1109results about "Software testing/debugging" patented technology

In one embodiment, a computer-implemented method executed using one or more processors of an incident management system comprises receiving a notification of an incident associated with a computer system, and in response to receiving the notification: extracting an error message from the notification; reading a set of computer program code changes that have been implemented in the computer system; matching the error message to the set of computer program code changes and outputting a set of one or more candidate code changes that may correspond to the error message; based on the set of candidate code changes, generating an automatic remediation for the incident; and using the one or more processors of the incident management system, executing the automatic remediation on the computer system.

Systems, methods, and software are disclosed herein for resolving coding issues via generative AI integrations in various implementations. In an implementation, in a debugging session, a computing apparatus receives a user query relating to an exception in source code. The computing apparatus elicits a response from a generative AI model which is tasked with identifying an interaction pattern for resolving the user query. The computing apparatus mediates the debugging session according to the interaction pattern identified by the generative AI model.

A system and method for optimizing the deployment of a cloud computing environment is presented. The method includes: inspecting the cloud computing environment; generating a representation of the cloud computing environment based on a result of inspecting the cloud computing environment; determining a resource cost associated with each entity of a plurality of entities represented in the generated representation of the cloud computing environment; generating an optimization action for an entity of the plurality of entities, wherein the optimization action reduces the determined resource cost; and initiating the optimization action in the cloud computing environment.

Access to log event data and corresponding source code is obtained and static code analysis is performed on the source code to produce analysis output. First vectors representing the log event data and second vectors representing the analysis output are generated. A similarity analysis is performed on the first vectors and the second vectors. A probabilistic relevance score associating a given log event with a segment of the source code is determined based on the similarity analysis. A visualization is generated for log events based on the probabilistic relevance score.

A computer-implemented method includes customizing artifacts of a large language model (LLM). A generative artificial intelligence (AI) (genAI) function of a software application that interfaces with the LLM and is instrumented for customer-side management is activated, as a genAI application. A verification test is activated to determine whether interactions between the LLM and genAI application generate expected results. Based on a result of the verification test, either adjusting the artifacts of the LLM or updating the genAI function of the software application. A new verification test is defined to determine whether interactions between the LLM and genAI application generate expected results. The new verification test is activated.

Efficient generation of multi-platform compatible source code and test code with consistency and quality in software development SOLUTION: The schema-driven development system includes a user input unit, a schema generation unit, and a code generation unit, wherein the user input unit acquires data in various formats such as a natural language and a CSV file, the schema generation unit converts the acquired data into a schema, and the code generation unit generates a source code and a test code based on the generated schema.SELECTED DRAWING: Figure 1

Client support for applications to access services can be provided. In an example, a computing system can receive, from a client device, text input for an in-progress application to access a service. The client may be prevented from accessing the service prior to the in-progress application being approved. The computing system can detect an error associated with processing the in-progress application based on the text input and contextual information. the computing system can determine that the error is associated with the text input or with a technical issue associated with the in-progress application. The computing system can generate a recommendation associated with the error based on determining that the error is associated with the text input or the technical issue. The computing system may output the recommendation to the client device for use in resolving the error with processing the in-progress application.

This invention relates to systems and methods for real-time dynamic classification and orchestration of test automation components in distributed DevOps environments. The system features an Auto Identify Automation (AIA) engine that leverages supervised learning, Multi-Modal Artificial Intelligence (AI), and Generative AI technologies. It includes a Smart Scenario Designer interface that allows users to author test scenarios using handwriting and voice inputs, which are processed in real-time by AI-driven handwriting recognition, voice recognition, and Natural Language Processing (NLP). The system dynamically suggests relevant automated components via a smart bubble pane, facilitating rapid scenario creation. The architecture is tool-agnostic and scalable, with a Shared Workbench Engine that supports real-time collaboration and conflict resolution. The system continuously adapts and improves, ensuring that the automation suite remains consistent, up-to-date, and aligned with evolving software requirements, enabling efficient and user-friendly management of complex test automation processes.

