Abstract: A technique includes receiving data representing an output of a security scan of an application and an audit of the security scan by a human auditor. The output represents a security issue with the application, which is identified by the security scan, and the audit represents an analysis of the security issue by the human auditor. The technique includes training a security scan classifier to learn a classification preference of the human auditor. Training the security scan classifier includes, processing the data in a processor-based machine to, based at least in part on the output of the security scan and the analysis of the security scan by the human auditor, learn the classification preference of the human auditor to the issue to build a classification model for the issue.

Abstract: A technique includes receiving data representing an output of a security scan of an application and an audit of the security scan by a human auditor. The output represents a security issue with the application, which is identified by the security scan, and the audit represents an analysis of the security issue by the human auditor. The technique includes training a security scan classifier to learn a classification preference of the human auditor. Training the security scan classifier includes, processing the data in a processor-based machine to, based at least in part on the output of the security scan and the analysis of the security scan by the human auditor, learn the classification preference of the human auditor to the issue to build a classification model for the issue.

Abstract: A technique includes receiving issue data, which represents an issue identified by a security scan of an application and attributes of the issue. The technique includes applying a machine classifier to the issue data to prioritize the issue; based at least in part on a human audit of the classified data, generating additional issue data representing a priority correction for the issue; and retraining the classifier based on the additional issue data.

Abstract: A technique includes receiving data representing issues identified in a security scan of an application and features associated with the issues. The technique includes processing the data in a processor-based machine to selectively assign classifiers to the security issues based at least in part on the features. The technique includes using the assigned classifiers to classify the issues.

Abstract: A data structure is based on examples that include respective program code portions associated with corresponding tags that indicate content of the respective program code portions. A tagger determines at least one tag to associate with a first program code portion based on the data structure. An updated version of the data structure is received, The tagger, which remains unmodified, determines at least one tag to associate with a second program code portion based on the updated version of the data structure.