Source
COBOL
An electricity company’s legacy application, with energy data records and query and extraction functions.
Customer story · modernAIze
Energy data.
From COBOL to Spring.
Working with Roger Casals, Wattium used modernAIze to access the energy data needed for an energy storage pilot. The information was managed in an electricity company’s legacy COBOL application. The work integrated querying and extracting that data into a Java and Spring service accessible from the pilot application.
Energy technology · Application modernization
- Source technology
- COBOL
- Target platform
- Java + Spring
Data must retain
its meaning.
Wattium develops energy storage technology. This project addressed a specific pilot requirement: incorporating energy data from an electricity company that managed it through a legacy COBOL application. The work focused on adapting query and extraction functions to make that information available to the pilot application through Java and Spring.
Integration depends on details that a language change alone does not resolve. Units, decimal precision, measurement periods and the distinction between measured, estimated and aggregated values determine how the data is interpreted. The service also needs a clear contract for queries, responses and the handling of missing data or invalid requests.
The modernization path
Understand the data.
Make it accessible.
modernAIze supports reviewing the source query and extraction functions and their interpretation rules to integrate them into a Java and Spring service.
Understanding & design
modernAIze
Data definitions, interpretation rules and access functions linked to the source logic.
Target
Java + Spring
An energy data query and extraction service, accessible from the energy storage pilot application.
A supporting technical demonstration
Power, time
and a precise result.
The screens illustrate how modernAIze makes units and decimal precision explicit through an interval-energy calculation, from COBOL to Spring. They use synthetic material from our COBOL demonstration. They contain no real project code and do not represent the pilot’s query and extraction functions.
Follow the calculation in source order.
The example first calculates the power deficit, then converts that value over a time interval into energy. The source flow keeps the operation order and rounding point visible.
Separate the calculation from its interface.
A request defines the quantities and units. A service owns the arithmetic. A Spring controller provides the application boundary, with invalid inputs handled explicitly.
Inspect the Java calculation.
BigDecimal makes the decimal arithmetic explicit. The final conversion declares its scale and rounding mode, giving reviewers a specific behaviour to compare with the source.
The engineering value
Data with meaning.
A maintainable service.
Make interpretation rules explicit
Connect data to its units, decimal precision, measurement periods and status as measured, estimated or aggregated values to preserve its meaning.
Make integration responsibilities explicit
Separate query and extraction functions from the service interface, request validation and error handling.
Turn edge cases into review criteria
Define how the service should respond to missing data, invalid requests and different measurement periods. These cases make acceptance criteria precise.
Start with a
data requirement.
Bring the source code, its data definitions and representative queries and responses. We can establish what meaning must be preserved and how to integrate access to that data into your application.
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