Systems for web page or web application instrumentation. Embodiments commence upon identification of a computer-readable user interface description comprising at least some markup language conforming to a respective document object model that is codified in a computer-readable language. An injector process modifies the user interface description by inserting markup text and code into the user interface description, where the inserted code includes instrumentation code to invoke dynamic generation of instrumentation locator IDs using the hierarchical elements found in the document object model. The modified computer-readable interface description is transmitted to a user device. Log messages are emitted upon user actions taken while using the user device. The log messages comprise the instrumentation locator IDs that are formed using hierarchical elements found in the document object model.

Provided is a method for generating web codes for a user interface (UI) based on a generative adversarial network (GAN) and a convolutional neural network (CNN). The method includes steps described below. A mapping relationship between display effects of a HyperText Markup Language (HTML) element and source codes of the HTML element is constructed. A location of an HTML element in an image I is recognized. Complete HTML codes of the image I are generated. The similarity between manually-written HTML codes and the generated complete HTML codes and the similarity between the image I and an image I.sub.1 generated by the generated complete HTML codes are obtained. After training, an image-to-HTML-code generation model M is obtained. A to-be-processed UI image is input into the model M so as to obtain corresponding HTML codes. According to the method of the present disclosure, an image-to-HTML-code generation model M can be obtained.

Provided is a method for generating web codes for a user interface (UI) based on a generative adversarial network (GAN) and a convolutional neural network (CNN). The method includes steps described below. A mapping relationship between display effects of a HyperText Markup Language (HTML) element and source codes of the HTML element is constructed. A location of an HTML element in an image I is recognized. Complete HTML codes of the image I are generated. The similarity between manually-written HTML codes and the generated complete HTML codes and the similarity between the image I and an image I.sub.1 generated by the generated complete HTML codes are obtained. After training, an image-to-HTML-code generation model M is obtained. A to-be-processed UI image is input into the model M so as to obtain corresponding HTML codes. According to the method of the present disclosure, an image-to-HTML-code generation model M can be obtained.

A system includes a content extraction engine comprising at least one processor and configured to receive a content page for a target product including product data for the target product and noise content unrelated to the target product, identify noise content pertaining to data unrelated to the target product, remove noise content from the content page, thereby generating a remainder content page containing target product data usable to enable product comparison between multiple sources.

A system includes a content extraction engine comprising at least one processor and configured to receive a content page for a target product including product data for the target product and noise content unrelated to the target product, identify noise content pertaining to data unrelated to the target product, remove noise content from the content page, thereby generating a remainder content page containing target product data usable to enable product comparison between multiple sources.

Structured documents are processed using convolutional neural networks. For example, the processing can include receiving a rendered form of a structured document; mapping a grid of cells to the rendered form; assigning a respective numeric embedding to each cell in the grid, comprising, for each cell: identifying content in the structured document that corresponds to a portion of the rendered form that is mapped to the cell, mapping the identified content to a numeric embedding for the identified content, and assigning the numeric embedding for the identified content to the cell; generating a matrix representation of the structured document from the numeric embeddings assigned to the cells of the grids; and generating neural network features of the structured document by processing the matrix representation of the structured document through a subnetwork comprising one or more convolutional neural network layers.

Method and system to automatically verify the layout of a webpage on various screen sizes. The method includes retrieving screen size data associated with the webpage. The method further includes determining anchor points for the webpage based on the retrieved screen size data. The method also includes rendering the plurality of elements of the webpage based on the retrieved screen size data and the determined anchor points and identifying a respective location of each of the plurality of elements of the webpage to verify the layout of the plurality of elements of the webpage. If an error in the layout of the webpage is detected, a visual and/or audio alert is sent.

Markup language documents including server side scripting code using PHP syntax are executed efficiently in response to requests received by a server. The processing of the markup language document results in generation of a transformed markup language document that is returned in response to the request. The server side script code is input to a compiler that generates C++ code (or code in any object-based language based on C language) implementing the functionality of the server side script code. The C++ code is compiled to generated object code which is executed in order to process the markup language document. The generated C++ code includes functionality supported by PHP language including redeclaration of functions, dynamic variables, global variables, and the like. The generated C++ code invokes memory allocation code that allocates and deallocates objects obtained by instantiating the generated C++ classes.

A proxy server receives from a client device a request for a network resource that is hosted at an origin server for a domain. The request is received at the proxy server as a result of a DNS request for the domain resolving to the proxy server. The origin server is one of multiple origin servers that belong to different domains that resolve to the proxy server and are owned by different entities. The proxy server retrieves the requested network resource. The proxy server determines that the requested resource is an HTML page. The proxy server scans the HTML page to locate one or more modification tokens that each indicates content that is subject to being modified. For at least one of the located modification tokens, the proxy server automatically modifies at least a portion of the content of the HTML page that corresponds to that modification token. The proxy server then transmits the modified HTML page to the client device.

A computing device translates each of a group of structured language graphical process flow element representations, that each represents within a structured language one node of a captured graphical process flow diagram of a first business process, into one of a group of numerical strings that each represents within a set of data fields the respective node and connections to and from the respective node. The group of numerical strings is sequenced in accordance with values of the respective data fields within each numerical string that represents the respective node and the connections to and from each represented node of the captured graphical process flow diagram of the first business process.