Abstract: A system and method to adaptively generate a program model. Source code of a program to be tested for code issues, and a set of predefined patterns to be tested in the source code are received. Feature configuration data is generated by determining a set of features corresponding to the received set of predefined patterns. A set of program models is identified by selecting, for each feature in the set of features, a program model from among a plurality of program models that is optimized for the feature. A dynamic program model is built based on the identified set of program models, the dynamic program model being adapted to resolve each of the patterns included in the received set of predefined patterns. And the source code is tested for code issues by extracting from the dynamic program model instances of each of the set of predefined patterns.

Abstract: A system and method to adaptively generate a program model. Source code of a program to be tested for code issues, and a set of predefined patterns to be tested in the source code are received. Feature configuration data is generated by determining a set of features corresponding to the received set of predefined patterns. A set of program models is identified by selecting, for each feature in the set of features, a program model from among a plurality of program models that is optimized for the feature. A dynamic program model is built based on the identified set of program models, the dynamic program model being adapted to resolve each of the patterns included in the received set of predefined patterns. And the source code is tested for code issues by extracting from the dynamic program model instances of each of the set of predefined patterns.

Abstract: Systems and methods for improving overall quality of three-dimensional (3D) content by altering parallax budget and compensating for moving objects are disclosed. According to an aspect, a method includes identifying areas including one or more pixels of the 3D image that violate pre-defined disparity criterion. Further, the method includes identifying a region that includes pixels whose disparity exceeds a predetermined threshold. The method also includes identifying pixels belonging to either left or right images to replace the corresponding ones on the other image. Further, the method includes identifying key pixels to determine disparity attributes of a problem area. The method also includes identifying a proper depth of key pixels. Further, the method includes calculating the disparity of all remaining pixels in the area based on the disparity values of key pixels.