Architecture discovery
AssigneeOutSystems—Software em Rede, S.A.
InventorsHugo Miguel Ferrão Casal Da Veiga, António Manuel de Carvalho dos Santos Alegria, Rui Valdemar Pereira Madaleno
Life of this patent
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Abstract
A program code component module implementing a portion of a program application is received. A trained machine learning model is used to automatically predict to which one among a plurality of program architecture layer classifications the program code component module belongs. An automatic analysis option is selected based on the predicted program architecture layer classification for the program code component module. The selected automatic analysis option is performed on the program code component module.
Claims20
A method, comprising:
- training a machine learning model to predict to which one among a plurality of program architecture layer classifications a program code component module belongs, including by:
- forming a training dataset, wherein:
- the training dataset includes a training program code component module and an assignment of the training program code component module to a program architecture layer classification included in the plurality of program architecture layer classifications;
- the program architecture layer classification included in the plurality of program architecture layer classifications identifies a functionality within a computer program that the training program code component module performs;
- the plurality of program architecture layer classifications includes a first layer corresponding to a first architectural role and a second layer corresponding to a second architectural role;
- and
- using the training dataset to train the machine learning model;
- and
- outputting the trained machine learning model.
A system, comprising:
- one or more processors configured to:
- train a machine learning model to predict to which one among a plurality of program architecture layer classifications a program code component module belongs, including by:
- forming a training dataset, wherein:
- the training dataset includes a training program code component module and an assignment of the training program code component module to a program architecture layer classification included in the plurality of program architecture layer classifications;
- the program architecture layer classification included in the plurality of program architecture layer classifications identifies a functionality within a computer program that the training program code component module performs;
- the plurality of program architecture layer classifications includes a first layer corresponding to a first architectural role and a second layer corresponding to a second architectural role;
- and
- using the training dataset to train the machine learning model;
- and
- output the trained machine learning model and
- a memory coupled to at least one of the one or more processors and configured to provide at least one of the one or more processors with instructions.
A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for:
- training a machine learning model to predict to which one among a plurality of program architecture layer classifications a program code component module belongs, including by:
- forming a training dataset, wherein:
- the training dataset includes a training program code component module and an assignment of the training program code component module to a program architecture layer classification included in the plurality of program architecture layer classifications;
- the program architecture layer classification included in the plurality of program architecture layer classifications identifies a functionality within a computer program that the training program code component module performs;
- the plurality of program architecture layer classifications includes a first layer corresponding to a first architectural role and a second layer corresponding to a second architectural role;
- and
- using the training dataset to train the machine learning model;
- and
- outputting the trained machine learning model.
Dependencies are inferred from the claim text.