Autocreated Code Suggestion Filtering via Trust Scores
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Solution Overview
Problem
Software developers face overwhelming and untrustworthy autocreated code editing suggestions, which can hinder productivity and consistency in code development, as existing technologies lack effective filtering mechanisms to distinguish endorsed suggestions from non-endorsed ones.
Innovation Solution
A system that filters autocreated software development suggestions based on provenance metadata, assigning a trust score to each suggestion based on its endorsement by the development team, and presenting only the endorsed suggestions to the developer, thereby reducing configuration burdens and enhancing code consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autocreated suggestions are presented to developers, then code development productivity is improved, but developer frustration increases due to overwhelming quantity and lack of trustworthiness
Solution Approach 1:
The patent extracts and removes untrustworthy autocreated suggestions from the presentation interface, separating them from the trusted suggestions. This is achieved by filtering suggestions based on provenance metadata and trust scores, so only suggestions that meet trust criteria are presented to developers, eliminating the harmful effect of overwhelming frustration while preserving the productivity benefits of having suggestions available.
Solution Approach 2:
The patent introduces an intermediary filtering mechanism between the autocreated suggestions and the developer. This intermediary system evaluates suggestions based on provenance metadata and trust scores before presenting them to the developer. The intermediary acts as a gatekeeper that maintains the productivity advantage of having AI-generated suggestions while preventing the harmful effect of developer frustration by ensuring only trustworthy suggestions are presented.
2Adaptability or versatility
If all autocreated suggestions are presented, then suggestion coverage is maximized, but time for evaluation increases
Solution Approach 1:
The patent extracts and removes low-trust suggestions from the evaluation pool, so developers only evaluate suggestions that have been pre-filtered based on provenance metadata and trust scores. This extraction process maintains comprehensive suggestion coverage for trusted recommendations while significantly reducing the time developers spend evaluating suggestions, as they no longer need to assess every autocreated suggestion.
Solution Approach 2:
The patent performs preliminary evaluation and filtering of autocreated suggestions before they reach the developer. The system pre-evaluates suggestions based on provenance metadata and assigns trust scores, so that when developers receive suggestions, they are already pre-sorted and prioritized. This preliminary action maintains broad suggestion coverage while eliminating the time-consuming task of evaluating all suggestions from scratch.
3Reliability
If trust scoring based on provenance metadata is implemented, then suggestion reliability is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service by having the system automatically generate, store, and utilize provenance metadata for autocreated suggestions without requiring manual configuration or complex user input. The system self-manages the trust scoring process by automatically evaluating existing codebase data and generating trust scores, thereby improving suggestion reliability while keeping the added complexity manageable through automation rather than manual intervention.
Solution Approach 2:
The patent changes the parameter space by introducing provenance metadata fields and trust scores as new dimensions for evaluating suggestions. Instead of dealing with raw autocreated suggestions, the system transforms them into structured data with provenance information and calculated trust scores. This parameter transformation improves suggestion reliability by providing measurable trust metrics while managing system complexity through standardized data structures and automated processing.
4Stability of the object's composition
If filtering mechanisms are added to distinguish endorsed suggestions, then code consistency is improved, but configuration burden increases
Solution Approach 1:
The patent implements self-service filtering where the system automatically determines which suggestions to present based on pre-existing provenance metadata and trust scores calculated from codebase data. This eliminates the need for manual configuration of filtering rules, as the system self-determines what constitutes a trustworthy suggestion based on automated evaluation of provenance information, thereby improving code consistency while avoiding the configuration burden that would otherwise be required.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously learns from codebase data and developer interactions to refine its trust score calculations. The provenance metadata is updated based on actual usage patterns and outcomes, creating a feedback loop that improves code consistency over time. This feedback-driven approach eliminates the need for manual configuration, as the system automatically adapts its filtering criteria based on observed patterns in the codebase and developer behavior.
Data Source
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AI summary
Some embodiments determine automatically which synthesized or otherwise autocreated suggestions for source code editing are presented to developers. Some filter out autocreated coding suggestions that have not been sufficiently endorsed by a developer's team, based on a suggestion trust score. The trust score may reflect the suggestion's adoption in a particular repository or codebase, or affiliation of the suggestion with a library release, or an actual or implied review of the suggestion by team members. Some suggestion filters enhance existing development team code review practices, by offering endorsed suggestions in autocompletion or analysis interfaces of tools and by withholding non-endorsed suggestions from display. Context illustrating the autocreated suggestion's provenance may be displayed to help developers decide whether to adopt the suggestion themselves while editing code. Some tools that are enhanced with suggestion filtering functionality avoid developer configuration burdens while increasing consistent adoption of endorsed suggestions inside a codebase.