ML Installation Script Generation for Runtime Dependency Compatibility
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Solution Overview
Problem
Creating a runtime environment for source code is time-consuming and error-prone due to the need to manually identify and resolve dependency compatibility issues and ordering, which can lead to compilation errors.
Innovation Solution
An installation script generation engine using a trained machine learning model, such as a neural network, automatically generates an installation script based on source code and target runtime environment characteristics to create a compatible and error-free execution environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual dependency management is used, then developers have full control over the environment setup, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs self-service by automatically analyzing source code, extracting dependencies, determining compilation order, and generating installation scripts without human intervention. The compiler automatically infers the runtime environment configuration and generates the necessary installation scripts, eliminating the need for manual dependency management while ensuring accuracy through programmatic analysis.
Solution Approach 2:
The patent replaces the mechanical manual process of environment setup with an automated computational system. Instead of developers manually identifying and resolving dependencies, a compiler-based system performs automated static analysis, dependency extraction, and script generation, substituting human effort with machine intelligence.
2Productivity
If automated script generation is implemented, then setup time is reduced, but complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary installation script generation engine that acts as a mediator between the source code and the runtime environment. This engine receives source code, performs automated analysis using compiler infrastructure, extracts dependencies, determines compilation order, and generates installation scripts. The intermediary handles the complexity internally while presenting a simple interface to users.
Solution Approach 2:
The installation script generation engine performs multiple functions within a single unified system: it analyzes source code, extracts dependencies, determines compilation order, identifies version conflicts, and generates installation scripts. By consolidating these functions into one multi-functional engine, the system manages complexity through integration rather than proliferation of separate tools.
Data Source
AI summary
A method for auto-generating an installation script that creates a runtime environment for a source code comprises receiving a source code identifier identifying the source code; determining characteristics of a target execution runtime environment in which the source code is to be executed; extracting dependency identifiers from the source code; and providing the dependency identifiers and the characteristics of the target execution runtime environment to a trained machine learning (ML) model. The method further comprises receiving from the trained ML model library version identifiers that each identify a library supporting a corresponding one of the dependency identifiers and that is compatible with the characteristics of the target execution runtime environment. An installation script usable to create the target execution runtime environment is composed from the ML model outputs.