A method and system for intelligent code completion suggestion optimization based on multi-level filtering pipeline

Abstract

The application relates to the technical field of code editor and integrated development environment, and core technical points are as follows: in view of the problems of long suggestion, poor correlation, lack of personalization, high repetition rate and the like existing in the existing code completion technology, the application provides an intelligent code completion suggestion optimization method and system based on a multilevel filtering pipeline; through a seven-level continuous filtering pipeline architecture, in combination with context perception, user behavior learning and a multi-factor sorting mechanism, invalid suggestion filtering, relevant suggestion strengthening and personalized sorting optimization are realized, and the accuracy and use efficiency of code completion are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of code editors and integrated development environments, specifically to a method and system for filtering and sorting intelligent code completion suggestions. Background Technology

[0002] In today's software development field, code editors and integrated development environments (IDEs) have become core tools for developers, with intelligent code completion being a key feature for improving coding efficiency. As programming languages ​​become more diverse and projects become more complex, developers have increasingly stringent requirements for the accuracy, relevance, and personalization of code completion.

[0003] However, existing code completion technologies have many pain points: the suggestion list is lengthy and messy, containing a large number of invalid suggestions that are irrelevant to the current coding scenario, increasing the developer's filtering costs; it lacks deep context awareness capabilities, and cannot accurately match the current code scope, data type, and semantic logic, resulting in poor suggestion relevance; it does not personalize the sorting according to the user's coding habits, making it difficult to prioritize frequently used suggestions; and duplicate and low-quality suggestions appear frequently, seriously affecting coding fluency and efficiency, and failing to meet the needs of modern software development for efficient coding tools. Summary of the Invention

[0004] To address the problems of lengthy suggestions, poor relevance, lack of personalization, and high repetition rate in existing code completion technologies, this invention provides an intelligent code completion suggestion optimization method and system based on a multi-level filtering pipeline. Through a seven-level continuous filtering pipeline architecture, combined with context awareness, user behavior learning, and multi-factor ranking mechanisms, it achieves invalid suggestion filtering, relevant suggestion reinforcement, and personalized ranking optimization, significantly improving the accuracy and efficiency of code completion.

[0005] This invention employs a seven-level filtering pipeline technology to continuously process the initial code completion suggestion set in a fixed order, ultimately outputting accurate, relevant, and personalized completion suggestions. Specific implementation steps are as follows: 1: Initialization, loading user configuration (language type, filtering rule threshold, personalization weight), programming language syntax rule library and user historical behavior data (acceptance / rejection suggestion records, usage frequency statistics); 2: Initial suggestion set generation: Based on the current encoding position, language characteristics, and basic syntax analysis, a set of original code completion suggestions is generated; 3: Blacklist filtering layer processing, using regular expression pattern matching to filter suggestions containing invalid keywords, illegal characters or user-defined blacklists; 4: Syntax matching filtering layer processing, implementing prefix matching (highest weight), substring matching (position weighted) and intelligent case matching, retaining suggestions that conform to the current syntax format; 5: Context-aware filtering layer processing, integrating scope analysis, type inference and semantic matching, to filter suggestions that are compatible with the current code context (variable type, function parameters, nested scope); 6: Frequency control filtering layer processing, based on the sliding window mechanism, filters suggestions that appear repeatedly in a short period of time to avoid high-frequency redundancy; 7: Intelligent deduplication layer processing, using similarity threshold detection (similarity ≥ 0.9 is judged as duplicate), to eliminate duplicate or highly similar content suggestions; 8: The relevance calculation layer processes the suggested weight by comprehensively considering contextual relevance (40% weight), usage frequency (20% weight), and recent usage records (20% weight); 9: Multi-factor ranking layer processing, based on a weighted scoring model (relevance + frequency of use + recent use + specificity 10% + alphabetical order 10%) to perform final ranking and generate an optimized suggestion list; 10: Output the suggestion list to the code editor / IDE, and record the user's acceptance / rejection feedback on the suggestions, update the user's historical behavior data, and use it for subsequent filtering rule optimization.

