Automatic Memory Leak Detection via Dynamic Linker Hooks
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Solution Overview
Problem
Memory leaks in complex software systems are difficult to detect and can lead to system unavailability, as existing tools either fail to identify runtime leaks or add significant overhead, making them unsuitable for quality assurance testing or customer use.
Innovation Solution
A system and method for automatic memory leak detection that collects memory usage data, performs trend analysis using linear regression, and generates notifications and recommendations for user identities, with low overhead and the ability to run continuously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If runtime tools are used to detect memory leaks for specific software components, then memory leak detection capability is improved, but system overhead becomes too high to be useful during QA testing and at customer sites
Solution Approach 1:
The patent introduces an intermediary mechanism that hooks into the dynamic linker/loader to intercept library loading events. This intermediary captures memory allocation information at the source without requiring instrumentation of the entire application, thereby maintaining detection capability while minimizing overhead on the running system.
Solution Approach 2:
The system performs preliminary actions by setting up detection hooks during the library loading phase, before the application fully executes. By capturing memory allocation patterns early in the runtime lifecycle, the system establishes a baseline that enables continuous monitoring without adding significant overhead during normal operation.
2Measurement precision
If manual identification of key processes is performed to isolate memory leaks, then detection accuracy is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system enables self-service by automatically attributing memory leaks to specific third-party libraries and software components without requiring manual intervention. The dynamic linker hooks automatically capture and report memory allocation information, allowing the system to identify and isolate leaks autonomously based on predefined severity thresholds.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor memory allocation patterns and provide real-time information about potential leaks. This feedback loop automatically correlates memory usage with specific library loading events, enabling rapid isolation of problematic components without manual analysis.
3Manufacturing precision
If static analysis tools are used to check application code at build time, then compilation-time error detection is improved, but runtime memory leaks during error conditions are not detected
Solution Approach 1:
The system ensures continuity of useful action by maintaining memory leak detection throughout the entire runtime lifecycle, including error conditions. Unlike static analysis that stops at build time, this approach continuously monitors memory allocations even when the application encounters errors or abnormal execution paths, ensuring comprehensive coverage.
4Adaptability or versatility
If monitoring tools examine all user space processes to detect memory leaks, then comprehensive detection coverage is improved, but system performance and operational simplicity deteriorate
Solution Approach 1:
The patent extracts the essential detection function by focusing specifically on third-party library memory allocations rather than monitoring all user space processes. By using dynamic linker hooks to intercept only library loading events, the system achieves comprehensive coverage for the most common leak sources while maintaining system performance.
Data Source
AI summary
Automatic memory leak detection is presented herein. An example method comprises, receiving, into a data slot of a defined data structure, a flow of memory usage data sent by a resource monitoring process operational on the system, in response to determining a time value exceeding a defined threshold time value, closing the data slot, based on the memory usage data included in the data slot, executing a trend analysis process using the memory usage data as an input to the trend analysis process, and in response to the trend analysis process determining a trend associated with the memory usage data, transmitting notification data representing the trend and recommendation data representing a suggestion to reverse the trend to a user equipment.


