588 results about "Unit testing" patented technology

A customized prompt generation service automates prompts to a large language model to perform a specified software engineering task. The service stores the custom data of a client that includes code diff hunks, source code segments, code reviews, repaired code, and unit tests from a code base or repository of the client. Prompt templates are associated with each software engineering task that include the requisite information needed for the large language model to perform the target task. A prompt to a large language model includes examples of the software engineering task from the custom data of the client of the service.

An artificial intelligence system can be used to respond to natural language inputs. The AI System may, for example, receive a first user input for a LLM, generate a first prompt based on the first user input, transmit the first prompt to an LLM, receive an output from the LLM, and evaluate the output from the LLM with reference to one or more validation tests. Responsive to determining that the output from the LLM is not validated, generate a second prompt for the LLM, where the second prompt indicates at least an aspect of the output that caused the output to not be evaluated (e.g., a portion of the output that may need to be updated or corrected), transmit the second prompt to the LLM, and receive an updated output from the LLM. The AI system can include an application for testing functions that utilize interactions with language models.

The invention provides a unit test case generation system based on a large language model, and the system comprises a function dependency analysis engine module which is used for preprocessing a target project, extracting a function dependency relationship, and generating an abstract syntax tree and a source code file; the large model module is used for automatically generating a test case through the code context information and the optimized cue word; and the verification and optimization module is used for verifying whether the test cases can be successfully executed or not by compiling and running the test cases, marking part of the test cases needing to be further processed, automatically analyzing error reasons, optimizing cue words and realizing automatic repair of the part of the test cases. According to the unit test case generation system based on the large language model, the function dependency analysis and the large language model are combined, the high-quality test case can be automatically generated, dependence on manpower or existing test cases is not needed, manual intervention is reduced, and the test efficiency and comprehensiveness are improved.

One or more unit-test cases are generated from a monolingual code corpus and the generated unit-test cases are filtered to generate a corpus of unit-test cases which have acceptability scores exceeding one or more predefined thresholds. One or more of the code samples of the monolingual code corpus are translated from a source language to a target language using a pretrained Large Language Model and the generated unit-test cases are translated from the source language to the target language. The LLM-translated code samples are validated using the translated unit-test cases and a parallel-data training corpus comprising the LLM-translated code samples that pass the validation is created. The pretrained large language model (LLM) is fine-tuned using the parallel-data training corpus, a given code segment is translated using the fine-tuned large language model (LLM), the translated given code segment is tested and the tested given code segment is deployed.

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

A method for automating a software development lifecycle by using artificial intelligence is disclosed. The method includes receiving a request to generate software components, the request including requirements in a natural language format; determining, by using a first model, tasks to generate the requested software components based on the request, each of the tasks including a sequence of subtasks, an acceptance criterion, and a predicted effort level; generating, by using a second model, sets of software code based on the tasks, the sets of software code corresponding to the requested software components; generating, by using a third model, unit tests for each of the sets of software code; initiating the unit tests for each of the sets of software code; and releasing, by using a fourth model, the sets of software code based on a result of the corresponding unit tests.

The invention particularly relates to a unit test case rapid generation method based on an AI large model. According to the unit test case rapid generation method based on the AI large model, structure information is extracted through a static AST, an execution path is tracked through dynamic instrumentation, and code analysis is achieved; an analysis result is converted into cue words through AI, a multi-scene use case is generated through reasoning, and the multi-scene use case is matched with a language framework; counting row / branch / path coverage, identifying gaps and optimizing loop logic; hot spots and paths are displayed through a visual report, IDE one-key repair and manual correction of a feedback model are supported, intelligent retrieval and reuse of historical cases are realized by relying on FAISS, and the generation accuracy is continuously improved. According to the unit test case rapid generation method based on the AI large model, high-coverage-rate test case generation is achieved through the AI large model, the test case generation efficiency is remarkably improved, the test period is shortened, the software quality is improved, the workload of manually compiling and maintaining the test case is reduced, and the development cost is reduced.

