109 results about "Snippet" patented technology

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.

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.

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.

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.

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.

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.

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.

The embodiment of the invention provides a code conversion method and device, a storage medium and a program product. The method comprises the following steps: segmenting a to-be-converted code file into a plurality of code snippets, retrieving a conversion rule matched with each code snippet from a preset conversion rule library, and retrieving a knowledge fragment matched with each code snippet from a user private knowledge base; based on the multiple code snippets, the conversion rules matched with the code snippets and the knowledge snippets matched with the code snippets, target cue words are generated, the code conversion model is guided through the target cue words, and the code snippets to be converted are recognized from the multiple code snippets; the to-be-converted code snippets are rewritten on the basis of the conversion rules matched with the to-be-converted code snippets and the knowledge snippets matched with the to-be-converted code snippets, so that the to-be-converted code file can be converted into the target code file under the target version more accurately by the code conversion model; and the accuracy of a code conversion result between different versions is improved.

Some embodiments find locations of targets which are related to a symbol in a source code snippet, when the snippet is external to a project codebase. Finding a target's location allows user-directed or proactive automatic navigation from the symbol into the codebase, display of data type, signature, and other semantic information of the symbol, proactive automatic creation of an import statement for a definition of the symbol, and other utilizations of the target location in an enhanced editor or enhanced debugger or another tool. In some scenarios, the external snippet is generated by an artificial intelligence agent, using part of the codebase as context. Some embodiments find a target of an external snippet's symbol in another external snippet, allowing a tool utilization that is informed by the project codebase even when both snippets are outside the project codebase.

Example methods for providing natural-language replies are disclosed herein. An example method includes receiving a natural-language input; providing, via the display generation component, a set of candidate snippets based on the natural-language input, wherein each candidate snippet of the set of candidate snippets corresponds to a respective response to the natural-language input; receiving, via the one or more input devices, an input corresponding to a selection of a candidate snippet of the set of candidate snippets; and providing, via the display generation component, a response to the natural-language input corresponding to the selected candidate snippet.

A method comprising, transmitting, from an API server to a backend server, one or more domain knowledge files; receiving, at the API server, a natural language input; transmitting the natural language input from the API server to the backend server; receiving, at the API server, a one or more snippets of executable code, one or more comments, or one or more values. A system and computer-readable medium are also disclosed.

A system generates synthetic documents from source documents. The system receives a source document and generates prompts for generating sections of a synthetic document from the source document. The system receives sections of the synthetic document based on execution of the machine learning based language model. For each of one or more portions of the synthetic document, the system determines a snippet of the source document that provides support for the portion of the synthetic document. The system sends the synthetic document for display via a user interface. The system receives a request via the user interface to inspect a portion of the synthetic document. The system identifies a snippet of the source document corresponding to the portion of the synthetic document and sends the snippet of the source document for display via the user interface.

Technology is disclosed for programmatically generating a summary by a language model of search results based on the corresponding relevance of the search results to an input search query to a search engine. A user inputs a search query into a search engine and the search engine determines and ranks a set of search results based on the relevance of each of the search results. A snippet of information is determined for the most relevant search results to the input search query. The snippets of information are used to generate an input prompt to a language model with an instruction to generate a summary of the snippets of information based on the input search query. The generated summary is provided in response to the user to the search query and / or is cached in order to provide the generated summary in response to similar search queries.

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.

There is provided an offloading data interfacing method between a DBMS storage engine and a computational storage device. In a query offloading method according to an embodiment of the disclosure, a DBMS may generate an offloading code which is a code for offloading a part of query computations, based on a received query, when a query execution request is received from a client, and may deliver the offloading code to a storage in which a DB is established in a CSD. Accordingly, in a DB system using a CSD, a snippet for offloading a part of query computations may be defined, and a DBMS and a storage are interfaced by using the offloading snippet, so that a guideline for execution of a query by interworking between the CSD-based storage and the DBMS is provided.

The embodiments of the present disclosure relate to a code editing method and device, and a device and a storage medium. The method provided herein comprises: sending to a target model an editing request which is associated with a target code, wherein the editing request indicates an output format of the target model, and the output format instructs the target model to output a code snippet, which needs to be replaced, and a code snippet, which needs to be updated, in a code file to be edited; acquiring response content which is output by the target model on the basis of the output format, wherein the response content comprises a first snippet to be replaced, and a second snippet used for replacing the first snippet; and generating an editing result of the target code by means of parsing the response content. In this way, the embodiments of the present disclosure can improve the output efficiency and accuracy of a target model.

Technology is disclosed for programmatically generate answers for a user that are responsive to aspects of a conversation. In one implementation, a conversation record is processed to determine a message embedding of a most recent message received. The message embedding is used to determine a semantically similar question embedding of a conversational snippet from a knowledge base. An answer-generation input instruction for a language model is generated based on the most recent message, the conversational snippet, and an answer-format instruction. The language model is directed to produce an answer output, which is presented via a user interface. An answer-augmentation instruction for the language model is generated based on the answer output, similar messages sent by the user based on string similarity with the answer output, and an augmented-answer format instruction. The language model is directed to produce an augmented-answer output, which is presented via the user interface.

There is provided a method for scheduling offloading snippets based on a large amount of DBMS task computation. A DB scheduling method according to an embodiment of the disclosure includes determining, by a DBMS, whether to offload a part of query computations upon receiving a query execution request from a client, generating, by the DBMS, an offloading code which is a code for offloading a part of the query computations, based on the received query, when offloading is determined, selecting one of the plurality of storages in which a DB is established, and delivering the offloading code. Accordingly, snippets which will be generated simultaneously are scheduled for CSDs, so that resources are equally utilized, a query execution time is reduced, and reliability on data processing is enhanced.