Abstract: A computerized system and method may provide a dynamic, adaptable, and robust framework for intelligent web-based video playing. A computerized system including a memory and a computer processor may arrange a plurality of video frames based on one or more data items associated with groups of pictures (GOPs) and/or video playing commands and/or settings, and render and/or play the arranged frames on an output display. Functionalities enabled by different embodiments of the invention include, for example: automatically adjusting a video playing speed according to a number of accumulated frames within a memory buffer; appending a memory buffer with frames included in another (e.g., separate) memory buffer based on a requested video playing direction; omitting frames from a video sequence (thus sparing the need to process omitted frames) based on quantifying their information content and/or distance from reference frames; and synchronizing video playing on different (e.g., remote) computer systems.

Abstract: A set of predicted binary quality indexes is created from a sample set of application lifecycle information and customer encountered defects (CED) for each module id and revision (rev) pair for each application. Normalized effort and quality related factors are extracted for each module id and rev pair of each application. A binary quality index is created based on a set of weighted CED ratings for each module id and rev pair of each application. A prediction model for the binary quality index is created by training a decision tree-based classifier with the sample set to create a set of prediction weights for each effort and quality factor. The set of prediction weights is applied to the effort and quality related factors to each module id and rev pair of an application under-development to create the set of predicted binary quality indexes.

Abstract: A set of predicted binary quality indexes is created from a sample set of application lifecycle information and customer encountered defects (CED) for each module id and revision (rev) pair for each application. Normalized effort and quality related factors are extracted for each module id and rev pair of each application. A binary quality index is created based on a set of weighted CED ratings for each module id and rev pair of each application. A prediction model for the binary quality index is created by training a decision tree-based classifier with the sample set to create a set of prediction weights for each effort and quality factor. The set of prediction weights is applied to the effort and quality related factors to each module id and rev pair of an application under-development to create the set of predicted binary quality indexes.