Training classifiers for program analysis

1. A method for training a classifier, comprising:

running an initial security analysis on a training codebase to generate a set of vulnerabilities associated with the training codebase;

analyzing a program with a feature set that limits a number of detected vulnerabilities to generate a limited set of vulnerabilities associated with the feature set;

comparing the limited set of vulnerabilities to a known vulnerability distribution to generate an accuracy score using a processor; and

iterating said steps of analyzing and comparing using different feature sets to find a feature set having a highest accuracy score.

2. The method of claim 1 , wherein the feature set includes vulnerability path length.

3. The method of claim 1 , wherein the feature set includes one or more blocked code locations.

4. The method of claim 1 , wherein the known vulnerability distribution comprises a statistical representation of vulnerability prevalence.

5. The method of claim 1 , wherein comparing comprises comparing numbers of vulnerabilities per line of code.

6. The method of claim 1 , further comprising training a classifier based on the feature set having a highest accuracy score.

7. The method of claim 1 , wherein the training codebase comprises a first set of applications and wherein the known vulnerability distribution is based on a manual analysis of a second set of applications.

8. The method of claim 7 , wherein the training codebase further comprises a user's local codebase.

9. The method of claim 1 , wherein each iteration uses a different feature set based on a Tabu search strategy.

10. A method for training a classifier, comprising:

running an initial security analysis on a training codebase to generate a set of vulnerabilities associated with the training codebase;

analyzing a program with a feature set, comprising vulnerability path length, that limits a number of detected vulnerabilities to generate a limited set of vulnerabilities associated with the feature set;

comparing a number of vulnerabilities per line of code found with the limited set of vulnerabilities to a known statistical representation of vulnerability prevalence to generate an accuracy score using a processor;

iterating said steps of analyzing and comparing using different feature sets to find a feature set having a highest accuracy score; and

generating a classifier based on the feature set having a highest accuracy score.

11. The method of claim 10 , wherein the feature set includes one or more blocked code locations.

12. The method of claim 10 , wherein the training codebase comprises a first set of applications and wherein the known vulnerability distribution is based on a manual analysis of a second set of applications.

13. The method of claim 12 , wherein the training codebase further comprises a user's local codebase.

14. A method for security analysis, comprising:

training a classifier, comprising:

running an initial security analysis on a training codebase to generate a set of vulnerabilities associated with the training codebase;

analyzing a program with a feature set that limits a number of detected vulnerabilities to generate a limited set of vulnerabilities associated with the feature set;

comparing the limited set of vulnerabilities to a known vulnerability distribution to generate an accuracy score using a processor;

iterating said steps of analyzing and comparing using different feature sets to find a feature set having a highest accuracy score; and

generating a classifier based on the feature set having the highest accuracy score; and

scanning code using the classifier to locate potential vulnerabilities.

15. The method of claim 14 , wherein the feature set includes vulnerability path length.

16. The method of claim 14 , wherein the feature set includes one or more blocked code locations.

17. The method of claim 14 , wherein the known vulnerability distribution comprises a statistical representation of vulnerability prevalence.

18. The method of claim 14 , wherein comparing comprises comparing numbers of vulnerabilities per line of code.

19. The method of claim 14 , wherein the training codebase comprises a first set of applications and wherein the known vulnerability distribution is based on a manual analysis of a second set of applications.

20. The method of claim 19 , wherein the training codebase further comprises a user's local codebase.

21. The method of claim 14 , wherein each iteration uses a different feature set based on a Tabu search strategy.

22. A method for security analysis, comprising:

training a classifier, comprising:

running an initial security analysis on a training codebase comprising a first set of applications and a user's local codebase to generate a set of vulnerabilities associated with the training codebase;

analyzing a program with a feature set, comprising vulnerability path length, that limits a number of detected vulnerabilities to generate a limited set of vulnerabilities associated with the feature set;

comparing the a number of vulnerabilities per line of code found with the limited set of vulnerabilities to a known statistical representation of vulnerability prevalence based on a manual analysis of a second set of applications to generate an accuracy score using a processor;

iterating said steps of analyzing and comparing using different feature sets to find a feature set having a highest accuracy score; and

generating a classifier based on the feature set having the highest accuracy score; and

scanning code using the classifier to locate potential vulnerabilities.