Memory Leak Detection via Heap Dump Correlation Analysis
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Solution Overview
Problem
Current memory leak analysis tools are limited by their inability to monitor the heap in real-time, leading to false positives and the complexity of identifying multiple contributing software issues, making it difficult to detect and analyze memory leaks effectively in large-scale applications.
Innovation Solution
The method involves collecting instance count data through class histograms and calculating a correlation coefficient for each class in the memory heap to identify classes contributing to memory growth, allowing for dynamic analysis and identification of memory leaks during application execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple heap dumps are collected and compared to identify memory leaks, then memory leak detection can be performed, but false positives occur and transient growth is misidentified as leaks
Solution Approach 1:
The patent performs preliminary actions by collecting multiple heap dumps at different time points before analysis. By gathering heap dump data at timestamps T1, T2, T3, and T4 in advance, the system establishes a temporal sequence that enables differentiation between transient and permanent memory growth patterns, thereby reducing false positives in memory leak detection.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring memory heap changes across multiple time points and using this information to refine leak detection accuracy. The system analyzes the correlation between memory growth patterns and object lifecycle, feeding this information back to distinguish true leaks from transient allocations, thus improving both precision and reliability.
2Measurement precision
If manual analysis of heap dumps is performed to locate failing code paths, then detailed memory leak identification is possible, but the process becomes extremely time-consuming and complex
Solution Approach 1:
The patent enables self-service by automatically analyzing heap dump data and generating memory leak reports without requiring manual intervention. The system autonomously performs object reference analysis, identifies leaking objects, traces code paths, and produces diagnostic results, thereby eliminating time-consuming manual analysis while maintaining high precision through automated algorithms.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated computational methods. Instead of human analysts manually examining heap dumps and tracing references, the system uses automated algorithms to perform object graph analysis, identify memory leaks, and locate failing code paths, dramatically reducing analysis time while maintaining or improving detection precision.
3Quantity of substance
If heap dump analysis is performed on large memory sizes in hundreds of megabytes, then comprehensive memory monitoring is achieved, but the complexity of manual analysis increases significantly
Solution Approach 1:
The patent applies segmentation by dividing the large memory heap into manageable units of individual objects and classes. Instead of analyzing the entire hundreds of megabytes of memory as a monolithic structure, the system segments the heap into discrete object instances, tracks their references individually, and aggregates results, thereby reducing analysis complexity while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The patent introduces a temporal dimension to memory analysis by collecting heap dumps at multiple time points (T1, T2, T3, T4). This transforms the analysis from a static single-point examination to a dynamic multi-temporal study, enabling the system to handle large memory sizes more effectively by observing patterns over time rather than attempting to analyze all memory simultaneously.
4Device complexity
If only one or two heap dumps are analyzed, then the analysis process is simple, but the ability to detect transient versus permanent memory growth is limited
Solution Approach 1:
The patent implements periodic action by collecting heap dumps at regular intervals (multiple time points T1, T2, T3, T4) during application execution. This periodic sampling enables the system to detect patterns in memory growth, distinguish between transient allocations that appear and disappear and permanent leaks that persist, thereby improving detection precision without requiring overly complex analysis of continuous data streams.
Data Source
AI summary
Embodiments of the invention provide systems and methods for detecting an analyzing memory leaks. According to one embodiment, a method of detecting a memory leak can comprise collecting instance count data for a number of instances of a plurality of classes in a memory heap. A correlation coefficient can be calculated for each of the plurality of classes in the memory heap. The correlation coefficient can represent a relevance of the class to a growth of the memory heap. Classes contributing to the growth of the memory heap can then be identified based on the correlation coefficient.