Techniques for suggesting correct identifiers

• US 8,930,387 B1
• Filed: 12/07/2012
• Issued: 01/06/2015
• Est. Priority Date: 08/31/2010
• Status: Active Grant

• Current Assignee: The MathWorks, Inc.
• Original Assignee: The MathWorks, Inc.
• Inventors: Hicklin, Joseph F., Benevento, Audrey L.
• Primary Examiner(s): Vy, Hung T
• Application Number:
  US13/708,440
• Time in Patent Office: 760 Days
• Field of Search: 707/759, 707/758, 707/765, 707/769, 715/773, 715/257, 704/8
• US Class Current: 707/759
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

In an illustrative embodiment, an apparatus, computer-readable medium, system, or method may be configured to suggest correct identifiers. Frequencies of occurrence of expected identifiers may be determined. One or more unrecognized identifiers may be detected. One or more first numerical scores indicating that one or more unrecognized identifiers were provided to the computing environment in place of one or more of the expected identifiers may be calculated. Second numerical scores indicating that the unrecognized identifiers were intended to include expected identifiers may also be calculated. The second numerical scores may be calculated using the first numerical scores and the frequencies of occurrence of the expected identifiers. One or more expected identifiers may be selected based on the second set of numerical scores.

Claims

1. One or more tangible non-transitory computer-readable storage media for storing computer-executable instructions executable by processing logic, the media storing one or more instructions to:

determine frequencies of occurrence of expected identifiers in a computing environment, wherein the frequencies of occurrence are determined based on usage data for the expected identifiers;

detect one or more unrecognized identifiers in the computing environment;

associate a first penalty score to first characters on a keyboard using a keystroke penalty matrix, the first characters being within a first distance of an entered character on the keyboard;

associate a second penalty score to second characters on the keyboard using the keystroke penalty matrix, the second characters being within a second distance of the entered character on the keyboard, wherein the first penalty score exceeds the second penalty score and the first distance exceeds the second distance;

calculate one or more first numerical scores using the keystroke penalty matrix, the first penalty score, and the second penalty score, wherein the one or more first numerical scores indicate that the one or more unrecognized identifiers were provided to the computing environment in place of one or more of the expected identifiers;

determine second numerical scores using the first numerical scores and the frequencies of occurrence of the one or more expected identifiers, wherein the second numerical scores indicate that one or more unrecognized identifiers were intended to include one or more expected identifiers; and

select one or more expected identifiers based on the second numerical scores.

2. The one or more tangible non-transitory computer-readable storage media of claim 1, wherein one or more unrecognized identifiers are detected when a command is evaluated.

3. The one or more tangible non-transitory computer-readable storage media of claim 1, wherein the one or more first numerical scores are calculated using the keystroke penalty matrix and a string matching algorithm.

4. The one or more tangible non-transitory computer-readable storage media of claim 3, wherein the string matching algorithm includes at least one of:

Needleman-Wunsch algorithm;

or

Smith-Waterman algorithm.

5. The one or more tangible non-transitory computer-readable storage media of claim 1, wherein the second numerical scores are calculated using the one or more first numerical scores, the frequencies of occurrence of the one or more expected identifiers, and Bayes theorem.

6. The one or more tangible non-transitory computer-readable storage media of claim 1, wherein the expected identifiers comprise at least one of:

commands, variables, workspace variables, local variables, function names, method names, argument names, tokens stored in files, keywords, properties, properties of an object, or lexical tokens.

7. The one or more tangible non-transitory computer-readable storage media of claim 1, herein selecting one or more expected identifiers based on the second numerical scores further comprises instructions to:

choose one or more expected identifiers based on a threshold value for the second numerical scores.

8. The one or more tangible non-transitory computer-readable storage media of claim 1, further comprising instructions to:

display the selected one or more expected identifiers.

9. The one or more tangible non-transitory computer-readable storage media of claim 8, wherein displaying the selected one or more expected identifiers further comprises instructions to:

depict the selected one or more expected identifiers as a user-selectable option;

receive an indication of a user selection of the user-selectable option; and

apply the user-selected option.

10. The one or more tangible non-transitory computer-readable storage media of claim 1, further comprising instructions to:

apply a selected option.

