9 results about "Compilation error" patented technology

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.

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 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 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.