146 results about "Snippet" patented technology

Systems and methods for searching, summarizing, and automating workflows for financial related documents, and a user interface for displaying the same, are disclosed. The summaries are generated in response to a user query or through natural-language, conversational chat, and they provide the user with underlying supporting documentation to allow the user to follow-up and / or confirm as necessary. An AI-powered document search is performed to identify relevant documents and relevant snippets within the documents. The relevant snippets and / or documents are then summarized by one or more AI models. The models use metadata associated with the documents from the document search to rank the snippets for summarization. AI agents further automate the workflow.

Techniques are described herein that are capable of providing a recommendation of AI-modified code in context of a developer tool. Based at least on code being developed in a developer tool, an interface element is provided in a user interface of the developer tool. The interface element is configured to receive a prompt that specifies a modification to be performed on the code. Based at least on receipt of the prompt, an AI model is automatically caused to perform the modification on at least a snippet of the code to provide a modified snippet. The modified snippet is processed using a language intelligence tool of the developer tool to provide a processed version of the modified snippet. A recommendation to replace the snippet in the code with the modified snippet is provided by causing the processed version of the modified snippet to be displayed via the user interface.

Embodiments described herein relate to systems and methods for automatically generating content for a generative answer interface of a collaboration platform. The system receives a natural language user input identifying corresponding blocks of text or snippets using a content extraction service. A prompt is generated using the blocks of text and is used to obtain a generative response. The generative response and links to corresponding content are displayed in the generative answer interface and can be inserted into content of the collaboration platform. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Methods and systems are provided for using generative artificial intelligence to evaluate fine-tuned language models. In embodiments described herein, natural language text snippets are generated via a generative language model based on corresponding data. A language model is fine-tuned into a fine-tuned language model via a language model fine-tuning component using the natural language text snippets and the corresponding data as training data. Independent natural language text snippets are generated via the generative language model based on the corresponding data. Each independent natural language text snippet is different than each corresponding natural language text snippet. An evaluation metric of the fine-tuned language model is generated via an evaluation component based on the independent natural language text snippets and the corresponding data.

The invention provides a code generation method which is executed by a code generation system, the code generation system is used for automatically generating a test code, and the method comprises the following steps: receiving a test step description input by a user, retrieving a text code pair vector library and a test method vector library according to the test step description, the text code pair vector library comprises description texts and vectors formed by corresponding test codes, and the test method vector library comprises vectors formed by the test method calling examples; and according to the test code snippets and the test methods corresponding to the similar test step descriptions, reasoning through a large language model (LLM), generating test codes corresponding to the test step descriptions, and presenting the test codes corresponding to the test step descriptions to a user. According to the method, retrieval and LLM reasoning are combined, the complex test code can be automatically generated, and the automatic development efficiency of the complex test scene is improved.

The present invention relates to a snippet information generation method and apparatus, a search result display method and apparatus, a device, and a medium. The search result display method includes: in response to search content, obtaining a first research result; displaying, in a search result page, snippet information corresponding to the first search result, wherein the snippet information is generated on the basis of detailed content and snippet attributes of the first search result, the snippet attributes comprise at least one of the snippet row number, the snippet area size, and snippet typesetting information, and the snippet attributes are determined on the basis of key content matched with the search content in the detailed content.

The invention discloses a code review method, system and device, a storage medium and a program product, and the method comprises the steps: cleaning a to-be-reviewed code snippet, and obtaining an incremental code snippet and position information; vectorizing the incremental code snippets; semantic query is conducted on the vectorized incremental code snippet data based on an ES database according to the cosine law, development rules of similar semantics are obtained and ranked from high to low according to similarity values, and a preset number of first several snippets are obtained; recombining cue words, wherein the recombining cue words comprise incremental code snippets and a preset number of development rules; and sending the recombined cue word to a general generative large model to obtain an examination suggestion. The code snippets to be examined are pushed to the gilab warehouse by a developer, and are cleaned by the gilab warehouse through webhook triggering. According to the review method, the whole process is automatic, the review efficiency is high, review can be conducted according to the development rules in the team, knowledge sharing is achieved, the review result is objective, and the problem that the text exceeds the upper limit is avoided.

