8 results about "Programming style" patented technology

The invention discloses an integrated circuit design Verilog code generation method and device based on a large language model, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps that semantic analysis is conducted on an input language of an integrated circuit user side through the large language model, and a target constraint set is determined based on an obtained performance index set and through a regular expression; a candidate architecture scheme is generated by utilizing thinking chain technology reasoning, the candidate architecture scheme is predicted by utilizing an XGBoost regression model, and a target architecture scheme is determined based on an index prediction result and the candidate architecture scheme by utilizing a non-dominated sorting genetic algorithm; and determining an initial Verilog code based on the target architecture scheme and a preset code template library, and performing code style conversion on the initial Verilog code based on a code style feature corresponding to the integrated circuit user side to obtain a target Verilog code. And the efficiency and availability of integrated circuit design are improved.

The invention provides an AI-based code review opinion automatic classification and priority ranking method. The method comprises the steps of collecting code review historical data; data preprocessing: preprocessing the collected data, and constructing a training data set; an AI model is selected and trained; inputting a new review opinion; automatic classification: the model automatically classifies the review suggestions, and classification categories include but are not limited to code errors, code style problems, performance optimization suggestions and potential safety hazard prompts; performing priority ranking: performing priority ranking on the review opinions based on a preset priority rule and the classification result; and generating result output. According to the code review method and device, automatic classification and priority ranking of the code review opinions are achieved through the AI model, a traditional manual processing mode is replaced, the problems that manual classification is low in efficiency and priority ranking is high in subjectivity are effectively solved, and the overall efficiency and result accuracy of code review are improved.

The invention discloses a task alignment Text-to-SQL (Structured Query Language) method and system based on a code style thinking chain. The method comprises the following steps: constructing a three-layer structured knowledge base comprising mode-level knowledge, instance-level knowledge and example-level knowledge; generating an initial query based on the target database and the user query; according to the list information in the initial query, constructing an instruction sequence represented by a similar SQL code, and forming a code style thinking chain; based on user query and initial query, acquiring multi-dimensional knowledge from the three-layer structured knowledge base and constructing an enhanced prompt context; and integrating the code style thinking chain and the enhanced prompt context based on the prompt template, and generating an executable final query through a multi-stage task alignment mechanism and a final prompt. According to the method, the problems of incoherence and inaccuracy of logical reasoning, insufficient knowledge generalization ability and multi-type model illusion existing in cross-domain, multi-table connection and complex query in the prior art are solved.

The invention discloses a method for dynamically constructing a UDF based on a large model. The method comprises the following steps: S1, constructing a multi-dimensional requirement cue word of a user-defined function according to characteristics and use requirements of a target platform; s2, the multi-dimensional requirement cue words are input into a pre-trained large language model for code generation, the large language model is subjected to multi-round iteration based on the multi-dimensional requirement cue words, and an optimal code sequence for achieving the user-defined function is output; s3, code style standardization is carried out on the optimal code sequence, and a compiler or an interpreter and a static analysis tool are respectively called to carry out grammar check and static analysis on the optimal code sequence, and constructing an optimization cue word according to an error log output by the compiler or the interpreter and a static analysis result output by the static analysis tool, and inputting the optimization cue word into the large language model to carry out iterative optimization on the optimal code sequence.

This application discloses a training method and electronic device for a code generation model, relating to the field of computer technology. The method includes: inputting sample prompts into a code generation model to obtain sample code; determining a first reward parameter to characterize execution stability based on the code execution information of the sample code, and parsing the code execution information, sample code, and sample prompts using a reference language model to obtain a second reward parameter to characterize the degree of syntax errors and a third reward parameter to characterize code style; adjusting the model parameters of the code generation model based on the first, second, and third reward parameters; and determining the target code generation model based on the adjusted code generation model when the adjusted model reaches convergence. This solves the technical problem of low training efficiency in code generation models and achieves the technical effect of improving the training efficiency of code generation models.

The invention provides a C language unit test generation method based on a reference test case, and belongs to the field of software engineering. Comprising the following steps: constructing a C project symbol index knowledge base; performing test case migration association retrieval; and generating a test case based on the reference test mode. According to the method, the pairwise resource'application / release 'mode is extracted and forcibly applied from the existing test, so that the problem that memory leakage may be introduced into the generated C language test case is solved; by injecting the test mode extracted from the real code, the generated test case is highly consistent with the tested item in data structure initialization, function call agreement and code style, and the readability and maintainability of the test code are improved; the expert knowledge and the generation capability of the large language model are deeply fused, and the test environment establishment and resource cleaning work are fully automated, so that the cost and time for compiling a high-quality test for a complex C language project are remarkably reduced.

At least one unit of a software application is identified. The at least one unit includes source code. The source code of the at least one unit is analyzed to determine a style of the source code. Metadata is extracted from the at least one unit based on the source code analysis. One or more features of the extracted metadata are classified. A template file is modified based on the extracted metadata and the classified features to create a modified template file.

This disclosure provides a knowledge graph question answering method based on code style context learning. For an input code style learning question and a knowledge base, a large language model is first used to obtain a function call sequence through code style context learning. A dense retrieval system is then used to link entities extracted from the function calls and to perform relation matching on the relation names extracted from the function calls. Finally, a program interpreter executes the generated function call sequence to obtain the logical form, which is then further converted into an executable query to obtain the answer to the question. This method effectively solves the formatting error problem in logical form generation through metafunction design and retrieval enhancement strategies. It not only avoids secondary training of a large model but also significantly improves generalization performance in scenarios with few or no samples.