Abstract: Techniques for facilitating incremental static program analysis based on machine learning techniques are provided. In one example, a system comprises a feature component that, in response to an update to a computer program, generates feature vector data representing the update, wherein the feature vector data comprises feature data representing a feature of the update derived from an abstract state of the computer program, and wherein the abstract state is based on a mathematical model of the computer program that is generated in response to static program analysis of the computer program. The system can further comprise a machine learning component that employs a classifier algorithm to identify an affected portion of the mathematical model that is affected by the update. The system can further comprise an incremental analysis component that incrementally applies the static program analysis to the computer program based on the affected portion.

Abstract: Techniques for facilitating incremental static program analysis based on machine learning techniques are provided. In one example, a system comprises a feature component that, in response to an update to a computer program, generates feature vector data representing the update, wherein the feature vector data comprises feature data representing a feature of the update derived from an abstract state of the computer program, and wherein the abstract state is based on a mathematical model of the computer program that is generated in response to static program analysis of the computer program. The system can further comprise a machine learning component that employs a classifier algorithm to identify an affected portion of the mathematical model that is affected by the update.