Microsoft technology licensing, llc. (20240338188). CODE ADAPTATION THROUGH DEEP LEARNING simplified abstract

CODE ADAPTATION THROUGH DEEP LEARNING

Organization Name

microsoft technology licensing, llc.

Inventor(s)

MILTIADIS Allamanis of CAMBRIDGE (GB)

SHENGYU Fu of REDMOND WA (US)

XIAOYU Liu of SAMMAMISH WA (US)

NEELAKANTAN Sundaresan of BELLEVUE WA (US)

ALEXEY Svyatkovskiy of BELLEVUE WA (US)

CODE ADAPTATION THROUGH DEEP LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240338188 titled 'CODE ADAPTATION THROUGH DEEP LEARNING

• • Simplified Explanation:**

This patent application describes a mechanism that automatically integrates variable names from a pasted source code snippet into a pre-existing partial source code program by replacing them with anonymized values and predicting the most likely variable names to replace them using a deep learning model trained on variable usage patterns.

• • Key Features and Innovation:**
• Automatic integration of variable names from pasted source code snippets into pre-existing code.
• Replacement of variable names with anonymized values.
• Prediction of the most likely variable names using a deep learning model trained on variable usage patterns.

• • Potential Applications:**

This technology can be applied in software development environments to streamline the integration of code snippets and improve code readability and maintainability.

• • Problems Solved:**
• Tedious manual integration of variable names from pasted code snippets.
• Potential errors in variable naming consistency.
• Time-consuming process of aligning variable names in different code segments.

• • Benefits:**
• Increased efficiency in code integration.
• Improved code readability and maintainability.
• Reduction of errors in variable naming consistency.

• • Commercial Applications:**

Potential commercial applications include software development tools, integrated development environments (IDEs), and code collaboration platforms.

• • Prior Art:**

Researchers and developers can explore prior art related to code integration mechanisms, variable naming conventions, and deep learning models in software development.

• • Frequently Updated Research:**

Stay updated on advancements in deep learning models for code analysis, variable naming conventions in software development, and tools for code integration and collaboration.

• • Questions about the Technology:**

1. How does this technology improve the efficiency of code integration in software development? 2. What are the potential challenges in implementing this mechanism in different programming languages?

Original Abstract Submitted

a code adaptation mechanism automatically integrates the variable names of a pasted source code snippet into variable names defined in a pre-existing partial source code program. the variable names from the pasted source code snippet are replaced with anonymized values. a deep learning model predicts the most likely variable name from the pre-existing partial source code program to replace each anonymized value. the deep learning model is trained on numerous variable usage patterns from various source code programs to learn to predict the most likely mapping of an undefined variable name from the pasted source code snippet to a variable name in the pre-existing partial source code program thereby generating a syntactically and semantically correct program.