Systems and methods for generating system architecture representation by implementing machine learning (ML) techniques are disclosed. The system receives a request for generating system architecture representation from at least one user and generates an architecture summary for the received request. Further, the system determines an architecture pattern relevant to the received request based on a context of the generated architecture summary using the large language models and machine learning (ML) models. The system further generates an architecture code corresponding to the received request. The system validates the generated architecture code for determining errors in the generated architecture code based on compliance-based rules, legal-based rules, and security-based rules specific to an organization using the machine learning (ML) models. The system further generates the at least one system architecture representation based on successful validation and outputs the generated architecture code and the at least one system architecture representation on a user interface.

Accuracy, reliability, and response speed improvements for software applications executed by a computing system or platform are provided herein. There are provided systems and methods for multidimensional error causal analysis for error intercorrelations that impact application availability. A service provider may utilize different computing services for data processing to provide different computing services to users, such as via websites and / or applications of the service provider. Due to errors, users may be unable to utilize applications or may face decreased performance and application availability. To improve application performance, error causal analysis may be performed that identifies error intercorrelations that impact application availability and other performance by identifying error effects on each other. Causal statements may be intelligently generated to then identify error intercorrelations. Once generated, these statements may be tested and verified to allow debugging teams and others to fix errors that reduce application performance and availability.

There is provided a system and method of automatic software testing. The method includes obtaining an input including software code of a software program and metadata, and feeding the input to a machine learning model to generate a test suite usable for testing the program. The test suite comprises a set of tests meeting a predefined condition. The test suite is generated by generating at least one question related to at least one of: expected intents of one or more sections of the software code, or tests for testing the sections, and presenting the at least one question to a user; upon receiving feedback from the user, analyzing the feedback with respect to the predefined condition, and determining whether to generate at least one new question; and, in response to an affirmative determination, repeating the above process with respect to the new question, until the predefined condition is met.

Embodiments of the present disclosure relate to applications, platforms, architecture, etc. for automating software requirement verification. In particular, one or more generative language model (GLM) prompts may be generated based at least on program information that describes a software program and based on requirement information that corresponds to a requirement of the software program. Based on such prompts, the GLM may be able to automatically identify segments of the software program information that relate to the requirement. Further, based on the identified segments and the GLM prompts, the GLM may be able to automatically create (e.g., based on one or more additional prompts) testing architecture that may be used to verify whether the software program satisfies the requirement.

[Problem] To support automation of a test. [Solution] This information processing system is characterized by including: a scenario generation unit for generating a scenario of a test by giving a first prompt instructing generation of the scenario to a large-scale language model on the basis of a test case describing an outline of the test; and a script generation unit for generating a script by giving a second prompt to the large-scale language model, the second prompt instructing generation of the script that causes a computer to execute the test on the basis of the scenario.

Embodiments facilitate software analysis by machine learning (ML) models, through extensible software analysis architecture (ESAA) or software analysis work allocation (SAWA). Pluggable ESAA ML modules include a vetted prompt which is actionable for software analysis, with a vetting certification. Some ML modules contain computational cost information such as a token count or model round trip time. Tools are tailored to ML analyzers to control background execution, availability offerings, and results displays. SAWA determines how well a software analyzer meets a prompt's software analysis requirements, and an ML planning model generates an analysis plan that balances software analysis workloads among ML analyzers and non-ML analyzers. ML analyzers are favored for summarization, task decomposition, task scheduling, and source code change review, while non-ML analyzers are otherwise favored. Non-ML analyzers gather control flow, data flow, internal structure, and similar context which is then supplied to an ML analyzer.

Trusted execution environment microservice debugging is provided. A debug operation is performed on a microservice running in the trusted execution environment utilizing a debugger server within a trusted execution environment based on a set of microservice debug messages received from a client device of a user. A microservice debug result of performing the debug operation on the microservice running in the trusted execution environment is sent to a debugger client within a privilege separation container outside the trusted execution environment via a secure channel between a privilege separation secure channel client within the trusted execution environment and a privilege separation secure channel server within the privilege separation container outside the trusted execution environment utilizing the debugger server. The microservice debug result of performing the debug operation on the microservice running in the trusted execution environment is sent to the client device of the user utilizing the debugger client.