The invention discloses a warehouse level code problem solving system based on a large language model, and relates to the technical field of software engineering, a source code file is extracted from a code warehouse, a static analysis tool is utilized to analyze codes, a warehouse level code graph is constructed, and then code entities and dependency relationships thereof are analyzed; based on the warehouse-level code graph, combining historical problem solving data, and constructing a dynamic evolution knowledge graph through continuous learning; through a context retrieval module, according to the context information of the current problem, dynamically retrieving related solution strategies and generating corresponding code patches, complementing or reviewing suggestions; the validity of the generated patch is verified through static analysis, unit testing and integration testing, and a problem solving strategy is optimized according to feedback of a developer. The problem solving efficiency and accuracy can be improved, the development cost is reduced, the solving process is gradually optimized through an intelligent feedback mechanism, a closed-loop feedback mechanism is formed, and therefore the increasingly complex software development requirements are met.

The invention belongs to the field of test case generation in traditional software engineering, and discloses a Python program test case generation method based on a large language model. The method can automatically generate and optimize a unit test case of a Python program, and comprises the following steps of: extracting public basic context and function information by utilizing project dependent structure scanning and an AST abstract syntax tree, and constructing an accurate cue word template; verification of grammar, operation logic and coverage rate is carried out on the test case generated by the large language model; aiming at the failed test case, designing a new cue word template and an iteration process so as to automatically iterate and improve, and setting the maximum number of iterations so as to ensure the efficiency; and finally, outputting an optimized high-quality successful test case set so as to improve the test efficiency and reduce the waste of manpower and material resources.

The invention discloses a method and a system for realizing automatic code generation by using multiple AI Agents, and relates to the field of electric digital data processing, a test driving agent receives a task transmitted by a previous node, and generates a task number, a task description, test data, a unit test script and a code statement; the code generation agent generates or updates codes according to the received task description and code declaration; the test driving agent returns an integrated test script and test data according to the task number; the continuous integration agent performs integration verification and regression test and feeds back information; the test driving agent returns a function test script, a performance test script and test data according to the task number; and the continuous deployment agent carries out continuous delivery and deployment and feeds back information. By means of mutual conversation and cooperative work of multiple AI Agents and in combination with a feedback mechanism, the problem that a single large model cannot complete complex software development tasks is solved, full-process operation automation can be achieved, and the code generation quality and efficiency can be improved.

The invention belongs to the technical field of equipment knowledge engineering and natural language processing, and discloses an LLM-based constraint and self-repairing uniformization equipment knowledge graph automatic construction method. The method comprises the following steps: firstly, acquiring equipment related document data through network collection, document arrangement and database query; then, utilizing an equipment domain ontology and constraints as preposed soft and hard constraints, driving LLM to generate semantic intermediate representation, and obtaining candidate triples through structured compiling; then, a self-repairing closed loop is formed through semantic unit testing, logic consistency detection and evidence binding verification, and triples which violate constraints and have conflicts or illusions are automatically recognized and repaired; and finally, entity standard identification, cross-document duplicate removal combination and conflict resolution are realized through cross-segment unification and incremental alignment. According to the method, the fragile path that the LLM directly generates the triple and blindly stores the triple is avoided, the illusion and inconsistency problems are effectively inhibited, and the correctness, interpretability and maintainability of the equipment knowledge graph are remarkably improved.

The invention provides a data quality detection and improvement system for a large language model, and relates to the technical field of code quality management. The data analysis and characteristic extraction module is used for extracting structured and semantic characteristics of codes and constructing a code distribution diagram; the multi-dimensional quality detection module comprehensively evaluates the data set quality from three aspects of semantic diversity, structural consistency and error mode; the code optimization and verification module generates an optimization suggestion based on a detection result, and verifies the effectiveness of an optimization scheme through dynamic evaluation and unit testing; the feedback and self-adaptive optimization module is combined with developer feedback and runtime performance test to dynamically adjust a repair strategy, and the complex code repair efficiency is improved through quantum computing auxiliary path search; according to the method, full-process automation from quality detection to optimization verification is achieved, the method is suitable for multi-dependency and multi-module complex code repair tasks, and the quality and efficiency of the code life cycle are improved.