Method for prioritizing code violations in a computer program, using machine learning includes: analyzing the computer program for code violations; extracting code snippets containing violations from the computer program; training a machine learning model to differentiate between vulnerable and non-vulnerable code in the extracted code snippets; inputting the extracted code snippets to a trained machine learning model to assign a vulnerability probability score to each snippet, wherein each vulnerability probability score indicates a severity of the violation for a respective snippet; ranking the code snippets based on their respective vulnerability probability score, wherein a higher score indicates a higher likelihood of causing severe vulnerabilities; and displaying the ranked code snippets to be fixed for their code violations.

A retrieval-augmented neural transformer model with chunk cross-attention predicts a code review given a proposed source code change, represented as a code diff hunk, and a set of historical code review comments. The code diff hunk represents proposed edits to a source code snippet with its surrounding context that has not been changed. The historical code review comments are associated with code edits that are semantically similar to the proposed source code changes. The code diff hunk is partitioned into chunks which are used to find semantically similar historical code review comments. The set of historical code review comments is aggregated and used to guide the model in makings its predictions.

Embodiments of the present disclosure provide a code generation method, a device, and a storage medium. In the embodiments of the present disclosure, a custom retrieval library can be constructed, wherein the custom retrieval library comprises sub-snippets segmented from reference code snippets; on this basis, a target sub-snippet meeting a preset similarity requirement can be retrieved for an uncompleted code from the custom retrieval library; and then a code context of the target sub-snippet can be acquired to generate a retrieval result. Since the custom retrieval library is at the sub-snippet level, the code diversity in the custom retrieval library can be effectively enhanced, and various code variants can be covered, so that the retrieval precision can be effectively improved, effectively improving the quality of retrieval results. Moreover, prompt information can be constructed on the basis of the generated retrieval result and input into a code generation model.

The invention is applicable to the technical field of code development, and provides a code modification method based on a CURD scene, which comprises the following steps: acquiring a business demand of code modification, and acquiring a data model required by code modification according to the business demand; based on the data model, obtaining a code file related to a CURD scene during code modification; analyzing a calling relationship among functions related to the CURD scene in the code file, and constructing a function calling graph; on the basis of the service requirements and the data model, service code snippets are generated, the service code snippets are combined and modified to the code file, code modification of the service requirements can be processed, and development efficiency and code quality are further improved.

The invention discloses a general algorithm reflection evolution (MHRE) method based on a large language model. The MHRE method comprises the steps of individual coding, population construction and population iterative optimization. In the individual coding stage, the large language model generates code snippets containing performance functions and architecture functions, and the snippets define basic structures and behaviors of population individuals. In the population construction stage, a prompt word of a specific task is used for further refining code snippets, and initial population individuals are formed. In the population iterative optimization stage, through a cross evolution module, a cooperative evolution module, a framework upgrading module and other modules, iterative optimization of population individuals is achieved in combination with a generation-standardization-evaluation-selection loop module (GSES), and algorithm individuals with the optimal performance are generated. The MHRE method can automatically generate, optimize and evaluate a plurality of algorithms, improves the adaptability, universality and optimization effect of the algorithms, and is especially suitable for complex optimization tasks such as industrial production, resource allocation and logistics path planning.

A vulnerability detection and repair system utilize a classifier model to detect a software vulnerability in a source code snippet and the tokens in the source code snippet attributable to the vulnerability. A large language model is then given the vulnerable source code snippet, its vulnerability type, the vulnerability tokens, and a few-shot examples to determine whether or not the source code snippet includes the identified vulnerability. The few-shot examples include positive and negative samples of the type of vulnerability to guide the large language model towards the correct output.

A source code repair system generates a prompt including a few-shot examples of code changes made previously to correct a particular source code vulnerability. The prompt is given to a large language model to generate repair code for a source code snippet having the same vulnerability. Code changes made for a particular source code vulnerability, in the form of code diffs, are clustered into groups of closely related code change embeddings. A select few of the code diffs in each group having a closest mean of the cluster are used as the few-shot examples for the corresponding source code vulnerability.

In some implementations, a remediation system may receive, a tracking system, a set of tickets associated with at least one data record. The remediation system may search, in the at least one data record and using a set of contexts indicated in the set of tickets, for a set of sensitive data snippets. The remediation system may generate a set of proposed remediations corresponding to the set of sensitive data snippets. The remediation system ma6y output, for each sensitive data snippet in the set of sensitive data snippets, a corresponding proposed remediation in the set of proposed remediations. The remediation system may selectively apply the set of proposed remediations based on inputs from a user.

