21 results about "Compilation error" patented technology

The invention discloses a structured text code generation and review method, which comprises the following steps of: receiving a control task demand input by a user, supporting natural language, structured form or graphical process input, performing deep semantic analysis by utilizing a demand analysis module, identifying control conditions, control actions and constraint factors, and generating and reviewing a structured text code. The task is converted into a plurality of control units through the SCoT chain; the SCoT chain reader generates a semantic vector according to a task demand, the AST reader generates AST representation through a grammar rule structure and a hierarchical relationship, and the decoder predicts a next grammar rule and generates an ST code in combination with the SCoT chain and AST features; and verifying grammar and logic of the ST code through an interactive automatic review and repair mechanism, if compiling errors occur, analyzing errors by the teacher model and providing repair suggestions, and performing code repair by the student model according to guidance until the code conforms to an expected function and passes compiling.

The invention discloses a method and a system for solving compilation errors in intelligent contract version evolution. The method comprises the following steps: collecting and cataloguing major changes of all records based on an official Solidity website, screening to obtain major changes causing a compiling problem, and constructing a knowledge base of version migration; combining codes with compiling problems and compiling error information of the codes; slicing the smart contract code by using a static analysis tool, and retrieving a version migration knowledge base by using a retriever based on compiling error information to obtain related version migration knowledge; and guiding the large language model to generate a high-quality patch in combination with a result obtained by the analysis. According to the method, a static analysis tool, structured retrieval of expert knowledge and the generation capacity of a large language model are organically combined, the problem of compiling errors caused by Solidity language evolution is systematically solved, and developers can be effectively assisted in coping with version migration and code repair tasks.

The invention discloses an automatic debugging system and method for deep learning compiler numerical errors, and the system comprises a model analysis module, a semantic matching module, a tracking module and a verification module, the analysis module receives and analyzes a defect model with compilation errors, carries out the extraction of the input of the defect model, sub-functions, and operators of each sub-function, and carries out the verification of the compilation errors. Constructing a symbolic calculation graph and an index table before and after optimization of the defect model; the semantic matching module performs hash processing on each node in the symbolic calculation graph, matches equivalent nodes before and after optimization by comparing the approximation degree of hash values between the nodes, and generates a matching relationship between the nodes and the equivalent nodes; the tracking module compares the data streams of the models before and after optimization according to the matching relationship, generates an error cumulant diagram by tracking the generation and propagation process of errors, and locates the modes causing the errors and compiler optimization transformation causing mode rewriting; and the inspection module inspects the correctness of the positioning result and finally outputs root information after inspection. According to the method, each computing node of the neural network model is quantified into a hash value capable of measuring the distance, compared with an existing positioning technology, the method can better adapt to the scene of a complex deep learning model, and compared with an incremental debugging technology, the error positioning speed and accuracy are remarkably improved.

The invention relates to an automatic program defect repairing method combining source code semantics and abnormal feedback, and belongs to the technical field of computer programs. The method comprises the following steps: firstly, selecting a latest released version of a to-be-repaired program, and randomly adding disturbance to the version program to generate a plurality of defect variant programs; secondly, extracting elements such as a defect row and a defect neighbor row of the defect variant program to construct a defect context; compiling errors or function errors after execution of the defect variant program are obtained to serve as abnormal feedback; finally, the defect context and the abnormal feedback are vectorized and spliced into training data, a loss function is designed to minimize the semantic difference between the repair code and the latest issued program, and a defect repair model is trained and generated. Aiming at the problem that a model is difficult to learn normal program characteristics due to the fact that open source training data and to-be-repaired program semantic differences are large in an existing method, the number of correctly repaired defects is increased by constructing proprietary training data and designing a target function to guide the model to learn semantic and contextual information.

A prompt to a large language model for a source code translation of an input source code snippet written in a source programming language into a different target programming language is augmented with compiled code translations. The compiled code translations are identified from a ranked list of the top-k code candidates in the target programming language having a close similarity to an embedding of the input source code snippet. A reward model generates a reward score for each of the top-k code candidates which is used to identify the top-n code candidates having less compilation errors. The top-n code candidates are included in the prompt to the large language model to guide the model on the translation task.