The invention relates to the technical field of SOC modular unit testing, and discloses an AI SOC modular unit testing method and device of a set top box, and the method can automatically construct a testing platform simulating a real use environment, generate testing cases covering various scenes, achieve parallel testing and real-time interference compensation, and improve the testing efficiency. And multi-dimensional analysis and cross-batch alignment can be carried out on test data. Meanwhile, the method also has an adaptive optimization capability, and can dynamically adjust a test strategy according to a test result, so as to continuously improve the test efficiency and accuracy.

Systems and methods for translating a first coding language into a second coding language train a machine learning (ML) model on a first coding language specific data set relating to the first coding language, in which the ML model is trained to translate one or more code sets of the first coding language to respective one or more code sets of the second coding language; using the ML model, generate various unit test cases, in which the unit test cases run the one or more code sets of the second coding language in parallel with the one or more code sets of the first coding language; iteratively test and refine the ML model until a maturity threshold is reached; and upon reaching the maturity threshold, containerize the one or more code sets of the second coding language into one or more applications.

The invention discloses a full-process automatic unit testing method, device and equipment for an AI chip operator, a medium and a product. The method comprises the following steps: generating an engineering directory and a test framework design document through an architect agent according to operator interface information and a DSL operator design document knowledge base; the method comprises the following steps of: generating a test code for unit test by a software engineer agent according to a test framework design document and a domain knowledge base, and storing the test code in an engineering directory; multiple rounds of interaction are carried out through a software engineer agent and a test engineer agent, a test code is gradually corrected into a target test code which can be successfully compiled, a generated test case can be successfully executed and the generated test case meets the code coverage rate requirement, and a target operator code passing a unit test is obtained; and submitting the target operator code and the target test code which pass the normativity check to a code warehouse through a code auditing engineer agent so as to automatically realize the whole-process unit test of the AI chip operator.

The invention discloses a method and device for automatically generating a Java method unit test case based on AI, and the method comprises the steps: building a structured annotation specification, carrying out the automatic traversal and intelligent analysis of a Java project, extracting a method signature, a parameter type and a parameter constraint condition, constructing a test data generation model, and carrying out the automatic generation of the test case. The model is transmitted to an AI service to analyze business logic and boundary conditions, test case codes are generated, test cases are optimized and integrated to a project test directory, finally, code areas with insufficient coverage are identified through coverage rate analysis, a test completion strategy is generated, and iterative perfection of the test cases is completed. The technical problems that in the prior art, project-level automatic test case generation cannot be achieved, the generated test case cannot fully cover business logic and boundary conditions, and context relation understanding is lacked are solved, full-automatic project-level test generation is achieved, and the test efficiency and quality are improved.

The invention relates to the technical field of research and development management and the field of financial science and technology, and discloses a unit test case generation method, which comprises the following steps: compiling a current project to generate a class file, carrying out byte code analysis on the class file, traversing each class and method in the class file, and extracting control flow information and calling relation information. Determining circle complexity and call chain information of each method based on the extracted information, and generating path information required by the test case; simulating the execution path to generate a basic unit test case, determining an effective test case by utilizing an evaluation standard, and taking the effective test case as a template for generating a new case; and identifying the to-be-tested code of which the test case is not generated in the class file, generating a new test case by using the template, integrating the effective test case and the new test case, and generating a complete unit test case set. According to the method, by combining byte code analysis and path simulation, dependence on code context is reduced, and the effectiveness of the test case is improved through variation test and coverage rate evaluation.

The invention relates to the technical field of software testing, and discloses a unit test case generation large model fine tuning method based on feedback reinforcement learning, which comprises the following steps: constructing a unit test fine tuning data set based on demand driving; an Actor model is constructed, and optimization is carried out through a demand-driven unit test fine tuning data set; a Critic model is constructed, and optimization is carried out through a demand-driven unit test fine tuning data set; and generating a unit test case based on the optimized Actor model, mapping the unit test case into a single instant reward based on the optimized Critic model, and obtaining a large model generated based on the unit test case through reinforcement learning. The unit test case generation method capable of automatically generating high-quality unit test cases with aligned requirements and consistent styles is constructed, the test efficiency and quality are remarkably improved, and the bottlenecks of traditional test generation in manual dependence, coverage depth and requirement consistency are broken through.

