Real-time Code Recommendations Using Machine Learning

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Solution Overview

Problem

The complexity of software development leads to disparities in source code quality due to varying developer proficiency and unique coding styles, resulting in inconsistency and inefficiency, as traditional tools rely on manual reviews and fail to accelerate developer ramp-up effectively.

Innovation Solution

A code recommendation system utilizing machine learning and reinforcement learning to provide intelligent, personalized, and contextualized real-time code recommendations within an integrated development environment (IDE), tailoring suggestions based on developer experience, domain context, and organizational needs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional manual code review tools are used, then developer skill development is supported, but the process is time-consuming and does not significantly accelerate developer ramp-up

Engineering Contradiction:

Improvecode quality consistencyVSAvoidtime for code review and developer ramp-up

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual code review (mechanical human process) with an automated code recommendation system using machine learning models. The system analyzes authored code, developer data, context data, and domain data to generate real-time code recommendations, eliminating the time-consuming manual review process while maintaining code quality consistency through automated analysis and suggestions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables developers to self-improve through automated code recommendations that provide real-time guidance on code quality, best practices, and improvements. Developers receive personalized recommendations based on their skill level and specific code context, allowing them to independently enhance their coding skills without requiring extensive manual review time from seniors or reviewers.

Inventive Principle:

Principle #25Self-service

2Productivity

If real-time code recommendations are provided to all developers, then developer efficiency is improved, but system complexity and computational resource consumption increase

Engineering Contradiction:

Improvedeveloper efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system provides customized code recommendations tailored to each developer's skill level, experience, and specific code context. Rather than applying uniform recommendations to all developers, the system adapts the complexity, type, and detail of recommendations based on individual developer profiles and the specific code being authored, optimizing efficiency gains while managing system complexity through targeted rather than universal processing.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically adjusts recommendation parameters such as the number of suggestions, detail level, and types of improvements based on developer experience level and code context. For junior developers, more guidance and educational recommendations are provided, while senior developers receive more targeted, optimization-focused suggestions, thereby improving efficiency without uniformly increasing system complexity for all users.

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If personalized code recommendations based on developer experience level are provided, then code quality consistency is improved, but processing requirements and computational resources increase

Engineering Contradiction:

Improvecode quality consistencyVSAvoidcomputational resource consumption

Core Design Contradiction:

Manufacturing precisionVSUse of energy by moving object

Solution Approach 1:

The system pre-processes and stores developer profile data, experience levels, and coding patterns in advance to create personalized developer models. This preliminary action allows the system to quickly retrieve and apply relevant parameters during code review without performing extensive real-time analysis, thereby maintaining code quality consistency through personalized recommendations while reducing computational resource consumption during actual code processing.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11269597B2Real-time code recommendations using machine learning and reinforcement learning

Publication Date: 2022.03.08 SAP SE

AI summary

Methods, systems, and computer-readable storage media for receiving, from an integrated development environment (IDE), authored code, the authored code having been input by a developer to the IDE, receiving developer data representative of the developer, and context data and domain data associated with the authored code, determining, at least partially based on processing the authored code, the developer data, the context data, and the domain data through one or more machine learning (ML) models, that one or more code recommendations are to be displayed to the developer in the IDS, defining a sub-set of code recommendations at least partially based on the authored code and the context data, the sub-set of code recommendations comprising at least one code snippet, and displaying graphical representations of code recommendations in the sub-set of code recommendations to the developer within the IDE.