Software Change Impact Prediction via Compatibility and Access Analysis
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Solution Overview
Problem
Current zero downtime maintenance approaches for business software applications do not guarantee full availability during software deployment, often requiring reduced functionality and limiting developer options, as they fail to accurately assess the impacts of software changes on system availability and user experience.
Innovation Solution
The implementation of a method that calculates the intersection of compatibility and access analysis tables to generate ratings of expected impacts, allowing developers to evaluate options and minimize disruptions, by identifying potential conflicts and access frequencies during the development process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If zero downtime maintenance approach is used to deploy software changes, then system availability is maintained, but the scope of available functionality is reduced during deployment
Solution Approach 1:
The system performs preliminary analysis of software changes before deployment to predict their impact on system functionality. By calculating the intersection of compatibility analysis tables (identifying changed objects) and access analysis tables (identifying frequently accessed objects), the system determines which functionalities will be affected, allowing planners to prepare alternative deployment strategies or communicate expected limitations to users in advance.
2Productivity
If software changes are deployed without impact assessment, then deployment speed is increased, but system disruptions and user experience degradation occur
Solution Approach 1:
The system performs preliminary impact assessment by analyzing software changes against historical access patterns before deployment. The compatibility analysis identifies which system objects will be modified, while the access analysis identifies which objects are frequently accessed. The intersection calculation predicts which functionalities will be disrupted, allowing teams to adjust deployment timing, notify users, or prepare mitigation strategies in advance, thereby maintaining deployment speed while reducing harmful disruptions.
3Measurement precision
If detailed impact analysis is performed on software changes, then deployment planning accuracy is improved, but analysis time and computational resources increase
Solution Approach 1:
The system uses historical access data as a copy of actual usage patterns to predict future impact without requiring detailed analysis of each potential user interaction. By analyzing past access frequencies and patterns stored in the access analysis table, the system efficiently estimates which functionalities will be affected by software changes, achieving high accuracy in impact assessment while avoiding time-consuming simulations of all possible user scenarios.
4Reliability
If developer options are constrained to maintain system availability, then system reliability is preserved, but development flexibility and option selection are reduced
Solution Approach 1:
The system provides feedback to developers about the expected impact of different software change options by calculating the intersection of compatibility and access analysis. This feedback shows which deployment options will minimize disruptions to frequently accessed functionalities while maintaining system availability. Developers can use this information to select optimal deployment strategies that balance reliability requirements with development flexibility, rather than being constrained by arbitrary rules.
Data Source
AI summary
Analysis of the expected impacts of deployment of a software change as well as the frequency of end user use of features of an application can be used to better guide development of software changes.