The invention discloses a code generation method and device based on a large model, equipment, a medium and a product, and the method comprises the steps: carrying out the code generation through a first code large model according to target code demand data, and obtaining an initial code; according to the first code snippet, generating a test code through a second code large model to obtain a unit test code; according to the unit test code and the initial code, code repair based on editing distance regularization is conducted through a third code large model, and a to-be-analyzed code is obtained; performing iterative optimization on the to-be-analyzed code and the unit test code to obtain a target code corresponding to the target code demand data; according to the method, code generation, unit test generation and code repair are carried out in a multi-model cooperation mode, the reliability and accuracy of code generation can be effectively improved, software security vulnerabilities are reduced, and therefore the stability and security of a software system are improved.

The present invention provides a high-reliability unit test automatic generation method and apparatus based on a conversational large language model. First, projects are parsed and analyzed to extract context information, then the testing necessity of a method to be tested is determined, and different test direction lists are generated on the basis of the division of testing purposes. After initial test cases are generated and repaired, input data searching is performed on passed test cases to improve test input data. Subsequently, mutation testing is performed on all test cases, and test cases are regenerated for failed mutations to improve assertion quality. Finally, successful test cases are reduced and merged to avoid overlapping. The present invention is superior to existing methods in terms of coverage, test data integrity, assertion quality, and the like, and has stable performance across projects of different scales and fields. The generated test cases have significant advantages in readability, understandability, maintainability, test input data integrity, assertion quality, and volume.

The embodiment of the utility model discloses full-automatic testing equipment, the full-automatic testing equipment comprises a first conveying unit and a testing device, the testing device comprises a first transfer unit, a testing unit, a testing instrument and an upper computer, the first conveying unit is arranged on one side of the first transfer unit, and the testing unit is arranged on the other side of the first transfer unit. The conveying unit is used for conveying a to-be-tested plate to the first transfer unit; the first transfer unit is arranged on one side of the test unit and is used for conveying the to-be-tested plate to the test unit; the upper computer is connected with the test instrument, and the test instrument is connected with the to-be-tested plate through the test unit. By applying the full-automatic test equipment provided by the embodiment of the utility model, the test process can be fully automatically realized, manual participation is reduced, and compared with a semi-automatic mode in the prior art, the test efficiency of the full-automatic test equipment is greatly improved.

The invention relates to the technical field of vehicle software testing, and discloses a vehicle unit test code generation method and device, equipment, a medium and a product. The method comprises the steps of obtaining a tested code; the tested code and the intention prompt word are input into a large language model to be analyzed, a test intention is generated through the large language model, and the test intention is used for indicating the process needed for testing the tested code; inputting the test intention, the tested code and the template cue word into a large language model for analysis so as to generate a test strategy through the large language model, and recommending a code template and a parameter value required by the test from a template library; and integrating the recommended code templates according to the test strategy, and filling the parameter values into the code templates to obtain test codes. The AI illusion is reduced, and the accuracy of generating the test code is improved.

The invention belongs to the technical field of software engineering, and particularly relates to a unit test method for generating a discrimination model based on retrieval. Searching a to-be-tested function, namely a test case pair, and establishing a code library; constructing an efficient code retriever based on code embedding and mixed feature extraction; constructing a multi-input matching discriminator network, preparing an own data set, and training the network; integrating the code library, the retriever and the discriminator into a local knowledge base, and performing fine adjustment on the large model based on the local knowledge base to obtain a retrieval generation discrimination model; and generating a test case based on the retrieval generation discrimination model, and testing. According to the method, the discrimination model is generated by using retrieval, corresponding change is automatically carried out according to the previous test case, and the test with higher coverage rate and quicker speed can be realized.

The invention provides a code analysis method, system and equipment based on a multi-programming language sandbox and a medium, and belongs to the technical field of code analysis and detection.The method specifically comprises the steps that an input code and a corresponding unit test sample are obtained, and a programming language type specified by the code is determined; sending the code and the unit test sample to a sub sandbox environment of a corresponding language for execution; a compiler in the sub sandbox reports a missing library according to a code compiling result and prompts a user to install the missing library; calling an analysis tool of a code analysis module to analyze the code; compiler feedback of the sub sandboxes and various analysis results generated by the code analysis module are integrated into a large language model; inputting the integration result into a large language model through a preset template; and analyzing and evaluating the code case based on the user instruction. Through multi-language sandbox isolation, automatic analysis tool integration and large language model enhancement, the security, efficiency and quality of a code processing flow are improved.

