Abstract: A system for assisting users in listing items for sale in an electronic marketplace is disclosed. A video is received from a user device associated with a user, the video including a video stream depicting a plurality of items to be listed for sale in the electronic marketplace. Respective images depicting respective items among the plurality of items are obtained from the video stream, and respective attributes of the respective items among the plurality of items are extracted from the video. Respective listings for sale of the respective items are generated based at least in part on the respective attributes of the respective items among the plurality of items, and the respective listings for sale of the respective items are displayed to the user.

Abstract: A first test case identifier that indicates a first test case is received. The first test case is indicative of testing one or more features of an application associated with the electronic marketplace. The first test case identifier is compared to a plurality of attributes. The plurality of attributes are associated with one or more listings that describe one or more items for sale in an electronic marketplace. Based at least in part on the comparing, it is determined that a first set of attributes, of the plurality of attributes, are test data candidates to be used as input to the first test case. Based at least in part on the determining, the first test case is caused to be run using at least one of the first set of attributes as test data for input.

Abstract: Various methods and systems for providing predicted item conditions for items in an item listing system. A predicted item condition may indicate a calculated estimate of a descriptive state of the item based on item transaction features. Operationally, item transaction features of an item—associated with an item listing interface—are accessed at the item listing system. The item transaction features are communicated to an item condition prediction machine learning model of the item listing system. The item condition machine learning model is trained on historical item transactions comprising item condition features of historical item transactions, the historical item transactions are previous item transactions associated with the item listing system. Based on the item transaction features of the item, the item condition machine learning model is caused to generate a predicted item condition. The predicted item condition is communicated as a recommended item condition or required item condition.

Abstract: Various methods and systems for providing customized-item-specific interfaces of items on extended interface devices in a search system. A plurality of items—each having an extended interface configuration that indicates customized-item-specific interface instructions for extended presentation of items on extended interface devices—are accessed at a primary interface device associated with a set of extended interface devices including a second-user extended interface device that supports a social mode. Based on the first extended interface configuration of a first item, a determination is made that the first item is extendable to a first extended interface device in the set of extended interface devices. Based on determining that the first item is extendable, automatically causing generation of a customized-item-specific interface of the first item on the first extended interface device.

Abstract: A first test case identifier that indicates a first test case is received. The first test case is indicative of testing one or more features of an application associated with the electronic marketplace. The first test case identifier is compared to a plurality of attributes. The plurality of attributes are associated with one or more listings that describe one or more items for sale in an electronic marketplace. Based at least in part on the comparing, it is determined that a first set of attributes, of the plurality of attributes, are test data candidates to be used as input to the first test case. Based at least in part on the determining, the first test case is caused to be run using at least one of the first set of attributes as test data for input.

Abstract: Automated determination of web page rendering performance techniques and systems are described. In one example, a web page including multiple user-interactive elements that can be selected or navigated via user input is received. A frame rendering system is employed to automatically identify the user-interactive elements and generate scripts to simulate user input at the user-interactive elements. The generated scripts are then used to simulate user input at the user-interactive elements. The scripts may be generated to simulate user interaction via a variety of different device types, and under a variety of different network conditions. During the simulation, a rendering performance score for the web page is generated by monitoring rendering performance relative to a threshold rendering rate.

Abstract: Automated determination of web page rendering performance techniques and systems are described. In one example, a web page including multiple user-interactive elements that can be selected or navigated via user input is received. A frame rendering system is employed to automatically identify the user-interactive elements and generate scripts to simulate user input at the user-interactive elements. The generated scripts are then used to simulate user input at the user-interactive elements. The scripts may be generated to simulate user interaction via a variety of different device types, and under a variety of different network conditions. During the simulation, a rendering performance score for the web page is generated by monitoring rendering performance relative to a threshold rendering rate.

Abstract: Automated determination of web page rendering performance techniques and systems are described. In one example, a web page including multiple user-interactive elements that can be selected or navigated via user input is received. A frame rendering system is employed to automatically identify the user-interactive elements and generate scripts to simulate user input at the user-interactive elements. The generated scripts are then used to simulate user input at the user-interactive elements. The scripts may be generated to simulate user interaction via a variety of different device types, and under a variety of different network conditions. During the simulation, a rendering performance score for the web page is generated by monitoring rendering performance relative to a threshold rendering rate.

Abstract: Methods, systems, and computer programs are presented for optimizing data delivery for a User Interface (UI) to a plurality of device types. One method includes operations for receiving at a server a request for an update to the UI of a client device, and for determining, by an orchestrator at the server, modules associated with the request, each module being presented on the UI. Further, the method includes operations for invoking domain services based on the determined modules (each domain service returning respective domain service data), and for synthesizing the domain service data to generate module data for each module. The synthesizing includes selecting from the domain service data and combining the selected domain service data to generate the module data. The module data is transmitted to the client device for all the modules, the module data being processable by the client device to update modules of the UI.

Abstract: Methods, systems, and computer programs are presented for optimizing data delivery for a User Interface (UI) to a plurality of device types. One method includes operations for receiving at a server a request for an update to the UI of a client device, and for determining, by an orchestrator at the server, modules associated with the request, each module being presented on the UI. Further, the method includes operations for invoking domain services based on the determined modules (each domain service returning respective domain service data), and for synthesizing the domain service data to generate module data for each module. The synthesizing includes selecting from the domain service data and combining the selected domain service data to generate the module data. The module data is transmitted to the client device for all the modules, the module data being processable by the client device to update modules of the UI.