Software Deployment Effort Estimation via Landscape Analysis
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Solution Overview
Problem
In IT systems, the effort and scope required for deploying software support and enhancement packages are difficult to estimate, leading to delayed deployments due to a lack of reliable analysis, which should be comprehensive and require minimal customer input.
Innovation Solution
A system and method that estimates the effort and scope of software support and enhancement package deployments by identifying affected modifications, regression testing needs, and test planning, using a software manager that receives data from both the software vendor and customer systems, with analysis performed in the background to provide a comprehensive yet non-labor-intensive effort and scope analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a comprehensive effort and scope analysis is performed for software deployment, then the reliability of deployment planning is improved, but the complexity and labor required for the analysis increases
Solution Approach 1:
The system performs effort and scope analysis in advance of actual software deployment by examining the support package contents, identifying affected custom objects, and calculating deployment characteristics before the customer must make deployment decisions. This preliminary analysis provides reliable deployment planning information without requiring the customer to perform complex manual assessments.
Solution Approach 2:
The software vendor's system automatically performs the effort and scope analysis using the customer's landscape data and the support package information. The analysis is generated autonomously by comparing the support package contents against the customer's custom objects, eliminating the need for the customer to manually conduct complex analysis while still providing comprehensive results.
2Measurement precision
If a comprehensive effort and scope analysis is performed for software deployment, then the accuracy of effort estimation is improved, but the time and resources required for analysis increase
Solution Approach 1:
The system calculates deployment effort and scope characteristics before deployment decisions are made by analyzing the support package contents and comparing them against the customer's landscape. This advance calculation provides accurate effort estimation without delaying deployment planning, as the analysis is performed automatically in the background.
Solution Approach 2:
The system replaces manual effort estimation processes with an automated computer-based analysis system. The processor automatically compares support package contents against customer landscape data, identifies affected objects, and calculates deployment characteristics, eliminating the time-consuming manual analysis while maintaining or improving accuracy.
3Reliability
If detailed analysis of customer modifications and custom code is performed, then the completeness of deployment scope analysis is improved, but the customer input and effort required increases
Solution Approach 1:
The system autonomously performs detailed analysis of the customer's landscape by automatically comparing support package contents against the customer's custom objects and modifications. The customer's system provides the landscape data, but the actual analysis of affected modifications and custom code is performed automatically by the vendor's system without requiring the customer to manually examine or document their modifications.
Solution Approach 2:
The system performs detailed analysis of customer modifications and custom code in advance of deployment by examining the support package contents against the customer's landscape data. This preliminary detailed analysis identifies all affected objects and calculates the complete deployment scope without requiring the customer to perform manual documentation or analysis of their custom code.
Data Source
AI summary
A plan to modify a software system is analyzed to identify objects of a first entity that are affected by the plan. An impact on a first part of the system is determined. Software modifications of a second entity in a second part of the system that are associated with the affected objects of the first entity are identified. Usage statistics of the first entity relating to the affected objects and usage statistics of the second entity relating to the software modifications are identified. An impact of the modifications to the affected objects on the software modifications of the second entity is determined. A first estimate of an effort to implement the modifications to the system is developed. A business blueprint is developed for the second entity. A trace of the affected objects and a trace of software executables are generated. A test plan is generated using the traces.