The invention relates to the technical field of vulnerability detection, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses a dependency processing method, device, equipment and medium for a multi-component project, which comprises the following steps of: analyzing description files of different component types in a multi-language project, extracting dependency data to generate unified dependency description; the method comprises the following steps: constructing a panoramic dependency graph containing component nodes and dependency edges, identifying version constraints of the component nodes to generate a unified constraint model, determining a compatible version combination by processing the constraint model, extracting a multi-component operation environment configured with a tool during installation and operation of environment configuration data, and installing project dependency according to the compatible version combination. And scanning the panoramic dependency graph based on the vulnerability information source to generate a repair instruction for updating the vulnerability component version. According to the method, centralized analysis and collaborative management of multi-language dependency are realized through unified dependency description and constraint models, and conflicts are reduced and the consistency and security of the system are improved in combination with environment configuration and a loophole repair closed-loop process.

Systems and methods for using machine learning to predict tests for testing application programming interface updates. The system receives a prediction request to predict one or more tests for testing a candidate update to an application programming interface, wherein the prediction request comprises an indication of the application programming interface. The system may determine, using the indication of the application programming interface, a plurality of parameters associated with the application programming interface and obtain, based on the plurality of parameters using a machine learning model a prediction of a plurality of tests to be executed for the candidate update to the application programming interface. The system may determine, a first subset of the plurality of tests enabled to be executed without user input and cause the first subset of the plurality of tests to be executed without the user input.

The present disclosure of the various embodiments relates to using a large language model to assistant with the creation of secure code and / or the completion of threat modeling tasks in software development. In one example, a system comprises a computing device configure to identify a prompt that requests generating secure source code for source code with a security vulnerability. A security data source is queried for a security threat embedding. The security threat embedding is received from the security data source and an augmented prompt is generated. The augmented prompt is transmitted to the large language model. A secure source code is received from the large language model and imported into application source code in a software development environment.

The technology relates to program monitoring, which can involve using metadata as part of span information evaluated by the system in order to identify a location of interest in user (source) code. This can be used to pinpoint the exact code location that participated in a trace being debugged. The span information may be enriched with the code location from which it originated. The technology is also able to avoid unnecessary execution of instrumented functions by creating an alternate code execution path. This alternate path gets the code path to a variation of the instrumented function, in which the variation has a different set of operations. Upon determination that an execution call path has been previously followed, the alternate path would be automatically called instead of the path with the original instrumented function.

Aspects of the subject disclosure may include, for example, obtaining first information indicative of function(s) that a software application is designed to be capable of performing; obtaining second information indicative of acceptance criteria that define whether the software application has performed the function(s); generating (based upon the first and second information) a first prompt configured for input to a generative artificial intelligence (AI) mechanism; inputting the first prompt to the generative AI mechanism; receiving at least one test plan that was generated by the generative AI mechanism; and facilitating execution of the software application based upon the test plan. Other embodiments are disclosed.

An information processing apparatus identifies a first path on the basis of relationship information indicative of data transfer relationships between two of functions in an AI system, management target data, and relevant persons. The first path indicates a route which follows a data transfer relationship from a start point to an end point with the start point set to one of the functions, the management target data, and the relevant persons and the end point set to another one. The information processing apparatus selects a first check item related to a first data transfer relationship on the route indicated by the first path from among a plurality of check items associated with any of the data transfer relationships indicated by the relationship information. Furthermore, the information processing apparatus displays information on the first check item.

Arrangements for optimizing software performance through generative artificial intelligence are provided. Execution data associated with a user interacting with an application programming interface through a user interface on a computing device may be received. Trace data based on the execution data may be generated. The trace data may indicate that a performance of an application is degraded. One or more performance prompts specifying program behavior may be generated. Application source code and the performance prompts may be sent to an artificial intelligence engine for performing key performance indicator driven code analysis. The one or more performance prompts may have been executed in the artificial intelligence engine for pretraining a large language model. Optimized source code may be received from the artificial intelligence engine. Performance testing may be executed on the optimized source code. An action may be performed to address the performance of the application.