The invention provides a code execution method and device based on a low-code platform, and the method comprises the steps: obtaining a current code file under the condition that a storage modification instruction for code modification based on the low-code platform is received; determining an incremental code snippet of the current code file; performing code compiling on the incremental code snippets to obtain a compiling result; and under the condition that the compiling result is determined to be failed, outputting and displaying the compiling result. According to the method provided by the invention, the current code file is automatically obtained under the condition that the storage instruction of code modification by the user is received, the incremental code snippets of the current code file are compiled, and the compilation result containing the compilation error information is returned and displayed under the condition that the compilation result is compilation failure; according to the method and the device, the compiling triggering is automatically enhanced, the compiling failure problem is quickly positioned, and the code development and debugging efficiency is further improved.

The application discloses a multi-position defect repairing method based on repairing sequence scheduling and multi-agent cooperation. First, the defect analysis agent is used to identify the code error root cause and generate a defect analysis report. Then, the report, error code, local context, failed test cases and test error information are combined to build a repair context. Then, the multi-agent architecture is used to iteratively generate patches: the coordinator agent analyzes the dependency relationship between code blocks and schedules the repair sequence, the proposer agent generates a local candidate patch, and the coordinator evaluates and selects the optimal patch. Repeat the process until all repairs are completed and a complete patch is assembled. Finally, according to the compilation error and test failure report, the patch is executed in two stages of syntax and semantics refinement. The application overcomes the defects of the existing large model repairing method, such as lack of sequence scheduling and difficulty in handling code block dependencies, and realizes efficient repair of complex multi-position defects.

This invention provides an AI-based method for automatic repair and recovery of compilation errors in continuous integration (CI), belonging to the field of software engineering automation technology. The method includes: automatically capturing and structurally parsing error logs when the CI process fails; inputting the structured error data into a pre-trained AI repair model to diagnose the root cause and generate repair code; automatically committing the repair code to a temporary branch of the code repository after pre-verification; and automatically triggering a new round of CI compilation for the temporary branch in response to the commit event to verify the repair results. If verification fails, the process is restarted based on new error information, forming a closed loop. This invention achieves full automation from the occurrence of a compilation error to repair verification, reducing repair time from hours to minutes, significantly improving the continuity of the CI process, development efficiency, and freeing up developer productivity.

The invention relates to the technical field of artificial intelligence, and can be applied to software code reconstruction scenes in financial and medical scenes. The invention provides a software reconstruction method, system and device and a storage medium, and the method comprises the steps: carrying out the context analysis of a target code entity through a planning agent, and generating a structured reconstruction plan based on an analysis result; generating a reconstructed code by the generation agent according to the reconstruction plan; the compilation agent performs compilation verification on the reconstructed code, and when compilation fails, compilation error information is fed back to the generation agent for iterative correction; and the test agent performs function test on the reconstructed code after successful compiling, and feeds back test failure information to the generation agent for iterative correction when the test fails. The method can be applied to financial / medical software code reconstruction scenes, and the success rate of software reconstruction is improved through multi-agent cooperation and double feedback closed loops of compiling and testing.

The application belongs to the field of information technology and relates to an automatic compilation error repairing method and system combining neural network prediction and a search algorithm. The method comprises the following steps: preprocessing a program with a compilation error to generate a tokenized and abstracted program; processing the tokenized and abstracted program by using an encoder to generate a vector sequence, wherein each vector represents a token in the program; predicting scores of an insertion operator, a deletion operator and a replacement operator by using a prediction head according to the generated vector sequence; constructing a weighted modification graph according to the predicted scores; and obtaining a repairing result by calling a search algorithm according to the weighted modification graph. The application can limit and guide the search algorithm and can generate a higher-quality repaired program.

This invention belongs to the field of software porting technology, and specifically relates to a software intelligent porting assistance method and system based on the SW processor. It includes: constructing a SW processor-specific feature knowledge base; performing static analysis on the code of the software to be ported and storing it in a vector database to construct a knowledge base vector index, thereby comparing code differences; performing code preprocessing based on the analysis and vector index results, as well as the SW architecture rules and x86 to SW architecture conversion rules in the specific feature knowledge base; identifying the software language and system environment of the software to be ported and compiling it, generating compilation error logs if compilation errors occur; retrieving errors from the compilation logs, and using the SW large model to identify and call repair tools for code modification via function call functionality; this invention solves the problems of poor compatibility, low efficiency, and poor stability of existing tools adapted to this processor.

