Listed 9 Best Java Static Code Analysis Tools

How do developers avoid frequent app crashes? Automated analysis could be the answer. Java static code analysis tools help detect any potential security issues or risks and help improve Java application performance by automating the code review process.

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

What is static code analysis?

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

Benefits and Disadvantages of Static Code Analysis

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

Although static analysis tools offer many advantages for code analysis, there are some disadvantages. Reviewing compiled data often requires substantial manual effort. Additionally, because static analysis has no runtime context, it can miss concurrency issues that only arise during actual Java concurrency execution. Static tools can identify common concurrency bugs, but they cannot replicate the intricate timing issues and race conditions revealed by real-time Java concurrency tests.

Therefore, while extremely useful, static analysis is best combined with concurrent runtime testing to discover the full range of multithreaded defects. Using static and dynamic techniques provides a comprehensive view on correcting Java concurrency.

Static vs. static code analysis dynamics

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

The process begins with analyzing the code to build an abstract syntax tree (AST). A set of predefined rules or patterns are applied to it to examine the structure, syntax and semantics of the code. This helps developers detect any failures, contributing to a stable lifecycle development workflow with different programming languages.

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

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

Both types of analytical tools have a complementary effect on each other. SCA can detect potential problems early in the development lifecycle, while DCA tools can help with maintenance and provide insight into how code behaves once it's released.

The role of SCA in Java

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

The most common faults that tools can identify are dereferenced values ​​in null pointers, code segments that do not work correctly, duplicate code blocks, and wrong variables.

• Null pointer exceptions where the value can 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 some segment of code is not closed or released correctly.

 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
  }
 }

• Detection of duplicate 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);
 }
 }

• Signaling possible security vulnerabilities – inappropriate entry.

 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 violations of coding standards – incorrect 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 compliance with coding standards and maintain operation.

#1 Checking Style

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

The Checkstyle configuration is versatile and can support virtually any encoding standard. It can perform standard checks (included in the base distribution) that apply to the general Java coding style without the need for external libraries, and supporting checks are usually performed through a specialized filter to collect 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 the issues identified by the scan, highlighting the need for attention or improvements for specific files. By addressing the concerns listed, developers can ensure that code remains operational with minimal risk to the codebase.

Product Highlights:

• Specified naming conventions applied to names of packages, variables, interfaces, constants, etc.
• Different types of size violation reports.
• 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 classifies warnings into four categories: worrisome, worrisome, scary and scariest. It is built with Gradle and can be used standalone or through Apache Ant and Maven, Eclipse, and IntelliJ IDEA integrations.

Product Highlights:

• A broad set of bug patterns for detecting errors in Java code.
• A plugin architecture that allows users to extend its capabilities.
• Seamless integrations with popular build tools with automated scans and reporting options.

#3 PMDJava

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

By applying coding conventions and best practices, PMD ensures code consistency and readability. Seamless integration with build tools and IDEs enables automatic analysis during the build process with real-time feedback. Additionally, PMD offers a copy and paste detector that finds duplicate code in multiple languages.

Product Highlights:

• Rules-based analysis to detect potential problems in the code.
• CPD prevents duplicate code from occurring.
• Efficient integration with popular development tools that support automated performance.

#4 Coverage

Coverity is a scalable static analysis tool (SAST) that helps identify and resolve vulnerabilities and issues in code. With its advanced detection practices, it can evaluate bug issues and security patterns.

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

Product Highlights:

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

#5 JUnit

JUnit is a popular unit testing framework. It makes writing and executing test cases easier, reducing the chance of errors and increasing code reliability. JUnit allows developers to define and test cases through APIs and annotations.

JUnit is one of the best testing frameworks for Java-based applications because it evaluates complex codes. Its basic structure makes it easy to use. The range of tests is vast and can save you time and money in the long run. The only disadvantage is that the program may fail messages on report line numbers and error codes.

Product Highlights:

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

#6 Infer

The Infer static analyzer for mobile and desktop applications is designed to detect Java bugs in code before it is shipped. The main purpose of this static analysis tool is to report a list of potential 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 track failures through its static analysis techniques.

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

Product Highlights:

• Advanced analysis technique to detect a variety of bugs.
• A scalable static analysis tool suitable for projects of different sizes.
• Seamless integration with build systems like Gradle or Maven.

#7 jQAssistant

jQAssistant is an open source tool that assists in the analysis and control of software systems. This is scan+document=validate slogan is an excellent representation of the three main use cases this tool was created for:

1. Perform software analysis to gain insights into software systems.
2. Document implementation of targeted design and architecture.
3. Validate data through living documentation to avoid coding gaps.

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

Product Highlights:

• In-depth dependency analysis for architectural problems.
• Coding standards applied through rules-based analysis.
• Create integrations and tool configurations according to specific project requirements.

#8 SonarQube

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

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

SonarQube has a clear Sonar Quality Gate to avoid code issues. Provides high operability with multiple deployments. SonarQube also includes critical security rules for important languages ​​such as Java, C#, PHP, Python, TypeScript, and JavaScript.

As the industry standard for software quality analysis, SonarQube is trusted by more than 400,000 organizations. It enables shared and unified configurations for code integrity and integrates with IDEs via the SonarLint extension for immediate detection of code issues. SonarQube promotes shared code quality expectations across the enterprise.

Product Highlights:

• Ability to provide enterprise-level reporting and aggregation for security oversight.
• Sonar quality gate pass/no pass.
• 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 versions of Java up to Java 16 and is an official open source project of Inria and a member of the OW2 open source consortium. It provides a well-designed AST (Abstract Syntax Tree) with a powerful parsing and transformation API. It can analyze source files and build a complete and solid 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 quickly detect invalid programs.

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

Product Highlights:

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

Java Static Code Analysis Tools Comparison

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

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

Best practices for using Java static code analysis tools

After developers perform initial requirements research and design the implementation process, they must determine which tools will improve the building's performance.

Implementing 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.

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

Some static analysis tools allow custom integration or plugins that can cut manual work in half from the start. If the tool does not meet the necessary standards, read the documentation or consult the tool's community. Replace the tool as a last resort.

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

Conclusion

Java is a complex language and static code analysis tools can assist in the development process. These tools should always be combined with dynamic analytics and other tools.

More advanced tools will also allow developers to consider serverless Java as an option for more effective deployment. However, it is important to evaluate how this will impact the development process. There is always a learning curve that can also affect development timelines. Ultimately, developers must focus on providing the best software possible.

Common questions

What is static code analysis in Java?

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

Why is static code analysis important?

Static analysis allows programmers to reduce the time spent implementing code during the development process. By preventing errors from the start, Java static code analysis tools enable developers to deliver software faster and more effectively.

How does static code analysis work?

Static tools are used to determine whether the source code has errors while the application is not operating. It acts as a lever for dynamic analysis tools because both static and dynamics have an operational function that allows developers to automate the analysis 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 community support will likely perform better. Developers should research each tool before making a decision.

How can 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 must be implemented early in the development process. By performing regular checks and finding automated solutions, analytics tools can provide valuable insights into how the program is operating.

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