The invention provides a vector optimization algorithm translation method and device oriented to an RISC-V vector extension platform, and relates to the technical field of computer software cross-architecture translations. The method comprises the steps that source codes of vector optimization algorithms written by other platforms and translation cues are input into a large language model; outputting a translation code of a vector optimization algorithm oriented to the RISC-V vector extension platform; the translated codes are input into a compiler to be compiled; under the condition that compiling succeeds, a compiling product is linked to the test suite to obtain an executable program, and unit testing is conducted on the executable program on the RISC-V vector extension device; and under the condition that the unit test is passed, determining that the translation code is a first version of correct translation code of a vector optimization algorithm oriented to the RISC-V vector extension platform. According to the method, a vector optimization algorithm written by using a special vector language for other platforms is efficiently translated to the RISC-V vector extension platform.

A large language model, trained on source code and natural language text generates a unit test for a change to a file in a pull request of a code repository. An ordered sequence of prompts is created and each is applied serially to the large language model to perform an individual task that leads to the generation of the unit test. The unit test may be added to an existing file or generated as a newly-created file. Each prompt includes the data from a previously-issued prompt of the ordered sequence in order for the model to retain contextual knowledge learned previously. The model generates the unit test as update commands when the unit test is added to an existing file.

The invention discloses a high-precision multi-interface collaborative FTU dynamic function test system and method. The system comprises a protection unit, a test bearing unit, a core test unit and a control system, the protection unit is composed of an acrylic transparent protection cover and a metal plate box body, and a test space is formed. The test bearing unit comprises a quickly replaceable carrier plate, an access structure and a positioning assembly, and the positioning assembly comprises a positioning pin and a guide block; the core test unit comprises a pressing plate structure, an endoscope and a test module; the test module is integrated with a key, a short circuit bolt, a network port, a DB9 and an aviation plug test module; the safety detection unit comprises a safety grating, an anti-pinch device and a pre-feeding induction mechanism, and the safety grating is arranged at the inlet of the test space; and the control system controls starting, execution and result uploading of the test process. The method is used for solving the technical problems of insufficient positioning precision, poor interface test collaboration, low safety protection reliability and limited automation and intelligence level in the existing FTU test.

The invention belongs to the technical field of software engineering, and particularly relates to software testing, in particular to a unit testing method based on a large model. Reading a source code from the tested unit; the code features are enhanced through an enhancement method; performing endpoint labeling and segmentation on the code by using a multi-dimensional code correlation comprehensive evaluation analysis method; inputting the segmented code into a code task unit test thinking chain template and generating a corresponding unit test; the segmented unit test is sent to coverage rate detection and coverage information analysis, and a unit test report is automatically generated according to a test report model; the uncovered information is fed back to the thinking chain to be repaired, and a repaired unit test case is generated and moved backwards. According to the method, the unit test case generated by the segmented feedback thinking is used, so that the test with higher coverage rate can be realized on the whole under the condition that a local high coverage effect is achieved.

The invention provides a local communication unit testing device and testing method based on HPLC and HRF, and relates to the technical field of communication testing, the local communication unit testing device based on HPLC and HRF comprises an HPLC communication module, an HRF communication module, a signal processing module, a control module, an environmental interference simulation module and an output module, according to the invention, the test efficiency and the communication reliability are significantly improved through dynamic spectrum allocation, dual-mode collaborative test and high-precision analysis technologies, a data-driven decision basis is provided for equipment optimization, an FPGA + DSP collaborative architecture is adopted, time-frequency joint demodulation of HPLC and HRF signals is realized, and the performance of the system is improved. And performing dynamic classification comprehensive evaluation on the communication quality in combination with a machine learning algorithm.

A customized prompt generation service automates prompts to a large language model to perform a specified software engineering task. The service stores the custom data of a client that includes code diff hunks, source code segments, code reviews, repaired code, and unit tests from a code base or repository of the client. Prompt templates are associated with each software engineering task that include the requisite information needed for the large language model to perform the target task. A prompt to a large language model includes examples of the software engineering task from the custom data of the client of the service.