This invention discloses a fuzzy testing method and system for aero-engine compilers based on a large language model. In the mutation hint generation stage, a seed program library is constructed using embedded programs with various structures and compiler test suite programs. Mutation operators are selected through diversified guidance strategies, and mutation hints are constructed based on hint templates. In the test generation stage, variant programs with complex data and control flows are generated using the large language model to enhance compiler test coverage. Test outputs are inserted into the program to monitor global and local variables, enabling the program to effectively detect the most harmful silent compilation errors. Furthermore, a front-end error feedback strategy is designed to improve the effectiveness of test programs generated by the large language model. In the differential testing stage, random differential testing and testing under different optimization levels are combined to detect back-end defects in the compiler. This invention can improve compiler test coverage and detect silent compilation errors.

The invention discloses a source code file compiling method, device and equipment and a computer readable storage medium. The method comprises the steps that for each source code file in a to-be-compiled project, if a compiling error exists in the process of compiling the source code file, the compiling error is recorded and processed; and after the processing is completed, continuing to compile from the position of the compilation error until the compilation of the source code file is completed, and starting the compilation of the next source code file. According to the method and the device, when the compilation error is encountered in the compilation process, the compilation error is directly processed, so that compilation can be continued from the position of the compilation error, repeated compilation is avoided, and the compilation efficiency is improved; and compiling errors are recorded, so that developers can know all problems existing in the to-be-compiled project, and the development efficiency is improved.

The invention provides a C language-oriented compiling error automatic repair method and system. The method comprises the following steps of: performing preliminary repair on an error program by using a fine-tuned CodeT5 model; calling a GCC compiler to process the preliminarily repaired program, and outputting a to-be-repaired error code line, an error position and error diagnosis information of the to-be-repaired error code line; according to the error position and the error diagnosis information, performing mask processing on the error code line to be repaired by using a preset mask template; inputting the error code line to be repaired after mask processing into a pre-training code model UniXcoder, and outputting a plurality of candidate repair patches and probability scores thereof; a token-based normalized editing distance of the candidate repair patches is calculated, a comprehensive evaluation score is calculated in combination with the probability scores of the candidate repair patches, and k candidate repair patches with the highest score are screened out; and repairing the error code line to be repaired by using the screened k candidate repair patches.

The invention discloses a compiling error intelligent optimizing and prompting system and method based on a large language model. The system comprises an error capture layer, an intelligent analysis layer, an interaction presentation layer, an iteration enhancement verification module and a knowledge base updating module. The error capture layer monitors the compiling process in real time and collects errors and context information; the intelligent analysis layer performs multi-dimensional analysis on errors through a large language model to generate classification, root causes and solutions; the interactive presentation layer outputs a result in a structured mode and supports developer feedback and intelligent inquiry; the iteration enhancement verification module starts a deep analysis optimization solution strategy when the scheme is invalid; and the knowledge base updating module dynamically stores the verified scheme to improve the subsequent processing efficiency. The problems that a traditional compiler is unfriendly in prompt and an AI tool lacks context awareness are solved, and the automation level of compiling error diagnosis and repair and developer experience are remarkably improved.

The application discloses a code clone detection method and system based on abstract syntax tree optimization and multi-representation, which comprises the following steps: compiling code text to obtain a corresponding abstract syntax tree; optimizing the abstract syntax tree, including removing the nodes generated by the compiler and the recovery nodes of the compilation errors, removing the declaration nodes and constant nodes, refining the expression nodes, and respectively converting the selection structure and the loop structure into corresponding unified sub-tree structures; traversing the optimized abstract syntax tree to obtain two sequences; inputting the two sequences into a Transformer network to output the feature fingerprints corresponding to the code text; obtaining a plurality of corresponding feature fingerprints according to a plurality of code texts; and if the cosine similarity of any two feature fingerprints is greater than a first set threshold, the two text codes corresponding to the two feature fingerprints are similar. The application optimizes the abstract syntax tree to improve the prediction result while reducing the prediction cost.

