Amplifying Source Code Signals for Machine Learning Training
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Solution Overview
Problem
Machine learning models trained for source code tasks face inefficiencies in identifying and learning source code signals, requiring large amounts of data and time, and may not be effective without reliable signal identification.
Innovation Solution
A method that involves identifying and amplifying source code signals, generating amplified code that is functionally equivalent to the original, and providing it for machine learning models to improve training efficiency, using a signal amplifier system that includes a code analyzer and re-writer to enhance signal visibility without altering the model's architecture or objective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a statistical approach is used to train ML models to identify source code signals, then the model can learn source code tasks, but it requires relatively large amounts of training data and time
Solution Approach 1:
The patent applies preliminary action by pre-processing source code data to identify and amplify source code signals before training the ML model. The signal amplifier analyzes source code and generates amplified versions with enhanced signals, preparing the data in advance so the model can learn more efficiently during training without requiring as much raw data or time
2Reliability
If a statistical approach is used to train ML models to identify source code signals, then the model can learn source code tasks, but it requires relatively large amounts of training data
Solution Approach 1:
The patent applies parameter changes by transforming the source code data parameters through signal amplification. The signal amplifier modifies the source code to enhance the visibility and prominence of source code signals, changing the data characteristics so that less training data is needed to achieve the same learning effectiveness
3Reliability
If traditional training methods are used, then ML models can be trained for source code tasks, but the training efficiency is low
Solution Approach 1:
The patent introduces an intermediary component - the signal amplifier - that sits between the raw source code data and the ML model training process. This intermediary analyzes and amplifies source code signals in the data before presenting it to the model, improving training efficiency while maintaining prediction accuracy
Data Source
AI summary
Embodiments are disclosed for a method. The method includes identifying one or more source code signals in a source code. The method also include generating an amplified code based on the identified signals and the source code. The amplified code is functionally equivalent to the source code. Further, the amplified code includes one or more amplified signals. The method additionally includes providing the amplified code for a machine learning model that is trained to perform a source code relevant task.