11. The one or more tangible non-transitory computer-readable storage media of claim 10, wherein applying the selected option further comprises instructions to:

display the selected option;

receive modifications to the selected option; and

execute the modified selected option.

12. The one or more tangible non-transitory computer-readable storage media of claim 1, further comprising instructions to:

transmit the selected one or more expected identifiers to a second computing environment.

13. The one or more tangible non-transitory computer-readable storage media of claim 1, wherein the expected identifiers include global identifiers and local identifiers, the global identifiers including at least one of global variables, global functions, global methods, or global keywords, and the local identifiers including at least one of local variables, local functions, or local scripts.

14. The one or more tangible non-transitory computer-readable storage media of claim 1, further comprising instructions to:

determine frequencies of occurrence of additional expected identifiers, the additional expected identifiers available to the computing environment.

15. One or more tangible non-transitory computer-readable storage media for storing computer-executable instructions executable by processing logic, the media storing one or more instructions to:

download the instructions of claim 1 to a computing environment, which when executed by the computing environment causes the computing environment to perform operations comprising the downloaded instructions.

16. One or more tangible non-transitory computer-readable media holding computer-executable instructions using a distributed environment, the media holding one or more instructions to:

perform the instructions of claim 1 in a distributed environment.

17. A computer-implemented method comprising:

determining frequencies of occurrence of expected identifiers in a computing environment, the determining performed by one or more processors;

identifying one or more unrecognized identifiers in the computing environment, the identifying performed by the one or more processors;

determining first numerical values in the computing environment, the calculating performed by the one or more processors, wherein the first numerical values;

indicate probabilities of inputting one or more unrecognized identifiers that were inputted in place of one or more expected identifiers, and

are determined using;

a penalty matrix that identifies a first penalty score for first characters when the first characters are within a first distance of an entered character on a keyboard, the first penalty matrix identifying a second penalty score for second characters when the second characters are within a second distance of the entered character, wherein the first penalty score exceeds the second penalty score and the first distance exceeds the second distance;

determining second numerical values, wherein the second numerical values;

indicate probabilities that one or more unrecognized identifiers correspond to one or more expected identifiers, and

are determined using;

the frequencies of occurrence of the one or more identifiers, and

the entries in the first plurality of numerical values; and

selecting one or more expected identifiers in the computing environment, the selecting performed by the one or more processors based on the second numerical values.

18. The method of claim 17, wherein the first numerical values are determined using the keystroke penalty matrix and a string matching algorithm.

19. The method of claim 18, wherein the string matching algorithm is based on one of:

Needleman-Wunsch algorithm;

or

Smith-Waterman algorithm.

20. The method of claim 17, wherein the second numerical values are determined using a first theorem including Bayes theorem.

21. A computer-implemented method, the method comprising:

downloading software to a computing environment, which when executed by the computing environment causes the computing environment to perform operations comprising the method of claim 17.

22. A computer-implemented method, the method comprising:

performing the method of claim 17 in a distributed environment.

23. A system comprising:

a storage device;

an input device; and

a processor configured to;

identify expected identifiers retrieved from the storage device;

determine frequencies of occurrence for the expected identifiers;

receive a command from the input device;

identify the command as including one or more unrecognized identifiers;

determine first numerical values using a string matching algorithm, wherein the first numerical values;

indicate probabilities of inputting one or more unrecognized identifiers when one or more expected identifiers were mistyped;

determine second numerical values, wherein the second numerical values;

indicate probabilities that one or more unrecognized identifiers were one or more mistyped expected identifiers, and

are determined using;

the first numerical values,

the frequencies of occurrence for the one or more expected identifiers, and

a second algorithm using Bayes theorem; and

identify one or more expected identifiers as the one or more unrecognized identifiers based on the second numerical values in the computing environment.

24. The system of claim 23, wherein the string matching algorithm is based on one of:

Needleman-Wunsch algorithm;

or

Smith-Waterman algorithm.

25. The system of claim 23, wherein the first numerical values are determined using a string matching algorithm and a keystroke penalty matrix, wherein using the keystroke penalty matrix associates a penalty score to characters identified with nearby keys on a keyboard that is smaller than a penalty score associated with keys located further away from an entered character on the keyboard.