Abstract: Various implementations provide an approach to control testing frequency based on behavior change detection. Behavior change detection is utilized, instead of a pre-defined patterns approach, to look at a system's behavior and detect any variances from what would otherwise be normal operating behavior. In at least some implementations, a behavior change detection system collects behavior from a service, such as an online service, and detects behavior changes, either permanent or transient, in the service. In this way, the changes may be used to compute a volatility score, which the system uses to control testing frequency of one or more services, such as URLs that are part of a particular service.

Abstract: Template identification techniques for control of testing are described. In one or more implementations, a method is described to control testing of one or more services by one or more computing devices using inferred template identification. Templates are inferred, by the one or more computing devices, that are likely used for documents for respective services of a service provider that are available via corresponding universal resource locators (URLs) to form an inferred dataset. Overlaps are identified by the one or computing devices in the inferred dataset to cluster services together that have likely used corresponding templates. Testing is controlled by the one or more computing devices of the one or more services based at least in part on the clusters.

Abstract: Template identification techniques for control of testing are described. In one or more implementations, a method is described to control testing of one or more services by one or more computing devices using inferred template identification. Templates are inferred, by the one or more computing devices, that are likely used for documents for respective services of a service provider that are available via corresponding universal resource locators (URLs) to form an inferred dataset. Overlaps are identified by the one or computing devices in the inferred dataset to cluster services together that have likely used corresponding templates. Testing is controlled by the one or more computing devices of the one or more services based at least in part on the clusters.

Abstract: Various implementations provide an approach to control of testing frequency based on the concept of behavior change detection or “volatility.” Behavior change detection is utilized, in place of a pre-defined patterns approach, to look at a system's behavior and detect any variances from what would otherwise be normal operating behavior. In at least some implementations, a behavior change detection system collects behavior from a service, such as an online service, and detects behavior changes, either permanent or transient, in the service. In this way, the changes may be used to compute a volatility score that describes an amount of change in the behaviors. The changes in behavior as reflected by the volatility scores are then usable to control a testing frequency of the services, such as URLs that are part of the service. This may be performed dynamically to reflect ongoing changes in volatility.

Abstract: An interface for designing a report is provided, with elements of the report being disposed at intersections of report positions and configured to present information derived from data in a database. A formula is associated with one or more selected elements in the report to generate a result for the one or more selected elements. A tuple identifying the report positions of the one or more selected elements is associated and stored with the formula in a formula list associated with the report. The report is configured so that, when the report is generated, it is determined whether identifying report positions corresponding to an identified element in the report correspond with the tuple in the formula list. Upon determining the tuple corresponds with the identifying report positions for the identified element, the formula associated with the tuple is applied to generate the result.

Abstract: System and method involving the manipulation of information that may be embedded in content in a variety of ways and for a number of reasons, such as for protecting the embedded content from unauthorized users. Additionally, application program interfaces are disclosed that expose one or more methods relating to encoding data before the data is embedded in some content. Conversely, a method for decoding data that has been embedded in content is provided.

Abstract: A computer-implemented method includes selecting a first member of a first dimension of a database to be included at a first location in a report. The first member includes first data to be retrieved from the database. A blank space to be added at a second location relative to the first member is also selected. A database query is generated that includes a first instruction to retrieve first data included in the first member. The first instruction is located at a first position in the query and the first data will appear in the report at the first location. The database query also includes a second instruction to retrieve a null set of data. The second instruction is located at a second position in the query and the null set of data will appear in the report at the second location.

Abstract: A computer-implemented method includes selecting a first member of a first dimension of a database to be included at a first location in a report. The first member includes first data to be retrieved from the database. A blank space to be added at a second location relative to the first member is also selected. A database query is generated that includes a first instruction to retrieve first data included in the first member. The first instruction is located at a first position in the query and the first data will appear in the report at the first location. The database query also includes a second instruction to retrieve a null set of data.