Code Impact Prediction via Dependency Graph Superimposition
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Solution Overview
Problem
Software developers face challenges in identifying and mitigating the impact of code modifications on client workflows due to lack of complete knowledge and visibility of upstream and downstream dependencies, leading to introduction of new errors and defects.
Innovation Solution
The system generates static and dynamic dependency graphs to map dependencies within a code base, allowing for comprehensive impact analysis and prediction of affected methods through comparison and superimposition of these graphs, providing a holistic view of method-to-method dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If developers manually analyze code dependencies to understand impact, then they can identify affected methods, but the process is time-consuming and error-prone due to lack of complete visibility
Solution Approach 1:
The system enables self-service by automatically generating dependency graphs and performing impact analysis without requiring manual developer intervention. The tool autonomously traces method calls, builds dependency models, and identifies impacted methods, allowing the system to serve itself rather than requiring continuous human analysis.
Solution Approach 2:
The patent replaces manual mechanical code analysis with automated computational systems. Instead of developers manually tracing dependencies, the system uses algorithmic approaches to generate static and dynamic dependency graphs, automatically identifying impacts through computational methods rather than human inspection.
2Productivity
If developers use available tools for code analysis, then they can speed up the process, but these tools do not provide complete knowledge and visibility of client workflows
Solution Approach 1:
The system segments the dependency analysis into multiple comprehensive layers: static dependency graphs for structural relationships, internal-domain dynamic dependency graphs for execution traces, and client-domain dynamic dependency graphs for actual workflow behavior. This segmentation allows each layer to capture different aspects of dependencies, providing complete knowledge when combined.
Solution Approach 2:
The patent transitions from single-dimension static analysis to multi-dimensional analysis by incorporating both static and dynamic perspectives. The system adds temporal and execution context dimensions through dynamic dependency graphs, enabling complete visibility of workflow dependencies that static tools alone cannot provide.
3Reliability
If developers perform comprehensive impact analysis, then they can identify all affected methods, but the complexity of analysis increases
Solution Approach 1:
The system introduces intermediary dependency graphs as mediators between the code base and the impact analysis. These graphs (static, internal-domain dynamic, and client-domain dynamic) serve as structured representations that simplify the complex relationships in the code, making comprehensive analysis manageable through visual and computational abstraction.
Solution Approach 2:
The system implements feedback mechanisms where the generated dependency graphs are used to automatically identify impacts, which then feed back into recommendations for developers. This iterative feedback loop allows the system to learn from analysis results and improve its predictions, reducing the manual complexity burden on developers.
Data Source
AI summary
Methods, systems, and computer-readable media are disclosed herein that utilizes historical changes made to files and methods in computer programming code to predict related files and methods that may be affected by current and/or future changes made to other files and methods. In aspects, when computer code for a particular method is going to be edited, other methods are identified that were changed in previous editing sessions that also included changes to the particular method. Using scoring techniques for the other methods, a recommendation is provided that details the relative strength of whether the other methods are predicted to be affected by any changes made to the computer code for the particular method that is edited.