ML-Based Source Code Performance Impact Analysis

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current methods for analyzing performance impacts of source code changes in cloud-based infrastructure are cumbersome, inaccurate, and inefficient, failing to effectively identify high-impact changes and flag responsible developers.

Innovation Solution

A machine learning model is employed to analyze developer activity, application metrics, and platform metrics, using a sliding window to track and generate reports on performance impacts, flagging detrimental changes and preventing their execution, while also modifying cloud infrastructure configurations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional methods are used to analyze performance impacts of source code changes, then the analysis process is simple to implement, but the accuracy and efficiency of identifying high-impact changes is poor

Engineering Contradiction:

Improveaccuracy of identifying high-impact changesVSAvoidcomplexity of analysis system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

A machine learning model is introduced as an intermediary between source code changes and performance impact analysis. The model takes code changes, developer activity, application metrics, and platform metrics as inputs and predicts performance impacts, thereby improving accuracy without requiring direct complex analysis of all system interactions.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces traditional manual or rule-based performance analysis mechanisms with a machine learning-based predictive system. This substitution enables automated, data-driven analysis that is both more accurate and scalable, resolving the contradiction between precision and complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If comprehensive metrics tracking is implemented to improve analysis accuracy, then identification of high-impact changes improves, but the system becomes more cumbersome and inefficient

Engineering Contradiction:

Improveaccuracy of performance impact analysisVSAvoidefficiency of analysis process

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The machine learning model is pre-trained on historical data containing developer activity, application metrics, and platform metrics. This preliminary training enables the model to quickly analyze new code changes without requiring real-time comprehensive data collection and processing, thereby maintaining high accuracy while improving analysis efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system automatically collects, processes, and analyzes metrics data without requiring manual intervention. The machine learning model self-services by continuously learning from incoming data and automatically generating performance impact predictions, eliminating the need for cumbersome manual analysis processes.

Inventive Principle:

Principle #25Self-service

3Ease of operation

If manual analysis processes are used, then the system is easier to operate, but it fails to effectively flag responsible developers and make automated infrastructure adjustments

Engineering Contradiction:

Improveease of system operationVSAvoidautomation of developer flagging and infrastructure adjustment

Core Design Contradiction:

Ease of operationVSExtent of automation

Solution Approach 1:

The system implements automated feedback loops where the machine learning model continuously predicts performance impacts, flags problematic code changes and responsible developers, and triggers automated infrastructure adjustments. This feedback mechanism enables high-level automation while maintaining ease of operation through centralized model management and automated decision-making.

Inventive Principle:

Principle #23Feedback

4Reliability

If traditional performance analysis methods are used, then resource consumption is lower, but the ability to identify and prevent detrimental changes is insufficient

Engineering Contradiction:

Improveability to prevent detrimental changesVSAvoidcomputational resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system applies partial analysis by focusing computational resources on predicting only the performance impact of code changes that are likely to be detrimental. The machine learning model prioritizes analyzing changes with higher risk profiles, thereby achieving high reliability in preventing bad changes while consuming computational resources only when necessary.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11531536B2Analyzing performance impacts of source code changes

Publication Date: 2022.12.20 RED HAT INC

AI summary

A method for analyzing performance impacts of changes to source code includes logging a change to source code and an identifier (ID) of an author of the change into a database. The method further includes providing the change to the source code to a machine learning model trained to analyze performance impacts of source-code changes. The method further includes generating, by a processing device, in view of the machine learning model, a performance impact of the change to the source code. The method further includes determining that the performance impact is above a performance-impact threshold. The method further includes, in view of the determining, preventing the change to the source code from being executed by the processing device.