellent representation of the three main use cases this tool is built for:

1. Performing software analytics to gain insight into software systems.
2. Documenting the implementation of targeted design and architecture.
3. Validating data through living documentation to avoid a coding gap.

jQAssistant plugin-based architecture ensures operation in different technologies, Java frameworks, and architectural concepts. Also, if the plugins are insufficient, they can be customized.

Product highlights:

• In-depth dependency analysis for architectural issues.
• Enforced coding standards through rule-based analysis.
• Build tool integrations and configuration to the project-specific requirements.

#8 SonarQube

SonarQube is a code quality and security solution designed for teams and enterprises. It offers deep integration into the enterprise environment, enabling the consistent and reliable deployment of clean code. SonarQube provides flexibility and governance as an enterprise tool and is self-managed, allowing customization

SonarQube helps deliver high-quality code systematically and supports over 30 languages, frameworks, and IaC platforms. SonarQube integrates with popular DevOps platforms such as GitHub, GitLab, Azure, and Bitbucket.

SonarQube has a clear go/no-go Sonar Quality Gate to prevent code issues. It offers high operability with various deployments. SonarQube also includes critical security rules for important languages like Java, C#, PHP, Python, TypeScript, and JavaScript.

As the industry standard for software quality analysis, SonarQube is trusted by over 400,000 organizations. It allows for shared and unified configurations for code health and integrates with IDEs through the SonarLint extension for on-the-fly code issue detection. SonarQube promotes shared code quality expectations across the enterprise.

Product highlights:

• Ability to provide enterprise-level reporting and aggregation for security oversight.
• Go/no-go Sonar Quality Gate.
• Super-fast analysis for actionable Clean Code metrics.

#9 Spoon

Spoon is an open-source library for analyzing and transforming Java source code. It supports modern Java versions up to Java 16 and is an official Inria open-source project and a member of the OW2 open-source consortium. It provides a well-designed AST (Abstract Syntax Tree) with a powerful analysis and transformation API. It can parse source files and build a complete and sound program model using different Java IDEs.

The text version of a Spoon model is well-formed and semantically equivalent to the original program. Spoon’s analysis and transformation API is intuitive and regular, and its transformation operators are designed to detect invalid programs quickly.

Although Spoon can consume source code for older versions of Java, it requires JDK 11+ to run. The design philosophy includes being close to the language concepts and warning about invalid programs.

Product highlights:

• Powerful API for programmatic Java source code manipulation.
• Automated operations and generation of metrics and documentation.
• Suitable for different purposes: code analysis, generation, and application in educational projects.

Comparison of Java Static Code Analysis Tools

Determining the effectiveness of each STA tool varies based on performance focus:

• Checkstyle and PMD enforce coding standards maintenance and detect style and structure glitches.
• Spotbugs, Coverity and Infer are designed to identify bugs before launching.
• jQAssistant analyzes dependencies while SonarQube implements quality and security checks.
• Spoon provides an API for programmatic code analysis and transformation, and it can work with various Java GUI frameworks.

Best Practices for Using Java Static Code Analysis Tools

After the developers have performed the initial requirements research and designed the implementation process, they should determine which tools will enhance building performance.

The implementation of a specific tool has a double value. First, it will define the coding and configuration standards and how they will be applied. This is also important for further testing of basic functions and system performance.

Gathering data after each analysis is vital. After identifying any potential glitches, find the best solutions to automate the process as much as possible. Make it a standard practice to include the reviews in the development and maintenance process.

Some static analysis tools allow custom integration or plugins that can cut manual labor in half from the start. If the tool doesn’t meet the necessary standards, read the documentation or look to the tool’s community. Substitute the tool as a last resort.

For businesses that may not have the capacity to conduct extensive static code analysis in-house, outsourcing Java development can be a viable solution. Outsourcing can provide access to skilled developers who are proficient with these tools and can manage the code quality and security checks. This option can free the in-house team to focus on other critical areas of the project while ensuring that the codebase remains robust and secure.

Conclusion

Java is a complex language, and static code analysis tools can aid the development process. These tools should always be paired with dynamic analysis and other tools.

More advanced tools will also enable developers to consider serverless Java as an option for more effective deployment. However, it’s important to assess how this will impact the development process. There’s always a learning curve that may impact development timelines, too. Ultimately, developers must focus on providing the best software possible.

FAQ

What is static code analysis in Java?

Static code analysis in Java is a methodology for examining the source code. By using SCA tools, developers can identify any potential performance or security issues, even when the program is not running.

Why is static code analysis important?

Static analysis allows programmers to cut down time spent on code implementation during the development process. By preventing errors early on, Java static code analysis tools allow developers to deliver software more quickly and effectively.

How does static code analysis work?

Static tools are used to determine whether the source code has errors while the application isn’t operating. It acts as leverage for dynamic analysis tools because both static and dynamic have an operational function that allows developers to automate the analytics process and save time in the long run.

What are the best static code analysis tools for Java?

The best static code analysis tools depend on personal preferences and requirements. Any Java tools that have larger libraries and support from the community will likely perform better. Developers should research each tool before making a decision.

How do I use static code analysis tools effectively?

Effective use of a static code analysis tool depends on the developer’s experience. In any case, it should be implemented early in the development process. By performing regular checks and finding automated solutions, analysis tools can provide valuable insight into how the program is operating.

If you enjoyed this, be sure to check out one of our other Java articles:

• Java Integration Testing Explained With Examples
• 10 Best Java NLP Libraries & Tools
• Should You Use Kotlin or Java For Android Development?
• The Pros and Cons of Java Development
• What’s New in Java 19? Should You Make the Leap?