Aspects of the disclosure relate to monitoring and detecting security vulnerabilities in software code to be executed in a continuous integration and continuous delivery (CI/CD) environment. A computing platform may receive, via the communication interface, an indication of a user request to deploy a code in a CI/CD environment, in which the user request includes user account information and the code. The computing platform may then analyze the code to identify a presence of one or more potential vulnerabilities in the code, including executing a security process on the code. Based on identifying one or more potential vulnerabilities, the computing platform may thereafter determine an alert action and send, via the communication interface, to the developer computing platform, the alert action.

Techniques for multi-representational learning models for static analysis of source code are disclosed. In some embodiments, a system/process/computer program product for multi-representational learning models for static analysis of source code includes storing on a networked device a set comprising one or more multi-representation learning (MRL) models for static analysis of source code; performing a static analysis of source code associated with a sample received at the network device, wherein performing the static analysis includes using at least one stored MRL model; and determining that the sample is malicious based at least in part on the static analysis of the source code associated with the received sample, and in response to determining that the sample is malicious, performing an action based on a security policy.

Techniques for multi-representational learning models for static analysis of source code are disclosed. In some embodiments, a system/process/computer program product for multi-representational learning models for static analysis of source code includes storing on a networked device a set comprising one or more multi-representation learning (MRL) models for static analysis of source code; performing a static analysis of source code associated with a sample received at the network device, wherein performing the static analysis includes using at least one stored MRL model; and determining that the sample is malicious based at least in part on the static analysis of the source code associated with the received sample, and in response to determining that the sample is malicious, performing an action based on a security policy.

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.

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.

Provided is a system that enables a user to easily analyze software. A software analysis support system 1 that supports analysis of a structure of software includes at least one computer. The computer acquires software component information 12 indicating a relationship and an attribute of each software component, acquires display element setting information 13 in which a first display element 41 corresponding to an attribute of each software component and a second display element 42 corresponding to a relationship between the respective software components are set, the display element setting information 13 being editable, and arranges each of the first display element and the second display element at a predetermined position of a virtual space 40 that displays the relationship of each software component on the basis of a plurality of predetermined attributes selected in advance among attributes of each software component.

Provided is a system that enables a user to easily analyze software. A software analysis support system 1 that supports analysis of a structure of software includes at least one computer. The computer acquires software component information 12 indicating a relationship and an attribute of each software component, acquires display element setting information 13 in which a first display element 41 corresponding to an attribute of each software component and a second display element 42 corresponding to a relationship between the respective software components are set, the display element setting information 13 being editable, and arranges each of the first display element and the second display element at a predetermined position of a virtual space 40 that displays the relationship of each software component on the basis of a plurality of predetermined attributes selected in advance among attributes of each software component.

Automatically generating code and related artifacts such as application programming interfaces (APIs) and related documentation from an abstract model of a database. The abstract model is derived from a physical model which may be a source such as a legacy database, an entity relationship diagram, or other schema defining the data tables, objects, entities, or relationships etc. of the source. The generated code may be exposed (that is, made visible to the developer in its pre-compiled state) and further configurable and extendable. Any such extended code is maintained separately from generated code. An API and related documentation are also generated from the same abstract model.

Automatically generating code and related artifacts such as application programming interfaces (APIs) and related documentation from an abstract model of a database. The abstract model is derived from a physical model which may be a source such as a legacy database, an entity relationship diagram, or other schema defining the data tables, objects, entities, or relationships etc. of the source. The generated code may be exposed (that is, made visible to the developer in its pre-compiled state) and further configurable and extendable. Any such extended code is maintained separately from generated code. An API and related documentation are also generated from the same abstract model.

A machine learning (ML) based code transformation system that transforms a source programming code developed using a source library for execution on a source platform into remediated code for execution on a target platform is disclosed. Metadata extracted from the source programming code is used to detect the source programming language, source libraries, and the source platform. The metadata also enables modularizing the source programming code based on the functionality and identifying a node from a plurality of nodes in a communication network to execute the various source code modules. A similarity map is generated mapping the source libraries to the target libraries and the source code modules that are incompatible with the target platform are identified and remediated with similar target code modules using the similarity map.