Architecture diagram analysis tool for software development

1. An architecture diagram analysis device, comprising:

a memory operable to store a machine learning model configured to classify an input as either compliant or non-compliant; and

a processor operably coupled to the memory, configured to:

obtain an architecture diagram comprising graphical representations of a plurality of features that are configured to form a workflow for a computer system, wherein the plurality of features comprises:

a plurality of components comprising software applications and hardware components; and

communication channels between the plurality of components;

identify the plurality of features within the architecture diagram;

identify metadata that is associated with the identified features, wherein:

the metadata comprise text associated with the identified features and location information for the identified features within the architecture diagram; and

the location information comprises information identifying where a feature is located within the architecture diagram;

convert the identified features into vector points based on the identified metadata, wherein:

a vector point is a point in an n-dimensional space; and

the number of dimensions in the n-dimensional space is based on the number of features in the architecture diagram;

generate a vector map, wherein the vector map is configured to associate a vector point with its corresponding metadata;

input the vector points into the machine learning model;

obtain classification results for the vector points in response to inputting the vector points into the machine learning model, wherein the classification results associate each vector point with one of a compliant classification and a non-compliant classification;

identify vector points that are associated with a non-compliant classification;

identify one or more non-compliant features corresponding with the vector points that are associated with a non-compliant classification;

identify alternative features for the one or more non-compliant features, wherein each alternative feature is one of a software application and a hardware component;

update the vector map by replacing the one or more non-compliant features with the alternative features; and

update the architecture diagram based on the updated vector map, wherein updating the architecture diagram replaces graphical representations of the one or more non-compliant features with graphical representations of the alternative features in the architecture diagram.

2. The device of claim 1 , wherein:

the processor is further configured to perform a clustering analysis comprising:

generating a plot of the vector points within the n-dimensional space;

determining a number of clusters within the plot;

determining the number of clusters exceeds a predetermined threshold value; and

determining that the vector points comprise one or more non-compliant features in response to determining that the number of clusters exceeds the predetermined threshold value; and

inputting the vector points into the machine learning model is in response to determining that the vector points comprise one or more non-compliant featured based on the clustering analysis.

3. The device of claim 1 , wherein:

the memory further comprises a feature repository that is configured to associates non-compliant features with compliant features; and

identifying the alternative features comprises identify compliant features that correspond with the one or more non-compliant features in the feature repository.

4. The device of claim 1 , wherein updating the architecture diagram comprises:

identifying location information for a non-compliant feature in the vector map;

identifying a graphical representation of the non-compliant feature in the architecture diagram based on the location information; and

replacing the graphical representation of the non-compliant feature with a graphical representation of an alternative feature in the architecture diagram.

5. The device of claim 1 , wherein identifying the one or more non-compliant features corresponding with the vector points that are associated with a non-compliant classification comprises highlighting the one or more non-compliant features in the architecture diagram.

6. The device of claim 5 , wherein highlighting the one or more non-compliant features in the architecture diagram comprises:

identifying location information for a non-compliant feature in the vector map; and

modifying the graphical representation of the non-compliant feature in the architecture diagram.

7. The device of claim 1 , wherein processor is further configured to output instructions for replacing non-compliant features with alternative features.

8. An architecture diagram analysis method for a computing system infrastructure, comprising:

obtaining an architecture diagram comprising graphical representations of a plurality of features that are configured to form a workflow for a computer system, wherein the plurality of features comprises:

a plurality of components comprising software applications and hardware components; and

communication channels between the plurality of components;

identifying the plurality of features within the architecture diagram;

identifying metadata that is associated with the identified features, wherein:

the metadata comprise text associated with the identified features and location information for the identified features within the architecture diagram; and

the location information comprises information identifying where a feature is located within the architecture diagram;

converting the identified features into vector points based on the identified metadata, wherein:

a vector point is a point in an n-dimensional space; and

the number of dimensions in the n-dimensional space is based on the number of features in the architecture diagram;

generating a vector map, wherein the vector map is configured to associate a vector point with its corresponding metadata;

inputting the vector points into a machine learning model that is configured to classify an input as either compliant or non-compliant;

obtaining classification results for the vector points in response to inputting the vector points into the machine learning model, wherein the classification results associate each vector point with one of a compliant classification and a non-compliant classification;

identifying vector points that are associated with a non-compliant classification;

identifying one or more non-compliant features corresponding with the vector points that are associated with a non-compliant classification;

identifying alternative features for the one or more non-compliant features, wherein each alternative feature is one of a software application and a hardware component;

updating the vector map by replacing the one or more non-compliant features with the alternative features; and

updating the architecture diagram based on the updated vector map, wherein updating the architecture diagram replaces graphical representations of the one or more non-compliant features with graphical representations of the alternative features in the architecture diagram.

