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Java Dependency Management and Impact Analysis with CodeLogic

Overview

Understanding and managing dependencies in complex Java applications at the enterprise level often poses challenges. Traditional tools focus either on top-down or bottom-up approaches, yet neither offers a comprehensive visibility of the complete codebase, particularly at the level of APIs, endpoints, database schemas, and JDBC connections.
 
CodeLogic revolutionizes Java dependency management. By conducting binary Jar/War file scans, runtime environment scans in test environments, and scans of JDBC database schemas, CodeLogic produces a detailed dependency graph, enabling effective impact analysis at the code level.
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Java: The Backbone of Modern Business Operations

Enterprise-level Java deployments are often foundational to business infrastructures. Several factors contribute to this ubiquity:

  • Stability and depth of the Java language
  • Wide array of development libraries
  • Application frameworks with sensible defaults, like Spring, Grails, Vaadin, Struts, Spark, and others
  • Efficient java database integrations via JDBC
  • Robust and performant data streaming/messaging frameworks such as Apache Kafka, Rabbit MQ, Java Messaging Service and Jakarta 

Challenges in Managing Java Dependencies at Scale

As your enterprise scales, so does the complexity of managing java dependencies. You may encounter issues such as:
 
  • Loss of original developers or documentation for older business rules
  • Missing inter-application dependencies in Java IDEs
  • Lack of code-level transparency in closed-source frameworks
  • Difficulty in predicting the impact of Java code changes due to complex systems

In short, it is extremely complicated to see all interconnections and all endpoints across all Java applications, classes, and methods within a single business. It’s even more difficult to know with certainty what code changes may produce regressions.

Integrating Software Intelligence in Java Development

Software Intelligence tools evolved as solutions for the problems above. These tools tend to access your application environment through either a top-down or bottom-up approach.

Top-down software intelligence tools

Top-down planning and monitoring tools provide data and guidance for your entire application environment. Some of the questions these top-down tools answer are:

  • How do all the applications fit together?
  • Which parts of the digital infrastructure are ripe for transformation to microservices?
  • Which applications are the performance bottlenecks?

Application monitoring tools (APMs) also provide top-down perspectives of how data is moving through all your databases and Java applications.

Because of their high-level view, top-down software intelligence tools are fantastic for architects and SREs alike: they help understand how the whole picture fits together and represent where the potential fault points are in your Java applications and information workflows.

For example, you may have a distributed Grails application running on the Spring Boot framework utilizing FusionAuth authentication, all drawing from multiple SQL databases via JDBC connections. The top-down approach shows what connections are made between each framework, database, or sub-application.

Java Software Intelligence

Simplified diagram of the Java application development process
Figure 1 caption: A simplified view of the Java application development process, including Planning, Coding, Building, Deploying and Monitoring an application. Shown in green are categories of software intelligence tools that assist at several stages. Shown in grey are specific software or frameworks used at specific stages.

Bottom-up software intelligence tools

Many software intelligence tools gather data at the code or repository level, often integrating directly with IDEs or GitHub. Source-code scans are adept at gathering highly localized, code and project-specific details. These rudimentary scans can also be triggered through your build or deployment phases through Maven, Gradle and Ant plugins or activated as part of your CI/CD pipeline through Jenkins, Travis CI, Terraform and others. The assembled relationship data, however, is limited to the domain of the project itself, yet provides additional features like code-linting, code quality analysis and code-styling.

In both cases, the focus is on one level of visibility: ground-level source code or 30,000-foot architectural overview. What’s obscure in both cases are the relationships between the code and a multi-application ecosystem.

Next-generation Java Continuous Software Intelligence

CodeLogic sets itself apart by offering a granular and overarching view of your entire application ecosystem, including Java database connections via JDBC. It scans Java, Kotlin, or Scala JAR and WAR files to detect endpoints, methods, classes, and relationships, building a comprehensive dependency graph of your Java applications and enabling actionable impact analysis.

Continuous Scanning for Real-Time Insights

CodeLogic’s platform continually scans your Java applications and JDBC databases:

  • Real-time Java dependency graphs
  • The ability to perform deep impact analysis to understand the implications of code changes large and small

Java Continuous Software Intelligence

Diagram of the Java Application development process with CodeLogic Continuous Software Intelligence
Figure 2 caption: Building off of the process presented in Figure 1, the CodeLogic labels show where the CodeLogic Continuous Software Intelligence Platform gathers data and provides insights.

The CodeLogic Continuous Software Intelligence Platform enables both top-down and bottom-up approaches to software system analysis and provides a continuous spectrum of relationship data everywhere in between. Finally, a solution exists showing inter-application relationships, across projects, databases and microservices.

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