A control method of a server is provided. The method includes acquiring code information about a program, identifying at least one error with respect to a code style included in the code information based on a predetermined code style rule, acquiring at least one error information with respect to the identified code style, and modifying the code style by inputting the code information and the error information to an artificial intelligence model in which the code style rule is trained.

In one embodiment, a device launches a core agent for a Java application. The core agent loads a first tenant and a second tenant, each tenant having its own isolated class loader. The device instruments, via the core agent and by each tenant, the Java application to capture data regarding execution of the Java application. The device provides the captured data to a user interface.

A shift-left analysis system receives information regarding an application implemented by one or more microservices. The system determines a microservice performance metric based on a performance prediction model for each microservice of the application. The system outputs an application performance metric for the application based on the microservice performance metrics determined for the one or more microservices of application.

A method for detecting outlier behavior in a set of executions of one or several applications on an information processing device, implemented by a computer and comprising steps of triggering (S1) said set of executions in collaboration with a profiling tool in order to collect, for each execution, at least one time series of measurement points assigning, for each measurement point, a value to a measured parameter; automatically formatting (S2) the time series obtained for said set, by adjusting, for each time series, its length, its values, and its number of measurement points; calculating (S3) a metric between two time series among the time series collected for said set of executions; detecting (S4) an outlier based on said distance.

Systems and methods for automatically recovering from software regression in a cloud computing environment. One example method includes determining, with an electronic processor, that a batch software update has been applied to the cloud computing environment. The method includes, in response to determining that a batch software update has been applied, transmitting a problem request to an event listener server. The method includes receiving, from the event listener server, a problem statement including a stack trace. The method includes determining, based on the stack trace, a software feature indicator. The method includes transmitting the software feature indicator to a root cause analyzer. The method includes receiving, from the root cause analyzer, a change list indicator and a relevancy score associated with the change list indicator. The method includes performing a mitigation action based on the change list indicator when the relevancy score exceeds a relevancy threshold.

A system for auditing an enterprise IT environment includes a multi-tier hierarchy generator configured to generate a multi-tier hierarchy, between and within each tier, maps the IT environment across a plurality of software applications of different types via which the IT environment is implemented. The system includes a test generation engine configured to generate test cases based on intake data about the IT environment. The system includes an auditing unit configured to test the IT environment based on the test cases and the multi-tier hierarchy.

Embodiments disclosed herein relate to methods, systems, and computer programs for verifying that data incorporated into a computer program is current. The methods, systems, and computer programs compare a source identifier status code associated with the data to a current source identifier status code at the location where the data was obtained. The methods, systems, and computer programs include at least one validation function which determines the validity of the data according to selected parameters. If the source identifier status code and current source identifier status code match and the at least one validation function determines the data is valid, an executable computer program incorporating the data and one or more functions is produced as output.

A method includes collecting code development data associated with development of code files on a per user basis. A predicted code execution performance score is determined based on weighting one or more developer attributes captured in the code development data on the per user basis, the predicted code execution performance score indicative of a predicted likelihood of a code quality issue, where the one or more developer attributes are based at least in part on personnel data including a level of developer experience and a history of observed development activities of an associated developer from review data. One or more resources of one or more code execution servers associated with execution of selected files are predictively allocated based on the predicted code execution performance score. One or more code execution metrics are captured associated with executing selected files on the one or more code execution servers.

Systems, methods, and software can be used to detect software errors in a binary code. In some aspects, a method comprises: obtaining a binary code; generating a base memory-write profile for the binary code, wherein the base memory-write profile comprises a count of memory updates for each of a plurality of memory locations during an execution of the binary code according to a base input; for each of a plurality of test inputs, generating a test memory-write profile for the binary code, wherein the test memory-write profile comprises a count of memory updates for each memory location during an execution of the binary code according to the test input; comparing the base memory-write profile and the plurality of test memory-write profiles; and generating a notification based on the comparison, wherein the notification indicates whether there is a difference between the base memory-write profile and the plurality of test memory-write profiles.

A computer-implemented method, computer program product and computing system for identifying one or more portions of a website structure of a specific website; and associating the one or more portions of the website structure with one or more descriptors of the specific website to define a specific data description model corresponding to the specific website.