Aspects of the disclosure relate to conducting automated web service testing in a continuous integration and delivery test deployment environment using artificial intelligence (AI) generated test data. In some embodiments, a computing platform may receive, from a developer computing platform, a test code request, receive, from a web service computing platform, a training data set, configure a test data set based on the training data set and the test code request, use AI engine to apply one or more corrections to the test data set based on the test code request and to produce a corrected test data set, execute the test code using the corrected test data set to produce test code output results, and send, to the developer computing platform, the test code output results.

Aspects of the disclosure relate to conducting automated web service testing in a continuous integration and delivery test deployment environment using artificial intelligence (AI) generated test data. In some embodiments, a computing platform may receive, from a developer computing platform, a test code request, receive, from a web service computing platform, a training data set, configure a test data set based on the training data set and the test code request, use AI engine to apply one or more corrections to the test data set based on the test code request and to produce a corrected test data set, execute the test code using the corrected test data set to produce test code output results, and send, to the developer computing platform, the test code output results.

A method and system for finding vulnerabilities in a program using fuzzing have been provided. The disclosure provides a vulnerability detection framework using a language agnostic single fuzzer that can fuzz smart contracts written in different programming languages. The idea here is that a smart contract written in a high-level language is converted/compiled into an LLVM intermediate representation (LLVM IR) code and then perform the fuzzing on this LLVM IR code instead of fuzzing smart contract source code directly. The process of generating fuzz driver, report driver is automated by handling the standardization issue by carefully dividing the smart contracts into categories. The present disclosure is proposing processes of automation of fuzz or report driver generation. Further the language agnostic feature (done with intermediate representation) is also achieved. Further profiling is achieved which processes fuzzer output and generates meaningful data points.

The present disclosure relates to a system, method, and computer program for testing the accuracy of software that identifies user interface (UI) elements on a webpage (“the auto-identifier software”). The system enables a user to tag UI elements on a webpage with labels. The system creates a normalized specification for the webpage, where the specification includes a mapping of UI elements to normalized labels. The system uses the auto-identifier software to identify UI elements on the webpage. The system evaluates the performance of the auto-identifier software with respect to the webpage using the specification. The system displays diagnostics related to the performance of the auto-identifier software. In certain embodiments, the method is used for testing the accuracy of autofill software on a webform. In certain embodiments, the method is used for testing the accuracy of cart-scraper software on a checkout page.

One example method includes acquiring data from a knowledge base that includes message codes indicating conditions that occurred during performance of one or more test runs of a computing operation, and the message codes are included in message code sequences, processing the data by mapping the message codes, and message codes included in a customer issue log, to codes that are readable by a machine learning process, transforming the data to generate an output that comprises, for each message code sequence, relationships between each of the message codes in that message code sequence, extracting features from the transformed data, and the extracting generates multiple datasets that include the features, performing a similarity comparison by comparing a customer field issue with the datasets, and based on the similarity comparison, identifying and recommending a solution to the customer field issue.

In one embodiment, a computing system of a vehicle generates perception data based on sensor data captured by one or more sensors of the vehicle. The perception data includes one or more representations of physical objects in an environment associated with the vehicle. The computing system further determines simulated perception data that includes one or more representations of virtual objects within the environment and generates modified perception data based on the perception data and the simulated perception data. The modified perception data includes at least one of the one or more representations of physical objects and the one or more representations of virtual objects. The computing system further determines a path of travel for the vehicle based on the modified perception data, which includes the one or more representations of the virtual objects.

Certain example embodiments test an application for security vulnerabilities. Binary and/or source code representations are subjected to static testing. Static testing identifies potential security weaknesses in the application. For each potential security weakness, a corresponding dynamic test set, containing one or more test cases, is generated based on (i) the corresponding potential security weakness, and (ii) lookups to weakness, application context, and attack pattern databases. The weakness database includes different weakness types and descriptions thereof. The attack pattern database includes information about how to generate attacks for the different weakness types. An instance of the application running in a test runtime environment is dynamically tested using the dynamic test cases. The dynamic test results verify whether each potential security weakness is a real vulnerability. The dynamic test results include fewer false-positives than the raw static test results. Verified security weakness of the application are repairable automatically.

A system, method and apparatus for choosing a digital processing platform that is optimal for a specified type of application and satisfies a set of user-specified constraints is provided. In operation, all known parameters on all available processing platforms in a database are stored, providing this information to a computer software application run by the user by querying the database, and then allowing a remote user to specify the constraints, in terms of hardware and system software, to eliminate those entries that would not satisfy the constraints in a step-by-step filtering process. The user then chooses a set of application programs to run on the platforms that were not eliminated. The runtime performance parameters/characteristics—e.g. computational throughput, I/O bandwidth, environmental parameters, etc. are measured to select the optimal solution. The system and method also allows for a regression test to ensure consistency between test software processes running on discrete platforms.

Disclosed herein are techniques for visualizing and configuring controller function sequences. Techniques include identifying at least one executable code segment associated with a controller; analyzing the at least one executable code segment to determine at least one function and at least one functional relationship associated with the at least one code segment; constructing, a software functionality line-of-code behavior and relation model visually depicting the determined at least one function and at least one functional relationship; displaying the software functionality line-of-code behavior and relation model at a user interface; receiving a first input at the interface; in response to the received first input, animating the line-of-code behavior and relation model to visually depict execution of the at least one executable code segment on the controller; receiving a second input at the interface; and in response to the received second input, animating an update to the line-of-code behavior and relation model.

A method for adjusting the resource allocation ratio between microservices used to run an application. A microservice test sequence is defined which has an order that follows the traffic flow through the microservices. Each microservice is analyzed in order of the test sequence to classify whether or not it is acting as a bottleneck for the application. This is done by measuring whether or not decrementing the microservice's resource causes the application throughput to decrease. For each microservice classified as a bottleneck and in reverse order of the test sequence, its resource is successively incremented until the application throughput starts to increase, indicating it is no longer acting as a bottleneck. The resource allocation ratio can then be adjusted to reflect this procedure.