Systems and methods for finding project-related information by clustering applications into related concept categories

• US 9,804,838 B2
• Filed: 12/30/2015
• Issued: 10/31/2017
• Est. Priority Date: 09/29/2011
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Grechanik, Mark
• Primary Examiner(s): Dao, Thuy
• Assistant Examiner(s): Rivera, Anibal
• Application Number:
  US14/985,060
• Publication Number:
  US 20160110186A1
• Time in Patent Office: 671 Days
• CPC Class Codes: G06F 11/3419 by assessing time
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 40/194 Calculation of difference between files
• CPC Class Codes: G06F 8/70 Software maintenance or management
• CPC Class Codes: G06F 8/71 Version control security arrangements therefor G06F21/57; Configuration management

Abstract

A system, method, and computer-readable medium, is described that finds similarities among programming applications based on semantic anchors found within the source code of such applications. The semantic anchors may be API calls, such as Java'"'"'s package and class calls of the JDK. Latent Semantic Indexing may be used to process the application and semantic anchor data and automatically develop a similarity matrix that contains numbers representing the similarity of one program to another.

Claims

1. A device comprising:

a processor, at least partially implemented in hardware, to;

generate a similarity matrix defining a similarity between a plurality of computer applications according to a categorization of application programming interface (API) calls,

the similarity matrix being generated from a term document matrix using singular value decomposition,

the term document matrix including a first dimension of first entries corresponding to the plurality of computer applications and a second dimension of second entries corresponding to categories of the categorization,

elements of the term document matrix having values based on a quantity of API calls in a computer application corresponding to a first entry of the first dimension, and in a category, of the categories, corresponding to a second entry of the second dimension, and

at least one of the API calls corresponding to one of the categories,

the similarity being based on weights for the API calls contained in the plurality of computer applications,

a respective weight for a respective API call in a respective computer application being based on a quantity of API calls in the respective computer application and a quantity of computer applications, of the plurality of computer applications, that contain the respective API call;

receive a selection of a first computer application of the plurality of computer applications; and

provide an indication of at least one second computer application, of the plurality of computer applications, using the similarity matrix and based on the selection of the first computer application.

2. The device of claim 1, where the similarity matrix defines the similarity between the plurality of computer applications as numerical values based on the API calls in source code of the plurality of computer applications.

3. The device of claim 1, where the processor, when generating the similarity matrix, is to:

generate the similarity matrix from a plurality of vectors corresponding to the plurality of computer applications using a vector space model,

the plurality of vectors including elements corresponding to the categories of the categorization,

the elements including values based on a number of the API calls in source code and documentation for a computer application corresponding to a vector and in the category corresponding to one of the elements,

at least one of the API calls corresponding to one of the categories.

4. The device of claim 1, where the processor is further to:

receive the plurality of computer applications from a computer application archive via a network.

5. The device of claim 1, where the processor, when generating the similarity matrix, is to:

utilize an inverse document frequency calculation to find common API calls; and

filter out the common API calls from the API calls prior to the categorization of the API calls.

6. The device of claim 1, where the first dimension of first entries are columns and the second dimension of second entries are rows.

7. The device of claim 1, where different API calls have different weights.

8. A non-transitory computer-readable medium for storing instructions, the instructions comprising:

a plurality of instructions which, when executed by one or more processors, cause the one or more processors to;

generate a similarity matrix defining a similarity between a plurality of computer applications according to a categorization of application programming interface (API) calls,

the similarity matrix being generated from a term document matrix using singular value decomposition,

the term document matrix including a first dimension of first entries corresponding to the plurality of computer applications and a second dimension of second entries corresponding to categories of the categorization,

elements of the term document matrix having values based on a quantity of API calls in a computer application corresponding to a first entry of the first dimension, and in a category, of the categories, corresponding to a second entry of the second dimension, and

at least one of the API calls corresponding to one of the categories,

the similarity being based on weights for the API calls contained in the plurality of computer applications,

a respective weight for a respective API call in a respective computer application being based on a quantity of API calls in the respective computer application and a quantity of computer applications, of the plurality of computer applications, that contain the respective API call;

receive a selection of a first computer application of the plurality of computer applications; and

provide an indication of at least one second computer application, of the plurality of computer applications, using the similarity matrix and based on the selection of the first computer application.

9. The non-transitory computer-readable medium of claim 8, where the similarity matrix defines the similarity between the plurality of computer applications as numerical values based on the API calls in source code of the plurality of computer applications.

10. The non-transitory computer-readable medium of claim 8, where the plurality of instructions, when executed by the one or more processors to generate the similarity matrix, cause the one or more processors to:

generate the similarity matrix from a plurality of vectors corresponding to the plurality of computer applications using a vector space model,

the plurality of vectors including elements corresponding to the categories of the categorization,

the elements including values based on a number of the API calls in source code and documentation for a computer application corresponding to a vector and in the category corresponding to one of the elements,

at least one of the API calls corresponding to one of the categories.

11. The non-transitory computer-readable medium of claim 8, where the plurality of instructions, when executed by the one or more processors to generate the similarity matrix, further cause the one or more processors to:

extract the API calls from source code of the plurality of computer applications.

12. The non-transitory computer-readable medium of claim 8, where the plurality of instructions, when executed by the one or more processors to generate the similarity matrix, cause the one or more processors to:

utilize an inverse document frequency calculation to find common API calls; and

filter out the common API calls from the API calls prior to the categorization of the API calls.

13. The non-transitory computer-readable medium of claim 8, where the first dimension of first entries are columns and the second dimension of second entries are rows.

14. The non-transitory computer-readable medium of claim 8, where different API calls have different weights.

15. A method comprising:

generating, by a device, a similarity matrix defining a similarity between a plurality of computer applications according to a categorization of application programming interface (API) calls,

the similarity matrix being generated from a term document matrix using singular value decomposition,

the term document matrix including a first dimension of first entries corresponding to the plurality of computer applications and a second dimension of second entries corresponding to categories of the categorization,

elements of the term document matrix having values based on a quantity of API calls in a computer application corresponding to a first entry of the first dimension, and in a category, of the categories, corresponding to a second entry of the second dimension, and

at least one of the API calls corresponding to one of the categories,

the similarity being based on weights for the API calls contained in the plurality of computer applications,

a respective weight for a respective API call in a respective computer application being based on a quantity of API calls in the respective computer application and a quantity of computer applications, of the plurality of computer applications, that contain the respective API call;

receiving, by the device, a selection of a first computer application of the plurality of computer applications; and

providing, by the device, an indication of at least one second computer application, of the plurality of computer applications, using the similarity matrix and based on the selection of the first computer application.

16. The method of claim 15, where the similarity matrix defines the similarity between the plurality of computer applications as numerical values based on the API calls in source code of the plurality of computer applications.

17. The method of claim 15, where generating the similarity matrix includes:

generating the similarity matrix from a plurality of vectors corresponding to the plurality of computer applications using a vector space model,

the plurality of vectors including elements corresponding to the categories of the categorization,

the elements including values based on a number of the API calls in source code and documentation for a computer application corresponding to a vector and in the category corresponding to one of the elements,

at least one of the API calls corresponding to one of the categories.

18. The method of claim 15, where generating the similarity matrix includes:

utilizing an inverse document frequency calculation to find common API calls; and

filtering out the common API calls from the API calls prior to the categorization of the API calls.

19. The method of claim 15, where the first dimension of first entries are columns and the second dimension of second entries are rows.

20. The method of claim 15, where different API calls have different weights.