Detection, presentation, and resolution of bottlenecks in monolith decomposition

    公开(公告)号:US11853753B1

    公开(公告)日:2023-12-26

    申请号:US17409448

    申请日:2021-08-23

    CPC classification number: G06F8/77 G06F8/433 G06F11/34 G06F8/72

    Abstract: Techniques are described for identifying resource bottlenecks in decomposing monolithic software applications as part of software modernization processes. An application modernization system constructs a graph model of a software application based on an analysis of application artifacts associated with the software application. The graph model includes nodes representing independent application components, and further includes edges representing identified dependency relationships among the application components. An application modernization system further generates application profile metrics associated with the identified dependencies, and weights derived from the metrics are applied to the nodes and/or the edges of the graph model to generate a weighted graph model that identifies the resource bottlenecks among the application components and the identified dependency relationships. The weighted graph model is transmitted to a computing device for display to a user.

    Machine learning-based identification of monolithic software application

    公开(公告)号:US11620128B1

    公开(公告)日:2023-04-04

    申请号:US17409383

    申请日:2021-08-23

    Abstract: Techniques are described for automatically identifying monolithic software applications in users' computing environments for software modernization purposes. A monolithic patent application typically refers to a single-tiered application with self-contained functionality designed largely without modularity, although many types of applications can have monolithic characteristics. In many cases, modularity in a software application's design is desirable and thus developers may often seek to decompose monolithic applications into more modular “microservices” or other subunits when possible. A software modernization system includes a software analysis service that obtains, for one or more software applications undergoing evaluation, a collection of application artifacts, application profiling metrics, and other application profile data. A collection of features is extracted from the application artifacts and metrics and used as input to a ML model trained to determine whether a software application likely is monolithic.

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