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According to recent reports, there are approximately 220 billion lines of code in customer production environments operating on COBOL and other legacy languages. Organizations are eager to modernize their mainframe applications to leverage the advantages of the Amazon Web Services (AWS) Cloud. This modernization enhances agility, fosters innovation, and allows companies to benefit from AWS’s ongoing commitment to improving cost/performance ratios. Additionally, this shift opens up a broader talent pool skilled in managing and automating workloads on AWS.
AWS Blu Age employs automated refactoring patterns within its Mainframe Modernization service. This technology can transform languages such as COBOL, PL/1, NATURAL, RPG/400, and COBOL/400 into dynamic Java services and web frameworks. The process focuses on fully automated refactoring while maintaining investments in existing business functions. It expedites the transition to Java, new data stores, and web frameworks. AWS Blu Age Refactor automatically generates modern applications from legacy monolithic mainframe or midrange source code. Applications written in COBOL, generated COBOL, PL/1, NATURAL, RPG/400, and COBOL/400, along with their respective underlying databases and data files such as DB2, DB2/400, VSAM, IMS, and IDMS, are transformed into modern distributed applications. These upgraded applications can utilize Angular, Java/Spring, PostgreSQL, or other databases, including Amazon Aurora and Oracle Database.
AWS Blu Insights offers comprehensive tools that facilitate the modernization of legacy application source code and their databases. These technical tools assist with inventory analysis, code assessments, code transformations, and test case captures. In the following sections, we will explore the capabilities of AWS Blu Insights through the lens of a typical modernization project.
AWS Blu Insights Throughout the Modernization Lifecycle
This section will outline how AWS Blu Age addresses the needs for mainframe migration and modernization projects. You will discover how AWS Blu Insights helps you gain visibility into your application inventory and visualize dependencies among components. Furthermore, it will demonstrate how the tool can automatically convert your code to Java.
Inventory Analysis
Modernizing applications begins with creating an inventory of all mainframe applications and their associated functions to identify candidates for refactoring. For organizations with legacy mainframe applications, manually cataloging applications can be time-consuming. AWS Blu Insights can conduct inventory analysis in just minutes for mainframe/midrange systems. This analysis reveals the volume of source code, the languages used, and the cyclomatic complexity of the code. Cyclomatic complexity measures the number of linearly independent paths through a program module, making it easier to modify and test programs with lower complexity.
Dependency Analysis
Once the inventory is complete, the next step involves identifying internal and external dependencies within the code base.
- Identifying Internal Dependencies and Entry Points: AWS Blu Insights provides dependency analysis for COBOL, generated COBOL, PL/1, NATURAL, RPG/400, and COBOL/400 applications with advanced visualization tools. This analysis reveals the lineage of components and their dependencies, including SQL and file nodes.
- Identifying External Dependencies: During this phase, you might uncover dependencies for which source code is unavailable, such as third-party applications that need adjustments to accommodate refactored code.
- Identifying Missing Components: After assessing all internal and external dependencies, unresolved dependencies can be categorized as missing components. This may include copybooks, DCLGENs, COBOL source files, and file definitions. It is crucial to compile a complete code base before proceeding to the refactoring stage.
- Identifying Dead Code: AWS Blu Insights offers predefined queries to create a dependency analysis graph, enabling the identification of isolated nodes that are neither calling nor being called by other nodes.
- Data Analysis: The dependency analysis generates a CRUD (Create, Read, Update, Delete) report, helping identify database usage within an application and providing a file-level usage report to clarify how a specific datastore interacts with the application.
Automated Code Transformation
Once the application code base undergoes dependency analysis, the refactoring process can commence. AWS Blu Insights conversion follows three steps:
- Transform: This step imports, parses, and analyzes the legacy code base to create intermediary files in JSON or domain-specific languages (DSL).
- Refactor: During this step, the tool automatically refactors artifacts produced by the Transform action, using configuration parameters to optimize or adjust specific conditions based on the application.
- Generate: This final step produces all Java/Spring Boot elements from the intermediary files. The straightforward interface facilitates the conversion of these artifacts, and this process can run in parallel to enhance code generation speed.
Capturing and Managing Test Scenarios
Validation of functional equivalence is essential post-modernization. AWS Blu Insights provides a capture and replay feature for recording test cases, which can connect to mainframes or midrange environments via TN3270 or TN5250 terminals. User interactions are recorded through screen captures and stored in JSON and Selenium files.
To replay test scenarios on modernized applications, testers can utilize both video and JSON recordings. You can manage, track, and update testing workflows within AWS Blu Insights using the “Test Scenarios” feature located under the “Management” tab.
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