The present invention discloses a workflow file parsing system, which relates to the technical field of workflow development. The system comprises: a parsing unit: used for obtaining an operating instruction and obtaining a parsing result based on the operating instruction; the operating instruction is obtained in the following manner: based on a predefined parsing tag, a compiler obtains parsing parameters of a file to be parsed, replaces the predefined parameters based on the parsing parameters and the command parameters, and obtains operating parameters and operating environment data; based on the environment tag of the operating environment data, an environment type is obtained, and based on the operating parameters and the environment type, the operating instruction is obtained. The system can solve the problems of a steep learning curve of an existing workflow parsing system, complex parsing and compiling workflow files, resulting in high learning costs, and prone to compilation errors and parsing errors.

The invention relates to the technical field of computer programming, and discloses a code repairing method and device based on an abstract syntax tree and a large model, and the method comprises the steps: carrying out grammar inspection on a to-be-repaired code, repairing a grammar problem, and generating a preliminary repairing code; compiling and verifying the preliminary repair code, and matching compiling error information based on an error rule knowledge base to obtain a repair rule when a compiling error occurs; repairing the abstract syntax tree of the preliminary repair code by utilizing a repair rule until the preliminary repair code passes compilation verification to obtain a target repair code, and performing operation verification; and when the running error is detected, calling a preset large language model based on the running error information to diagnose and repair until the running verification is passed. According to the method, the code error repairing efficiency is improved, abstract syntax tree analysis and large language model repairing are combined, the processing capacity and repairing precision of deep semantic errors and unknown running errors are enhanced, and the code quality, reliability and development efficiency are improved.

The embodiment of the invention discloses a data testing method and device, equipment and a storage medium. The method comprises the following steps: receiving resource object information created by a client for a to-be-compiled code in an environment container; obtaining a target programming language type indicated by the resource object information; according to the target programming language type, constructing a compiling sub-container corresponding to the target programming language type in an environment container; and pulling the to-be-compiled code from the client according to the resource object information in the compiling sub-container, and compiling the to-be-compiled code in a compiling environment of the compiling sub-container to obtain a compiling result. Therefore, the compiling sub-container corresponding to the target programming language type of the to-be-compiled code is quickly created in the environment container, and the to-be-compiled code is pulled into the compiling environment of the compiling sub-container to be compiled, so that compiling errors of the to-be-compiled code caused by external factors can be reduced, stable compiling of the to-be-compiled code can be realized, and the compiling efficiency of the to-be-compiled code is improved. And the compiling test efficiency of the to-be-compiled code is improved.

The invention discloses a high-safety PLC compiling verification method for a nuclear energy automation control system, and relates to the technical field of software engineering and nuclear energy control, and the method comprises the following steps: firstly, designing a PLC language subset ST-light suitable for the nuclear energy control system; secondly, constructing a semantic model of ST-light based on big step semantics; then, a trusted compilation algorithm from ST-light to Light is designed; comprising the steps of performing type checking to ensure variable type matching; a Light code is generated; a CompCert credible compiling chain is adopted to convert the Light into a target code; and finally, proving the semantic consistency of the designed trusted compilation algorithm by using a theorem proving technology. According to the method provided by the invention, system faults caused by compiling errors are avoided, so that the operation reliability of nuclear energy facilities is improved.

The invention discloses a large language model automation unit test generation method based on segmented thinking chain driving. Firstly, a to-be-tested project is analyzed by using a tree-siter to construct an abstract syntax tree, and detailed context information of class and method levels is extracted and stored in a database. Then, a sectional thinking chain driving framework is adopted, a complex unit test generation task is decomposed into three subtasks which are executed step by step, a large language model is guided to carry out systematic test scene recognition from three dimensions of a typical scene, a boundary scene and an abnormal scene, and a detailed test case is generated based on the recognized test scene; and generating a unit test code which can be directly operated in combination with the test case information. On this basis, a multi-dimensional error repair mechanism is established, template-based local repair and large language model-based intelligent repair are performed for compiling errors and runtime errors, and meanwhile, an error recognition module is introduced to detect and terminate invalid repair cycles.