To assist users in artificial intelligence driven software development, techniques for agentic software development assistance leveraging application behavioral models are disclosed. A software agent receives a codebase issue and orchestrates an iteration of a refining process. To orchestrate the process, the software agent feeds the codebase issue and instructions to generate a plan into a development assistant, which automatically returns a plan to the agent. The agent feeds the plan and instructions to generate a solution to the codebase issue back to the development assistant, which automatically returns a solution to the agent. The agent feeds the solution back to the development assistant, which tests the solution in order to verify the solution's quality in responding to the codebase issue. The solution is awarded a quality score, and the quality score is returned to the agent. Based on the quality score, another iteration of the refining process can be performed.

To support large scale software refactoring by using a language model.SOLUTION: An information processing device is provided with execution control means for extracting a group of a plurality of code blocks including similar description determined by a prescribed language model to have a high similarity of feature information of a program by at least a portion of a set of a plurality of programs constituting prescribed software by using the language mode, and generating a common code block describing common processing based on the similar description and a plurality of partial code blocks corresponding to each code block on the basis of difference between each code block and the similar description from the plurality of code blocks belonging to the group by using the language model.SELECTED DRAWING: Figure 1

The process validation platform disclosed herein enables automated validation of data transformation processes based on simulation-based data. For example, the process validation platform can generate metadata associated with a data transformation environment, evaluate the metadata, and generate test data based on the metadata. The process validation platform can generate test cases (e.g., test records) and associated code samples based on the test data and enables updates and review of the test cases. The process validation platform can transmit the code samples to the data transformation pipeline for execution of the associated data validation tasks.

A method, a system, and computer program product for managing an application programming interface (API). A specification file including values describing an API is received. A template of the API is retrieved based on at least one of the values. A source code of the API and a test corresponding to the source code are generated using a parameterized predefined code from the template that is modified to replace parameters based on the values of the specification file. The source code of the API is validated using the test.

Presented is a system (100) and a method (200) for optimizing computer simulated environment(s) used for testing of an autonomous system (404). The method (200) comprises generating a virtual representation of each failed test case in a computer simulated environment (102) based on the received one or more failed test results of the failed test cases and simulating the execution of the failed test case within the virtual representation of the autonomous system. The method (200) analyzes the simulated execution to identify deviation data from an expected behavior of the autonomous system and generates a feedback-based learning algorithm to train the computer simulated environment (102) based on the deviation data and expert inputs received from a second source. Furthermore, the method (200) comprises deploying, upon training, the feedback- based learning algorithm onto a test execution platform for testing of a system- under-test, wherein the system-under-test is associated with the autonomous system.

Disclosed herein are methods, systems, servers, and computer-readable media for interpreting computer code with a multimodal machine learning model. In an embodiment, this comprises: receiving, an input comprising at least one of a text prompt, file prompt, or data object, determining, using a multimodal machine learning model, that the input requires implementing computer code, and in response to determining the input requires implementing computer code: generating computer code based on the input, executing the generated computer code using a code interpreter, and providing, through an interface, an output based on the generated computer code.

To provide a software fault localization method based on feature intersection and KAN network. The method first extracts different types of features from bug reports and source code files, then uses a crossover layer to crossover and correlate the features and extract hidden relationships between them. Furthermore, by utilizing the KAN network's learning of parameterized nonlinear activation functions, the process of fitting a polynomial function using the feature crossover network is transformed into a process of fitting multiple univariate functions, thereby overcoming the "curse of dimensionality" and more accurately capturing and adapting to complex function changes and complex high-order feature interaction information in defect localization, thereby improving defect localization performance. Finally, the output of the KAN network is input to a fully connected layer to calculate a final similarity score, which is then sorted in descending order based on the final similarity score to obtain the defect localization results.

Systems and methods are provided for a prompt and content generation service to validate checkpoints of large language models (LLMs). The prompt and content generation service may execute use case scenarios against checkpoints of LLMs using a prompt and expected output regarding each of the use case scenarios. The prompt and content generation service may then retrieve or receive generated outputs by the LLMs to compare against the expected outputs for each use case scenario. By comparing the expected outputs to the generated outputs, the prompt and content generation service may determine a degree of matching for each use scenario and an overall score taking into the account the degree of matching for all of the use case scenarios.