Dynamic Memory Debugging with Heuristics Reliability Scores
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Solution Overview
Problem
Conventional solutions for debugging dynamic memory allocations lack detail and reliability, failing to provide users with valuable insights to optimize program performance.
Innovation Solution
A method and system that utilize heuristics reliability scores to categorize dynamically allocated memory blocks in detail, generating scores based on criteria and improving analysis by comparing current and previous scores for accurate categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional heuristics are used to categorize dynamic memory allocations, then the categorization process is simple and fast, but the results lack detail and reliability
Solution Approach 1:
The patent segments the memory debugging process into multiple phases: initial categorization using heuristics, followed by detailed analysis phases that progressively refine categorization. Memory blocks are segmented into different analysis groups based on their characteristics, allowing the system to apply different levels of analysis depth to different blocks, thereby improving overall reliability without uniformly increasing complexity across all memory analysis.
Solution Approach 2:
The system dynamically adjusts the level of analysis depth and detail based on initial heuristic results. Memory blocks that show suspicious patterns or fail initial checks are automatically subjected to more rigorous analysis, while clearly benign blocks receive minimal processing. This dynamic approach ensures high reliability for problematic areas while maintaining efficiency for routine cases.
2Measurement precision
If detailed analysis of dynamically allocated memory is performed, then categorization accuracy improves, but the debugging process becomes more complex and time-consuming
Solution Approach 1:
The patent implements partial detailed analysis rather than exhaustive analysis of all memory blocks. It applies full detailed analysis only to memory blocks that fail initial heuristic checks or show suspicious patterns, while accepting good-enough categorization for clearly benign blocks. This selective approach achieves high measurement precision where needed while minimizing time loss on routine memory allocations.
Solution Approach 2:
The system uses feedback from initial heuristic categorization to guide the depth of subsequent detailed analysis. Results from preliminary analysis feed into decisions about which memory blocks require further examination, creating a feedback loop that optimizes the balance between analysis precision and debugging time. This iterative feedback mechanism ensures that detailed analysis is focused on the most problematic areas.
3Productivity
If heuristics are used to categorize memory at a high level, then the process is efficient, but the results do not provide valuable insights for optimization
Solution Approach 1:
The patent segments memory analysis into multiple detail levels, with each segment providing progressively more detailed information. The system maintains and correlates results across all segments, allowing users to access both high-level efficiency summaries and detailed information about specific memory blocks. This segmented approach preserves valuable details while maintaining efficient high-level overview capabilities.
Solution Approach 2:
The system adds dimensional depth to memory categorization by organizing results in multiple hierarchical levels. Instead of a single flat categorization, it creates multi-dimensional views that include both aggregate statistics and individual block details, allowing users to navigate between different levels of information granularity. This dimensional approach prevents information loss while maintaining debugging efficiency.
Data Source
AI summary
A computing system determines whether memory data pertaining to a block of dynamically allocated memory within an inferior process memory space satisfies one or more criteria in heuristics data. The computing system identifies a category to assign to the block of dynamically allocated memory based on the determination of whether the memory data satisfies the criteria and generates a reliability score for the block of dynamically allocated memory indicating a level of reliability of the identified category. The computing system categorizes the block of dynamically allocated memory based on a comparison of the reliability score and a previous reliability score of the block of the dynamically allocated memory.