Abstract: A machine may be configured to assign digital content campaign treatments to targets of a digital content campaign, at a server. For example, the machine access data pertaining to one or more characteristics of an application. The application is hosted on a client device. The data is stored at a server-side database. The machine determines that the one or more characteristics of the application match one or more targeting criteria associated with a digital content campaign. The digital content campaign includes a plurality of flights of a digital content item. The machine assigns a flight of the plurality of flights of the digital content campaign to the client device. The flight identifies a variant of the digital content item. The machine transmits a communication to the client device via a network. The communication includes a flight identifier associated with the flight.

Abstract: In some embodiments, the disclosed subject matter involves a system and method relating to segmenting user population to test the quality of different software states. In at least one embodiment, the software states may be exposed dynamically to users who receive a software build, because features may be turned on/off via the server, at runtime. In an embodiment, if the server is unavailable, feature information may be retrieved from the local client storage. New features may be dynamically exposed to segments of a population to assist in verification and test of the new features. Other embodiments are described and claimed.

Abstract: A machine may be configured to test an application based on an in-app impersonating of an audience profile. For example, the machine receives a selection of an audience identifier via a user interface. The machine maps the audience identifier to a configuration file that includes identifiers of features of the application available for use by the audience. The machine, based on the audience identifier and the configuration file, causes a display, in the user interface, of the identifiers of the features. The machine receives, via the user interface, a request to modify an operational state of a feature of the application in the software context associated with the audience. The machine configures, at run-time, the application based on the audience identifier, the configuration file, and the request to modify the operational state of the feature. The configuring results in the application including the feature in a modified operational state.

Abstract: A machine may be configured to assign digital content campaign treatments to targets of a digital content campaign, at a server. For example, the machine access data pertaining to one or more characteristics of an application. The application is hosted on a client device. The data is stored at a server-side database. The machine determines that the one or more characteristics of the application match one or more targeting criteria associated with a digital content campaign. The digital content campaign includes a plurality of flights of a digital content item. The machine assigns a flight of the plurality of flights of the digital content campaign to the client device. The flight identifies a variant of the digital content item. The machine transmits a communication to the client device via a network. The communication includes a flight identifier associated with the flight.

Abstract: In some embodiments, the disclosed subject matter involves a system and method relating to automation test runs of a software build, where the test runs effect impersonating an audience and selecting optional features to test with the impersonated audience. The impersonated audience is associated with a software build having static features, and the test runs include optional features, where each optional feature may have more than one associated treatment. Each feature/treatment combination may be tested in a test scenario associated with the feature/treatment combination. New features may be dynamically exposed to a selected audience to assist in verification and test of the new features. Other embodiments are described and claimed.

Abstract: In some embodiments, the disclosed subject matter involves a system and method relating to automation test runs of a software build, where the test runs effect impersonating an automation audience and selecting optional features to test with the automation audience. The automation audience is associated with a software build having static features, and the test runs include optional features, where each optional feature may have more than one associated treatment. Each feature/treatment combination may be tested in a test scenario associated with the feature/treatment combination. New features that are exposed in other audiences may be enabled for the automation audience to assist in verification and test of the new features. Dynamic selection of the optional features is effected using an application program interface (API) in the source code for the features, where the API accesses feature configuration information during runtime. Other embodiments are described and claimed.

Abstract: Systems and methods for controlling use of a software feature. One system includes a client device having an interface for receiving a feature control filter associated with the software feature from an external source and an electronic processor. The electronic processor executes the feature control filter to detect a current value of at least one run-time parameter of the client device, and compares the current value of the at least one run-time parameter of the client device to a predetermined value of the at least one run-time parameter defined for the software feature. When the current value satisfies the predetermined value, the electronic processor enables the software feature on the client device. When the current value does not satisfy the predetermined value, the electronic processor does not enable the software feature on the client device.

Abstract: Architecture that uses content from off-page data sources such as feeds (e.g., yellow pages, coupons, social networks, commerce, etc.) to present additional, relevant information in association with search results. The additional and relevant information is directly relevant to the implicit task the user is trying to accomplish. The architecture includes online and offline mechanisms that identify an entity represented on a web page and look-up information related to that entity in disparate data sources. Relevance heuristics are employed to determine which of the available entity data to show in the caption given the user query, the web page, and the underlying user task (other known information about the user such as geographic location).

Abstract: Architecture that inserts one or more label items in search result entries. In addition to the typical search result caption (title, snippet, and link), the architecture includes the label component of one or more of the label items in the result entry. The number and type of label annotations are based on the query. When a particular label item is selected (e.g., hover, mouse click), a presentation component (e.g., expansion object, pop-up window) launches proximate to a label item in response to interaction with the label item and presents additional information from the target webpage. The additional information can include an action and data related to the search result entry and the target webpage. The data can be obtained from a data source other than the target webpage.

Abstract: Architecture that computes a dominant image from one or more images on a webpage. A dominant image classifier scans webpages in an offline-created index to identify the prominent images in the webpages. In a more specific implementation the image selected is the image associated with a name query. Face detection technology can be utilized to identify which of the images on a given webpage contain faces. A query classifier identifies queries that contain people names. In the context of search engines and search result pages, the web results for name queries can further include prominent people face images as thumbnail images. Additional facts (structured data) can further be included that together with the results elements of caption title, snippet and attribute (uniform resource locator (URL)) provide an improved summary of the person on the page.

Abstract: Architecture that provides a preview template of information supplemented to a result entry of a results page, such as a search engine results page. A data component supplements the result entry with information from a results destination document. The information is segmented and grouped into logical collections of related results according to contextual templates. Each template is associated with an interactive label that exposes a corresponding collection in response to label interaction. Each label of a template is a visual cue that includes a descriptive title which relates to individual web results of the collection. A user can scan through the labels and interact only with the section (collection) of interest to obtain a preview of destination document results before committing a click that navigates the user to the destination document of the website. The labels are also ranked within the template of information for a given result entry.

Abstract: Architecture that uses content from off-page data sources such as feeds (e.g., yellow pages, coupons, social networks, commerce, etc.) to present additional, relevant information in association with search results. The additional and relevant information is directly relevant to the implicit task the user is trying to accomplish. The architecture includes online and offline mechanisms that identify an entity represented on a web page and look-up information related to that entity in disparate data sources. Relevance heuristics are employed to determine which of the available entity data to show in the caption given the user query, the web page, and the underlying user task (other known information about the user such as geographic location).

Abstract: Architecture that inserts one or more label items in search result entries. In addition to the typical search result caption (title, snippet, and link), the architecture includes the label component of one or more of the label items in the result entry. The number and type of label annotations are based on the query. When a particular label item is selected (e.g., hover, mouse click), a presentation component (e.g., expansion object, pop-up window) launches proximate to a label item in response to interaction with the label item and presents additional information from the target webpage. The additional information can include an action and data related to the search result entry and the target webpage. The data can be obtained from a data source other than the target webpage.