ML Bug Classification for Software Conflict Detection

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Solution Overview

Problem

In manufacturing and development processes, identifying issues early on is crucial to prevent costly rework and redesign, but existing methods are inefficient and costly, especially when software conflicts arise later in the process, leading to exponential increases in validation and testing expenses.

Innovation Solution

A machine learning engine is used to classify and reclassify bug reports, identifying trends and additional attributes to improve the development process by analyzing data on software or hardware conflicts, enabling efficient identification of areas needing additional testing or validation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If extensive instrumentation, simulation, and validation processes are used to eliminate conflicts, then problem identification accuracy is improved, but development costs increase prohibitively

Engineering Contradiction:

Improveproblem identification accuracyVSAvoiddevelopment cost

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent replaces extensive manual instrumentation, simulation, and validation processes with an automated machine learning classification system. The ML model automatically analyzes bug reports and identifies conflicts without requiring exhaustive manual testing and validation, thereby maintaining high problem identification accuracy while significantly reducing development costs and resource consumption.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service through automated bug classification and analysis. The machine learning model independently processes bug reports, identifies patterns, and classifies conflicts without requiring extensive human intervention or manual validation processes, reducing both cost and time while maintaining reliability.

Inventive Principle:

Principle #25Self-service

2Loss of energy

If software conflicts are identified at the coding stage, then resolution cost is reduced, but detection capability is limited without automated analysis

Engineering Contradiction:

Improveresolution costVSAvoiddetection capability

Core Design Contradiction:

Loss of energyVSDifficulty of detecting and measuring

Solution Approach 1:

The patent replaces manual code review and testing processes with an automated machine learning classification system that continuously analyzes bug reports. This automated system enhances detection capability by processing large volumes of data quickly and accurately, enabling early conflict identification at the coding stage while maintaining low resolution costs through automated prioritization and classification.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The machine learning classification system acts as an intermediary between raw bug reports and human developers. It automatically processes and classifies bug data, translating unstructured reports into organized, prioritized information that enables early detection and efficient resolution of software conflicts without requiring direct human analysis of every bug report.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If manual bug report analysis is performed, then data accuracy is maintained, but processing efficiency decreases

Engineering Contradiction:

Improvedata accuracyVSAvoidprocessing efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual bug report analysis with an automated machine learning classification system. The ML model maintains data accuracy by learning from training data and applying consistent classification criteria, while simultaneously dramatically improving processing efficiency by automatically analyzing large volumes of bug reports without human intervention.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The machine learning system enables continuous automated analysis of bug reports without interruption or fatigue. The classification process operates continuously, maintaining consistent accuracy standards while processing unlimited volumes of data, thereby eliminating the productivity limitations of manual analysis while preserving data accuracy through trained model consistency.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS10740216B1Automatic bug classification using machine learning

Publication Date: 2020.08.11 AMAZON TECH INC

AI summary

A machine learning engine can be used to identify inconsistencies and errors in a plurality of bug reports and to glean new information from the bug reports. Bug data associated with a large number of bug reports from different bug categories may be processed and used by a machine learning model of the machine learning engine. The machine learning engine can extract bug attributes from the bug data of a first bug. The machine learning engine can then compare the attributes of the first bug to a machine learning model created using a plurality of second bug reports. Based on then similarity between the first bug report and the second bug reports, the machine learning engine can apply, or correct, various attributes of the first bug report. The machine learning model may be updated over time by the machine learning engine as data correlations evolve.