Code Synthesis via Operation Library Search
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Solution Overview
Problem
Developers face challenges in transferring and generating computer-readable code across different programming languages due to language-specific operations and redundancies, making it difficult to identify efficient sequences of operations that produce desired outputs.
Innovation Solution
A computing system that receives input and output examples to search a library of operations, using weighted enumerative search strategies and machine learning models to identify combinations of operations that generate the desired output, providing user interfaces for code synthesis and education tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a developer manually identifies and composes operations from a large library to transform input to desired output, then code can be generated, but the process is time-consuming and difficult even for experienced programmers
Solution Approach 1:
The patent replaces the mechanical process of manual operation identification and composition with an automated machine learning system. The system uses trained models to automatically identify relevant operations and compose them into correct sequences, eliminating the time-consuming manual search and composition process that even experienced developers face when working with large operation libraries.
Solution Approach 2:
The system enables self-service code generation by automatically performing tasks that previously required developer expertise. Given input data and desired output, the system autonomously identifies the correct operation sequence without human intervention, allowing developers to obtain code transformations without manually searching through operation libraries or understanding complex composition rules.
2Adaptability or versatility
If multiple programming languages with different built-in functions are used, then developers can leverage language-specific optimizations, but transferring solutions between languages becomes difficult
Solution Approach 1:
The patent creates a universal code generation system that works across multiple programming languages through a common operation library abstraction. By defining operations in a language-agnostic manner and using machine learning models that understand cross-language patterns, the system can generate equivalent code in different languages, allowing solutions to be transferred between languages while maintaining language-specific optimizations.
3Adaptability or versatility
If a comprehensive library of operations is provided to handle all possible transformations, then any input-output transformation can be achieved, but identifying the correct sequence of operations becomes increasingly difficult
Solution Approach 1:
The patent segments the large operation library into organized categories and uses machine learning models to navigate this structured space. Rather than presenting the entire library to developers, the system segments operations by function and type, then uses trained models to efficiently search and compose relevant operations from appropriate segments, making the complexity manageable while preserving comprehensive transformation capability.
Solution Approach 2:
The machine learning system acts as an intermediary between the comprehensive operation library and the developer. Instead of requiring developers to directly navigate the large library, the ML models serve as intermediaries that understand the full library, automatically identify relevant operations, and compose them into correct sequences, shielding developers from the underlying complexity while maintaining access to all transformation capabilities.
Data Source
AI summary
The present disclosure provides systems and methods for synthesizing computer-readable code based on the receipt of input and output examples. A computing system in accordance with the disclosure can be configured to receive a given input and output, access and library of operations, and perform a search of a library of operations (e.g., transpose, slice, norm, etc.) that can be applied to the input. By applying the operations to the input and tracking the results, the computing system may identify an expression comprising one or a combination of operations that when applied to the input generates the output. In this manner, implementations of the disclosure may be used to identify one or more solutions that a user having access to the library of operations may use to generate the output from the input.