A vulnerability detection and repair system utilizes a classifier model to detect software vulnerabilities in source code fragments and lexical elements attributable to the vulnerabilities in the source code fragments. The vulnerable source code fragment, its vulnerability type, vulnerability lexical elements, and few sample examples are then given to a large language model to determine whether the source code fragment includes the identified vulnerability. The few sample examples include positive and negative samples of vulnerability types to direct the large language model towards correct outputs.

An assert statement generator employs a neural transformer model with attention to generate candidate assert statements for a unit test method that tests a focal method. The neural transformer model is pre-trained with source code programs and natural language text and fine-tuned with test-assert triplets. A test-assert triplet includes a source code snippet that includes: (1) a unit test method with an assert placeholder; (2) the focal method; and (3) a corresponding assert statement. In this manner, the neural transformer model is trained to learn the semantics and statistical properties of a natural language, the syntax of a programming language, and the relationships between the code elements of the programming language and the syntax of an assert statement.

Systems and methods are disclosed for automatically extracting relevant information from various source document that include tabular data. The tabular data in various forms can be received as mixed with other dissimilar data. Tabular data can appear in different orientations, document types, and can be fragmented horizontally or vertically. The proposed technique automatically detects table header data in certain regions of the received source document and associates values to the extracted headers. The proposed system is capable of combining different snippets of smaller tables into a single cohesive and monolithic table with headers designated by a set of keywords and all the values in the various columns (and / or rows) included under or along proper headers.

The invention provides an applet permission security detection method and system, and relates to software engineering.The method comprises the steps that API test code snippets of a super application are generated based on a large language model, the large language model is guided according to an API dependency tree to generate a plurality of API test code snippets, and executable test codes are constructed; dynamically executing the executable test code in the simulation environment, and obtaining the actual permission configuration of the applet according to the dynamic execution result; obtaining standard permission configuration of the super application, comparing the actual permission configuration with the standard permission configuration, and if the situation that the actual permission configuration does not accord with the standard permission configuration occurs, determining that the super application has an API permission exception problem; the method comprises the following steps: constructing an abstract syntax tree according to an applet source code, performing granularity optimization on the basis of the abstract syntax tree to construct an abstract method tree, searching an abnormal API (Application Program Interface) with a permission defect in the abstract method tree, determining a function body name where the abnormal API is located, performing front-end and rear-end dual confirmation according to the function body name, and determining a calling condition and a binding condition of the abnormal API. Accurate detection of the permission defect in the super application is realized.

One or more new coding instructions are generated using a language model (LM) prompted to perform one or more genetic operations on one or more seed coding instructions of an initial set of coding instruction-snippet pairs. One or more respective coding snippets are generated to implement the one or more new coding instructions using a LM prompted to generate coding snippets for the one or more new coding instructions. A generational set of coding instruction-snippet pairs comprising the initial set of coding instruction-snippet pairs and a new set of coding instruction-snippet pairs comprising the one or more new coding instructions and the one or more respective coding snippets is created.

Systems and methods for searching, summarizing, and automating workflows for financial related documents, and a user interface for displaying the same, are disclosed. The summaries are generated in response to a user query or through natural-language, conversational chat, and they provide the user with underlying supporting documentation to allow the user to follow-up and / or confirm as necessary. An AI-powered document search is performed to identify relevant documents and relevant snippets within the documents. The relevant snippets and / or documents are then summarized by one or more AI models. The models use metadata associated with the documents from the document search to rank the snippets for summarization. AI agents further automate the workflow.

This invention relates to systems and methods for performing double-level ranking of documents. The system implements methods for retrieving documents based on a pre-processed user query to generate a document level ranking of one or more documents that are determined to be relevant. The system implements methods for aggregating one or more sub-topic snippets from the document level ranked documents. The system further implements methods for generating a topic level ranking of the one or more sub-topic snippets. Once topic level ranking of the one or more sub-topic snippets has been performed, the system implements methods for transmitting the topic level ranked one or more sub-topic snippets to a user associated with the user query.