9. The method of claim 8 , further comprising performing a clustering analysis comprising:

generating a plot of the vector points within the n-dimensional space;

determining a number of clusters within the plot;

determining the number of clusters exceeds a predetermined threshold value; and

determining that the vector points comprise one or more non-compliant features in response to determining that the number of clusters exceeds the predetermined threshold value; and

wherein inputting the vector points into the machine learning model is in response to determining that the vector points comprise one or more non-compliant featured based on the clustering analysis.

10. The method of claim 8 , further comprising identifying the alternative features comprises identify compliant features that correspond with the one or more non-compliant features in a feature repository, wherein the feature repository is configured to associate non-compliant features with compliant features.

11. The method of claim 8 , wherein updating the architecture diagram comprises:

identifying location information for a non-compliant feature in the vector map;

identifying a graphical representation of the non-compliant feature in the architecture diagram based on the location information; and

replacing the graphical representation of the non-compliant feature with a graphical representation of an alternative feature in the architecture diagram.

12. The method of claim 8 , wherein identifying the one or more non-compliant features corresponding with the vector points that are associated with a non-compliant classification comprises highlighting the one or more non-compliant features in the architecture diagram.

13. The method of claim 12 , wherein highlighting the one or more non-compliant features in the architecture diagram comprises:

identifying location information for a non-compliant feature in the vector map; and

modifying the graphical representation of the non-compliant feature in the architecture diagram.

14. The method of claim 8 , further comprising outputting instructions for replacing non-compliant features with alternative features.

15. A computer program comprising executable instructions stored in a non-transitory computer readable medium that when executed by a processor causes the processor to:

obtain an architecture diagram comprising graphical representations of a plurality of features that are configured to form a workflow for a computer system, wherein the plurality of features comprises:

a plurality of components comprising software applications and hardware components; and

communication channels between the plurality of components;

identify the plurality of features within the architecture diagram;

identify metadata that is associated with the identified features, wherein:

the metadata comprise text associated with the identified features and location information for the identified features within the architecture diagram; and

the location information comprises information identifying where a feature is located within the architecture diagram;

convert the identified features into vector points based on the identified metadata, wherein:

a vector point is a point in an n-dimensional space; and

the number of dimensions in the n-dimensional space is based on the number of features in the architecture diagram;

generate a vector map, wherein the vector map is configured to associate a vector point with its corresponding metadata;

input the vector points into a machine learning model that is configured to classify an input as either compliant or non-compliant;

obtain classification results for the vector points in response to inputting the vector points into the machine learning model, wherein the classification results associate each vector point with one of a compliant classification and a non-compliant classification;

identify vector points that are associated with a non-compliant classification;

identify one or more non-compliant features corresponding with the vector points that are associated with a non-compliant classification;

identify alternative features for the one or more non-compliant features, wherein each alternative feature is one of a software application and a hardware component;

update the vector map by replacing the one or more non-compliant features with the alternative features; and

update the architecture diagram based on the updated vector map, wherein updating the architecture diagram replaces graphical representations of the one or more non-compliant features with graphical representations of the alternative features in the architecture diagram.

16. The computer program of claim 15 , further comprising instructions that when executed by the processor causes the processor to perform a clustering analysis comprising:

generating a plot of the vector points within the n-dimensional space;

determining a number of clusters within the plot;

determining the number of clusters exceeds a predetermined threshold value; and

determining that the vector points comprise one or more non-compliant features in response to determining that the number of clusters exceeds the predetermined threshold value; and

wherein inputting the vector points into the machine learning model is in response to determining that the vector points comprise one or more non-compliant featured based on the clustering analysis.

17. The computer program of claim 15 , further comprising instructions that when executed by the processor causes the processor to identify the alternative features comprises identify compliant features that correspond with the one or more non-compliant features in a feature repository, wherein the feature repository is configured to associate non-compliant features with compliant features.

18. The computer program of claim 15 , wherein updating the architecture diagram comprises:

identifying location information for a non-compliant feature in the vector map;

identifying a graphical representation of the non-compliant feature in the architecture diagram based on the location information; and

replacing the graphical representation of the non-compliant feature with a graphical representation of an alternative feature in the architecture diagram.

19. The computer program of claim 15 , wherein:

identifying the one or more non-compliant features corresponding with the vector points that are associated with a non-compliant classification comprises highlighting the one or more non-compliant features in the architecture diagram; and

highlighting the one or more non-compliant features in the architecture diagram comprises:

identifying location information for a non-compliant feature in the vector map; and

modifying the graphical representation of the non-compliant feature in the architecture diagram.

20. The computer program of claim 15 , further comprising instructions that when executed by the processor causes the processor output instructions for replacing non-compliant features with alternative features.