Program Fragment Generation via Keyword Context Ranking
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Solution Overview
Problem
Current tools for writing computer programs are often complex and require advanced programming skills, limiting their accessibility to ordinary users who may not possess these skills.
Innovation Solution
A program development framework (PDF) that allows users to create programs by successively specifying program fragments, using keyword information and context analysis to generate and rank candidate fragments, providing interactive output based on confidence levels and statistical information from a corpus of previously created programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional programming tools are used, then program creation capability is maintained, but accessibility to ordinary users deteriorates due to complexity requirements
Solution Approach 1:
The patent segments the program creation process into discrete, manageable steps where users specify individual program fragments through simple keyword inputs rather than writing complete complex programs at once. This breaks down the intimidating task of program creation into smaller, more accessible units that ordinary users can handle.
Solution Approach 2:
The system introduces an intermediary component that translates simple keyword inputs into complete program fragments. This mediator handles the complexity of program synthesis, ranking candidates, and selecting appropriate code snippets, shielding users from the underlying complexity while maintaining program creation capability.
2Ease of operation
If automated program generation is implemented, then ease of use improves, but program creation precision deteriorates due to lack of user control
Solution Approach 1:
The system dynamically adjusts the level of automation and user control based on the situation. It generates multiple candidate program fragments and presents them for user selection, allowing users to intervene when precision is critical while maintaining automated generation for routine tasks. The system adapts between fully automated and user-guided modes.
Solution Approach 2:
The system incorporates feedback mechanisms where user selections and corrections are used to refine and re-rank program fragment candidates. This feedback loop improves precision by learning from user preferences and corrections, ensuring that automated generation becomes increasingly accurate while maintaining ease of use.
3Adaptability or versatility
If multiple candidate program fragments are generated, then program creation flexibility improves, but processing time increases due to ranking and selection requirements
Solution Approach 1:
The system performs preliminary actions by pre-ranking and organizing program fragment candidates before presenting them to users. This advance preparation reduces the time users need to spend evaluating options, as candidates are already sorted by relevance and quality based on the keyword inputs and context analysis.
Data Source
AI summary
A program development framework (PDF) is described herein which allows a user to produce a program in piecemeal fashion by successively specifying program fragments. The PDF creates a new program fragment by receiving keyword information from the user that describes a new program fragment, and then identifies context information that pertains to a programmatic context in which the new program fragment appears within the overall program being created. The PDF then generates a set of candidate program fragments that satisfy the keyword information and the context information, and ranks those candidate program fragments based on ranking information. At least part of the ranking information may be based on statistical information that is produced by analyzing a corpus of previous programs produced by one or more users. The PDF then provides the ranked program fragments to the user using various user-friendly presentation strategies.