CDS Test Double Framework Automation Tool
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Solution Overview
Problem
The manual generation of test classes for Core Data Services (CDS) views is a repetitive and time-consuming task, requiring significant engineer time and effort, which affects the efficiency and consistency of the process.
Innovation Solution
An automation tool for the CDS Test Double Framework is developed to generate local test classes based on templates, allowing for the creation of mock tables and data, and executing SQL queries to compare actual and expected results, thereby automating the generation of test classes for various CDS view types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual generation of test classes is performed, then flexibility and customization are maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The system enables self-service automation where the test class generation process serves itself through automated template selection and parameter extraction. The automation tool automatically identifies CDS view types, selects appropriate templates, and generates test classes without requiring manual intervention for each view, thereby dramatically reducing time consumption while maintaining consistency.
Solution Approach 2:
The invention applies parameter changes by dynamically selecting different test class templates based on the specific type of CDS view being tested. The system extracts parameters such as view type, underlying tables, and aggregation methods, then uses these parameters to automatically configure and generate the appropriate test class structure, achieving both speed and adaptability.
2Manufacturing precision
If manual test class generation is performed, then engineer judgment and customization are applied, but consistency and quality vary based on individual skills
Solution Approach 1:
The invention implements universality through a standardized template system that serves multiple CDS view types. A single automation framework handles interface views, aggregation views, calculation views, and other view types by selecting from a library of templates, ensuring consistent test class generation across different view types without requiring specialized manual intervention for each type.
Solution Approach 2:
The system uses copying by maintaining a library of pre-defined test class templates that can be automatically selected and instantiated based on the CDS view type. Instead of creating test classes from scratch for each view, the automation tool copies and adapts appropriate templates, ensuring consistency in test structure, methodology, and quality across all generated test classes.
3Productivity
If automation tool is implemented, then time efficiency and consistency are improved, but initial setup complexity increases
Solution Approach 1:
The automation framework is segmented into distinct modular components: template management module, view type identification module, parameter extraction module, and test class generation module. Each component handles a specific aspect of the automation process, making the overall system more manageable and easier to maintain despite its complexity. This segmentation allows the system to achieve high productivity through coordinated operation of specialized modules.
Data Source
AI summary
Computer-implemented methods for evaluating integrity of data models with improved efficiency by providing an automation tool for core data services test double framework. A package is selected where a semantic and reusable data model is expressed in data definition language. Subsequently, a class is selected to create local test classes. Local test class templates are generated for the package based on a class name and a package name. Then integrity of the data model is determined by comparing an actual result for the data model and an expected result for the data model. Related apparatus, systems, techniques and articles are also described.