Techniques for suggesting correct identifiers

• US 8,346,793 B1
• Filed: 03/29/2011
• Issued: 01/01/2013
• Est. Priority Date: 08/31/2010
• Status: Active Grant

• Current Assignee: The MathWorks, Inc.
• Original Assignee: The MathWorks, Inc.
• Inventors: Hicklin, Joe F., Benevento, Audrey L.
• Primary Examiner(s): Vy, Hung T
• Application Number:
  US13/074,717
• Time in Patent Office: 644 Days
• Field of Search: 707/722, 707/758, 707/759, 707/765, 707/769, 715/773, 715/257, 345/168, 345/173, 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, or method may be configured to suggest correct identifiers. Expected identifiers may be created and their frequencies of occurrence may be calculated. Unrecognized identifiers may be detected. First numerical scores indicating that the unrecognized identifiers were provided in place of one or more 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. The system may select one or more expected identifiers 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 for:

creating expected identifiers in a computing environment;

calculating frequencies of occurrence of the expected identifiers in the computing environment;

detecting one or more unrecognized identifiers received by the computing environment;

calculating first numerical scores using a string matching algorithm and a keystroke penalty matrix, where the first numerical scores indicate that one or more unrecognized identifiers were provided to the computing environment in place of one or more expected identifiers;

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

selecting 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 detecting one or more unrecognized identifiers occurs when a command is evaluated.

3. The one or more tangible non-transitory computer-readable storage media of claim 1, wherein the string matching algorithm includes at least one of:

Needleman-Wunsch algorithm;

or

Smith-Waterman algorithm.

4. The one or more tangible non-transitory computer-readable storage media of claim 1, 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.

5. 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.

6. The one or more tangible non-transitory computer-readable storage media of claim 1, wherein the frequencies of occurrence are determined based on usage data for expected identifiers.

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

choosing 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:

displaying 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:

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

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

applying the user-selected option.

10. The one or more tangible non-transitory computer-readable storage media of claim 9, wherein applying the user-selected option further comprises:

displaying the user-selected option;

receiving modifications to the user-selected option; and

executing the modified user-selected option.

11. The one or more tangible non-transitory computer-readable storage media of claim 10, wherein displaying the user-selected option occurs on a command-line.

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

transmitting 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 include at least one of global variables, global functions, global methods, or global keywords, and the local identifiers include 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:

adding additional expected identifiers to the computer environment; and

calculating the frequencies of occurrence of the additional expected identifiers.

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 for:

downloading 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 for:

performing the instructions of claim 1 in a distributed environment.

17. 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 for:

creating a keystroke penalty matrix by performing a first operation on keystroke mistype probabilities in a computing environment;

receiving textual input in the computing environment;

calculating initial scores for expected identifiers in the computing environment, the calculating the initial scores comprising;

performing a first operation on the initial scores, where the first operation includes;

comparing the textual input and the expected identifiers to the keystroke penalty matrix; and

calculating a final score for the expected identifiers, the calculating a final score comprising;

performing a second operation on the initial scores in the computing environment, the second operation using Bayes theorem.

18. The one or more tangible non-transitory computer-readable storage media of claim 17, wherein calculating the initial scores comprises:

comparing keystrokes from the textual input with keystrokes of the expected identifiers using the penalty matrix;

obtaining penalty numerical values based on the comparing; and

determining the initial scores using the penalty numerical values.

19. The one or more tangible non-transitory computer-readable storage media of claim 17, wherein the first operation includes a log function and the second operation is an exponentiating function.

20. The one or more tangible non-transitory computer-readable storage media of claim 17, wherein the keystroke mistype probabilities includes at least one of the probability of typing:

a match, an incorrect case, a key close to an intended key, or a key miss.

21. A computer-implemented method comprising:

creating expected identifiers in a computing environment by one or more processors;

calculating frequencies of occurrence of the expected identifiers in the computing environment by the one or more processors;

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

calculating first numerical values in the computing environment by the one or more processors, where the first numerical values;

indicate probabilities of inputting one or more unrecognized identifiers when intending to input one or more expected identifiers, and

are calculated using;

a string matching algorithm, and

a penalty matrix;

calculating second numerical values, where the second numerical values;

indicate probabilities that one or more unrecognized identifiers were intended to be one or more expected identifiers, and

are calculated using;

a first theorem including Bays theorem,

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 by the one or more processors based on the second numerical values.

22. The method of claim 21, wherein the string matching algorithm is based on one of:

Needleman-Wunsch algorithm;

or

Smith-Waterman algorithm.

23. 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 21.

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

performing the method of claim 21 in a distributed environment.

25. A system comprising:

a storage device;

an input device; and

a processor configured to;

identify expected identifiers from the storage device;

calculate frequencies of occurrence for the expected identifiers;

receive a command from the input device;

identify the command as including one or more unrecognized identifiers;

compute first numerical values using a string matching algorithm and a keystroke penalty matrix, where the first numerical values;

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

compute second numerical values, where the second numerical values;

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

are calculated 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.

26. The system of claim 25, wherein the string matching algorithm is based on one of:

Needleman-Wunsch algorithm;

or

Smith-Waterman algorithm.