Executable Code Heatmaps for Targeted Update Recommendations
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Solution Overview
Problem
Current methods for deploying software fixes and patches lack sufficient detail for informed decision-making, leading to inefficient and ineffective problem resolution, as they do not guarantee that the fix addresses the specific issue at hand.
Innovation Solution
A computer-implemented method that divides executable programs into units, creates logical layers for instantiation, monitors execution to increment counters, generates a heatmap based on these counters, and recommends updates based on the heatmap analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual evaluation of all changes is performed before deployment, then deployment risks are reduced, but time consumption and complexity increase significantly
Solution Approach 1:
The patent segments the codebase into functional units and creates a hierarchical structure with entry lists at multiple levels (file level, function level, unit level). This segmentation allows automated tracking of code execution paths without requiring manual evaluation of entire codebases, thus reducing both time and complexity while maintaining reliability through precise problem-localized assessment.
Solution Approach 2:
The patent implements automated feedback mechanisms by monitoring code execution, tracking which units are actually executed during runtime, and using this data to generate heatmaps that indicate problem locations. This feedback loop replaces manual evaluation with automated data collection and analysis, significantly reducing evaluation time while improving accuracy through empirical execution data.
2Ease of manufacture
If fixes are applied on a regular scheduled frequency, then deployment complexity is reduced, but the effectiveness of problem resolution decreases
Solution Approach 1:
The patent performs preliminary actions by automatically instrumenting the codebase beforehand, establishing entry lists and execution tracking infrastructure before problems occur. This preliminary setup enables rapid, targeted fix deployment when problems are detected, combining the simplicity of automated scheduling with the precision of data-driven problem identification through heatmaps.
Solution Approach 2:
The patent changes the parameter of fix deployment from time-based (scheduled frequency) to condition-based (heatmap-driven problem detection). By monitoring execution patterns and identifying actual problem locations through automated tracking, the system deploys fixes only when and where needed, maintaining deployment simplicity while significantly improving problem resolution effectiveness.
3Loss of information
If detailed technical analysis of each fix is performed, then informed decision-making is enabled, but the complexity and time required for deployment increases
Solution Approach 1:
The patent introduces an intermediary automated analysis system that collects execution data, generates heatmaps, and provides problem location information. This intermediary layer bridges the gap between raw code execution and decision-making, providing detailed technical information about actual problem locations without requiring manual analysis complexity, thus enabling informed decisions while reducing evaluation burden.
Data Source
AI summary
An approach for recommending an application of a software code update. The approach receives components of an executable program. The approach scans the components to divide the executable program into units. The approach creates a logical layer of entry lists, based on the units, for instantiations of the executable program. The approach loads the instantiations of the executable program based on the logical layers. The approach monitors the execution of the instantiations to increment counters based on executions of the units. The approach creates a heatmap of the executions based on the counters. The approach generates a recommendation to update the executable based on the heatmap.