Machine Learning Program Matrix for Software Impact Prediction

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Solution Overview

Problem

Existing software development systems face challenges in determining relationships between multiple programs and predicting the impact of modifications across a software project, leading to resource-intensive processing and reduced throughput.

Innovation Solution

A machine learning-based system that generates a program matrix to identify relationships between programs, uses a regression model to map these relationships to a spatial domain, and generates impact reports and test case information to predict modification impacts and optimize software testing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional code analysis methods are used to determine relationships between programs, then relationship identification is achieved, but processing power consumption increases significantly and system throughput decreases

Engineering Contradiction:

Improverelationship identification accuracyVSAvoidsystem throughput

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent introduces an intermediary representation layer (program matrix and spatial domain mapping) between the raw code and the relationship analysis process. This intermediary allows the system to work with condensed relationship data rather than analyzing entire codebases, thereby maintaining measurement precision while reducing the computational burden on the system.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent extracts only the essential relationship information from the code into a program matrix format, separating the critical relationship data from the full codebase. This extraction process enables the system to focus analysis on condensed relationship representations rather than processing all code, thus improving throughput while maintaining relationship identification accuracy.

Inventive Principle:

Principle #2Taking out (Extraction)

2Loss of information

If comprehensive code analysis is performed on all programs in a software project, then complete relationship mapping is achieved, but resource consumption increases and development time extends

Engineering Contradiction:

Improverelationship information completenessVSAvoiddevelopment time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent transforms the relationship representation from traditional code-based parameters to spatial domain parameters. By mapping programs to spatial coordinates where distance represents impact level, the system can efficiently query and analyze relationships without examining the underlying code, thus preserving relationship information completeness while significantly reducing development time.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent creates a simplified copy of the program relationships in the form of a program matrix and spatial domain representation. This copy contains the essential relationship information needed for impact analysis without requiring access to the full codebase, enabling fast relationship queries and impact assessments while maintaining information completeness.

Inventive Principle:

Principle #26Copying

3Measurement precision

If the system analyzes modifications to predict their impact on other programs, then impact prediction accuracy is improved, but processing resources are consumed and system performance decreases

Engineering Contradiction:

Improveimpact prediction accuracyVSAvoidprocessing resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent performs preliminary action by pre-computing and storing program relationships in a program matrix and spatial domain representation before modification analysis is needed. This pre-processing creates a ready-to-use relationship model that enables fast impact prediction without requiring intensive processing resources at the time of modification analysis, thus maintaining prediction accuracy while reducing real-time resource consumption.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11556460B2Test case generation for software development using machine learning

Publication Date: 2023.01.17 BANK OF AMERICA CORP

AI summary

A device is further configured to determine a location within a spatial domain for a first program. The device is further configured to determine a first distance threshold value that corresponds with a first distance away from the location of the first program within the spatial domain. The device is further configured to determine distances between the location of the first program and locations of other programs from the plurality of programs and to identify one or more programs from the plurality of programs that are less than the first distance threshold value. The device is further configured to identify the one or more programs from the plurality of programs that are less than the first distance threshold value.