Software Profiling via Input-Size Correlation
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Solution Overview
Problem
Existing profilers are overinclusive in identifying resource-intensive routines that may already be efficient and underinclusive in detecting inefficient routines that do not consume a large portion of resources in test cases, making it difficult to pinpoint and improve actual inefficiencies in software applications.
Innovation Solution
An enhanced profiling system that stores data inputs and resource consumption information during software application execution, correlates this data to input sizes, and uses regression analysis or empirical function discovery to identify routines with inefficient resource consumption, even if they do not significantly contribute to overall resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing profilers identify routines with high resource consumption, then developers can focus on optimizing those routines, but the profilers are overinclusive and may lead developers to optimize routines that are already efficient
Solution Approach 1:
The patent applies local quality by making the profiling analysis adaptive to each routine's characteristics. Instead of using a uniform threshold for all routines, the system dynamically determines inefficiency based on each routine's specific resource consumption pattern, input size, and baseline behavior. This allows the profiler to precisely identify only those routines that are actually inefficient, avoiding false positives and reducing wasted developer optimization effort.
2Measurement precision
If existing profilers use test cases to generate profiles, then they can identify resource consumption patterns, but inefficient routines may not constitute a large percentage of resource consumption if the test case input size does not trigger inefficient behavior
Solution Approach 1:
The patent applies dynamics by making the profiling process adaptive and iterative. The system dynamically adjusts its analysis based on observed resource consumption patterns across multiple executions with varying input sizes. It continuously refines its understanding of each routine's behavior and updates its inefficiency determination accordingly, allowing it to detect inefficient routines even when they don't consume large resources in initial test cases.
Solution Approach 2:
The patent applies parameter changes by systematically varying test case parameters, particularly input size, to trigger inefficient behavior. The system executes routines with different input sizes and monitors resource consumption changes. By observing how resource usage scales with input size, the profiler can identify routines that become inefficient under certain conditions, even if they appear efficient in initial test cases.
3Measurement precision
If profilers store information about resource consumption during long application executions, then they can accurately profile the execution, but the tremendous amount of data makes it difficult to find inefficient routines
Solution Approach 1:
The patent applies the extraction principle by isolating and focusing analysis on specific routines that exhibit inefficient resource consumption patterns. Instead of requiring developers to search through all collected profiling data, the system extracts and highlights only the problematic routines. It uses heuristics and patterns to identify inefficient routines and presents them in a condensed, actionable format, separating the signal from the noise in the profiling data.
Solution Approach 2:
The patent applies inversion by reversing the traditional profiling approach. Instead of presenting all collected data and expecting developers to find inefficiencies, the system inverts the process by automatically analyzing the data and presenting only the identified inefficient routines. It turns the profiler from a data collection tool into an automated analysis tool that delivers actionable insights directly.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for enhanced profiling. An exemplary method includes initiating an execution of a software application which includes a plurality of routines, storing information related to data inputs to the plurality of routines during the execution of the software application, storing resource consumption information for the plurality of routines during the execution of the software application, correlating the resource consumption information for the plurality of routines to a size of the data inputs, and analyzing the correlated resource consumption information to determine a subset of the plurality of routines that exhibit at least a threshold amount of resource consumption with increasing size of the data inputs.