Abstract: Embodiments of the present invention provide systems and methods for performing data analysis. Mapping analytics are applied on data which contains extensive information. Mapped building blocks are found by applying mapping analytics. These mapped building blocks help determine which functions are in use within a system. By determining which functions are in use within a system, a higher-level of functionality in use can also be determined.

Abstract: Embodiments of the present invention provide systems and methods for performing data analysis. Mapping analytics are applied on data which contains extensive information. Mapped building blocks are found by applying mapping analytics. These mapped building blocks help determine which functions are in use within a system. By determining which functions are in use within a system, a higher-level of functionality in use can also be determined.

Abstract: Embodiments include method, systems and computer program products for identifying dependency relationships in a software product. Aspects include obtaining change history data for the software product and extracting a plurality of change elements from the change history data, each change element including an identifier of a code segment that was changed and a timestamp of the change. Aspects also include creating a dependency graph based on the plurality of change elements, wherein the dependency graph includes nodes that correspond to the code segments and edges that connect nodes that were both updated in a same logical grouping, calculating a weight for each of the edges based on probability that the nodes connected by the edge will be updated together, and outputting the dependency graph.

Abstract: Embodiments of the present invention provide systems and methods for performing data analysis. Mapping analytics are applied on data which contains extensive information. Mapped building blocks are found by applying mapping analytics. These mapped building blocks help determine which functions are in use within a system. By determining which functions are in use within a system, a higher-level of functionality in use can also be determined.

Abstract: Embodiments of the present invention provide systems and methods for performing data analysis. Mapping analytics are applied on data which contains extensive information. Mapped building blocks are found by applying mapping analytics. These mapped building blocks help determine which functions are in use within a system. By determining which functions are in use within a system, a higher-level of functionality in use can also be determined.

Abstract: A system and method that provides an automated solution to obtaining quality scheduling for users of computing resources. The system, implemented in an enterprise software test center, collects information from test-shop personnel about test machine features and availability, test jobs, and tester preferences and constraints. The system reformulates this testing information as a system of constraints. An optimizing scheduling engine computes efficient schedules whereby all the jobs are feasibly scheduled while satisfying the users' time preferences to the greatest extent possible. The method and system achieves fairness: if all preferences can not be meet, it is attempted to evenly distribute violations of preferences across the users. The test scheduling is generated according to a first application of a greedy algorithm that finds an initial feasible assignment of jobs. The second is a local search algorithm that improves the initial greedy solution.

Abstract: A system and method that provides an automated solution to obtaining quality scheduling for users of computing resources. The system, implemented in an enterprise software test center, collects information from test-shop personnel about test machine features and availability, test jobs, and tester preferences and constraints. The system reformulates this testing information as a system of constraints. An optimizing scheduling engine computes efficient schedules whereby all the jobs are feasibly scheduled while satisfying the users' time preferences to the greatest extent possible. The method and system achieves fairness: if all preferences can not be meet, it is attempted to evenly distribute violations of preferences across the users. The test scheduling is generated according to a first application of a greedy algorithm that finds an initial feasible assignment of jobs. The second is a local search algorithm that improves the initial greedy solution.