[0006] Compared with traditional code completion techniques, this method and system have four main advantages: High completion accuracy: Seven-level filtering pipeline filters out invalid suggestions at a rate of over 90%, improving code completion accuracy by over 40%; Improved coding efficiency: The optimized suggestion list is concise and highly relevant, reducing the time users spend filtering suggestions by 60% and significantly reducing the frequency of coding interruptions; Multi-scenario adaptation: Supports multiple programming languages ​​such as JavaScript, Python, and Java, and has language-specific syntax recognition and pattern matching capabilities to adapt to different development scenarios; Personalized adaptation: Integrating a user behavior learning mechanism, it adjusts weights in real time based on acceptance / rejection feedback to achieve personalized suggestion ranking and adapt to the coding habits of different developers. Detailed Implementation

[0007] The specific implementation method is as follows: 1. During the initialization phase, when the system starts up, it loads the user's preset configuration (including the target programming language type, threshold parameters for each filtering layer, and personalized weight allocation), synchronously loads the syntax rule library and keyword library of the corresponding programming language, and reads the user's historical behavior data (including the suggestion acceptance count, rejection count, and usage frequency statistics for the past 30 days) from local storage. 2: Initial suggestion set generation: When a developer triggers a code completion event during coding, the system generates an initial suggestion set containing 50-200 suggestions based on the current cursor position, the entered code snippet, and project dependency library information; 3: Blacklist filtering layer processing, using regular expression matching rules to filter out invalid keywords such as null and undefined, special character combinations, or suggestions from user-defined blacklists (such as specific deprecated APIs), retaining valid suggestions after filtering; 4: The syntax matching filtering layer prioritizes prefix matching (with a weight of 0.8 for successful matches), followed by substring matching (with a weight of 0.3-0.7 based on the matching position), and finally performs intelligent case matching (automatically adapting to case-sensitive rules based on language characteristics), preserving suggestions for grammatically compliant formats. 5: Context-aware filtering layer processing: Scope analysis is performed by traversing the abstract syntax tree (AST) of the current code, type inference is performed based on variable declarations and function definitions, and semantic pattern matching algorithm is used to filter suggestions that are compatible with the current coding scenario, and invalid suggestions that do not match the type or are outside the scope are eliminated; 6: Frequency control filtering layer processing uses a sliding window of size 10 to record the suggestions output in the last 10 times, and filters the current suggestions that are repeated with the items in the window to prevent repeated pushes in a short period of time. 7: Intelligent deduplication layer processing: By calculating the common prefix length and overall length similarity of the suggested strings, and combining the character matching rate, a similarity value is obtained, and duplicate suggestions with similarity ≥ 0.9 are eliminated to ensure the uniqueness of the suggestion list; 8: Relevance calculation layer processing, calculate the weight score of each suggestion according to the preset weight formula (relevance score = context matching degree × 0.4 + historical usage frequency × 0.2 + recent usage record × 0.2 + specificity × 0.1 + alphabetical order fit × 0.1); 9: Multi-factor ranking layer processing, sort the suggestion list in descending order of weight score, and sort suggestions with the same score in alphabetical order to generate the final set of optimization suggestions (the number of suggestions is controlled between 5 and 15). 10: Output the set of optimization suggestions to the completion list of the code editor / IDE, monitor user actions in real time, and accumulate the usage frequency and recent usage records if the user accepts a suggestion, and record it in the rejection list if the user rejects it. Adjust the weight parameters and matching rules of each filter layer regularly (daily) based on user behavior data to continuously optimize the completion effect. Attached Figure Description

[0008] Figure 1 A diagram illustrating the optimized system architecture for intelligent code completion suggestions based on multi-level filtering pipelines.

[0009] Figure 2 This is a flowchart of a seven-stage filtration pipeline process.

Claims

1. A method for intelligent code completion suggestion optimization based on multi-level filtering pipelines, characterized in that... The process includes the following sequential processing steps: initial configuration and data loading, initial suggestion set generation, blacklist filtering, syntax matching filtering, context-aware filtering, frequency control filtering, intelligent deduplication filtering, relevance calculation, multi-factor sorting, suggestion output and behavioral data update. The filtering stage contains seven independent and sequential processing levels.

2. The method according to claim 1, characterized in that: The context-aware filtering layer integrates scope analysis, type inference, and semantic matching algorithms, and can filter relevant suggestions based on the abstract syntax tree (AST) of the current code, ensuring type compatibility and scope.

3. The method according to claim 1, characterized in that: The relevance calculation adopts a multi-factor weighted model, with the weights allocated as follows: context relevance 40%, historical usage frequency 20%, recent usage records 20%, specificity 10%, and alphabetical order fit 10%.

4. The method according to claim 1, characterized in that: The intelligent deduplication layer uses a similarity calculation method based on common prefix length and character matching rate, sets a similarity threshold of 0.9, and removes duplicate suggestions with similarity ≥ the threshold.

5. An intelligent code completion suggestion optimization system based on a multi-level filtering pipeline, characterized in that... include: The system includes a configuration loading module, a suggestion generation module, a seven-level filtering pipeline module (blacklist filtering module, syntax matching filtering module, context-aware filtering module, frequency control filtering module, intelligent deduplication module, relevance calculation module, multi-factor sorting module), a user behavior recording module, and a suggestion output module. These modules are linked sequentially according to the data flow.

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