A mutated issue in AI generated source code is identified. For example, the mutated issue may be a mutated type of malware. A snippet of source code in the AI generated source code that comprises the mutated issue is identified. A vector based on the snippet of source code in the AI generated source code that comprises the mutated issue is generated. Vectors of a second source code (e.g., a new software application) are compared using the vector generated from the snippet of source code in the AI generated source code that comprises the mutated issue. The comparison is used to identify new types of issues in the second source code.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating answers to medical questions using neural networks and other components. In one aspect, a method includes: obtaining question data representing a medical question; obtaining a plurality of document snippets from a medical database that stores medical documents; for each document snippet in the plurality of document snippets, determining a relevance score for the document snippet by using a ranking neural network based on the document snippet and the medical question; selecting, based at least in part on the relevance scores, a subset of the plurality of document snippets; generating a prompt that includes (i) the medical question and (ii) the subset of the plurality of document snippets; and generating an answer to the medical question based on processing the prompt using a generative neural network.

A computer-implemented system is disclosed which enhances legal research by utilizing a first user interface (UI) to receive search criteria as text prompts from users, which are then processed by an application program interface (API) across an electronic connection. The system includes an instruction base for expanding the prompts into snippets for a machine learning network, which defines the legal database searches. The process further refines the prompts through a transformation module, breaking down and tagging the text for compatibility with the machine learning network. Coupled with non-transitory computer memory, the system efficiently outputs results in response to the applied search criteria. A second interactive UI displays these results and allows for user interaction to delve into detailed information, optimizing the legal information retrieval process. Also disclosed is a method for processing legal research queries enhanced by a machine learning network.

Embodiments described herein relate to systems and methods for automatically generating content for a generative answer interface of a collaboration platform. The system receives a natural language user input identifying corresponding blocks of text or snippets using a content extraction service. A prompt is generated using the blocks of text and is used to obtain a generative response. The generative response and links to corresponding content are displayed in the generative answer interface and can be inserted into content of the collaboration platform. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Implementations are described herein for using GMs such as LLMs to generate and / or modify source code. In various implementations, a modification made to either a source code comment or a snippet of source code that is referenced by the source code comment may be detected in first rendered GM output. A first GM prompt may be assembled to include data indicative of the modification and the source code comment and / or the snippet of the source code that is referenced by the source code comment. The first GM prompt may be processed using GM(s) to generate a first GM response, which may be operable to provide second rendered GM output that includes a new version of the source code in which the modification to the one of either the source code comment or the snippet is reflected in the other of the source code comment or the snippet.

The present invention relates to the technical field of text layout. Disclosed are a text format adjustment method and system based on large language model code generation. The method comprises: constructing an API vector knowledge base on the basis of a development document of target software, the API vector knowledge base comprising vector representations of text format adjustment attributes and knowledge snippets of the text format adjustment attributes; acquiring a user format adjustment requirement; vectorizing the user format adjustment requirement, and by matching a vector representation of the user format adjustment requirement with the vector representations of the text format adjustment attributes, obtaining the knowledge snippet and an input / output example of the corresponding text format adjustment attribute; and embedding the user format adjustment requirement and the knowledge snippet and input / output example of the corresponding text format adjustment attribute into a code generation prompt word template, and generating a text format adjustment result on the basis of a large language model. The present invention can reduce the workload of developing and designing text format templates, and can more flexibly accommodate various format requirements.

Techniques for intelligently prompting an LLM to fix code are disclosed. A corpus of release notes for a set of libraries is accessed. The release notes include information describing deprecated or removed APIs associated with the libraries. The corpus is stored in a vector database. A code snippet is accessed. This snippet is identified as potentially using a deprecated API. The code snippet is used to identify a set of release notes from the vector database. These release notes are determined to satisfy a threshold level of similarity with the code snippet. An LLM prompt is built and is fed to the LLM. The LLM prompt instructs the LLM to update the code snippet based on the identified set of release notes. Output of the LLM is displayed. This output includes a proposed rewritten version of the code snippet.

A computing system comprising one or more computing devices can access a file comprising documentation that includes a textual description and a source code snippet written to comply with a programming language syntax. The computing system can identify the source code snippet. The computing system can generate an executable based on the source code snippet. The computing system can initiate a test process that accesses the executable, the test process causing the executable to cause an event. The computing system can determine that the event is a valid event or an invalid event.