A method for assessing the quality of mobile applications, the method being performed by a computer networked environment comprising a cloud-based service for mobile devices, the method comprising the method steps: performing a static analysis risk assessment of binary code associated with a mobile application being submitted by a submission source; examining execution behavior of the mobile application within an instrumented sandbox environment aggregating the analysis results of the execution behavior and the static analysis; generating, based on the aggregated analysis results of the execution behavior and the static analysis, a feature vector comprising: (i) a network summary feature, (ii) an operating system based behavioral feature, and (iii) a static analysis feature, wherein the feature vector is an analysis vector comprised of one or more feature sets derived from analysis of application related data selected from the group comprising execution characteristics of the application and analysis of static characteristics of the application; accumulating analysis results from a selected set of previously generated feature vectors; applying one or more machine learning techniques on the accumulated analysis results; performing a periodical clustering re-evaluation of the set of feature vectors thereby resulting in a set of classification clusters; benchmarking aggregated statistical assessments of the analysis results for the mobile application against aggregated statistical assessments associated with a set of previous analyses using the same or different mobile applications; and performing predictive classification of the mobile application using the feature vector and the machine learning techniques on the accumulated analysis results, yielding predictor statistics describing quality and vulnerability characteristics of mobile applications. performing a static analysis risk assessment of binary code associated with a mobile application being submitted by a submission source; examining execution behavior of the mobile application within an instrumented sandbox environment aggregating the analysis results of the execution behavior and the static analysis; generating, based on the aggregated analysis results of the execution behavior and the static analysis, a feature vector comprising: (i) a network summary feature, (ii) an operating system based behavioral feature, and (iii) a static analysis feature, wherein the feature vector is an analysis vector comprised of one or more feature sets derived from analysis of application related data selected from the group comprising execution characteristics of the application and analysis of static characteristics of the application; accumulating analysis results from a selected set of previously generated feature vectors; applying one or more machine learning techniques on the accumulated analysis results; performing a periodical clustering re-evaluation of the set of feature vectors thereby resulting in a set of classification clusters; benchmarking aggregated statistical assessments of the analysis results for the mobile application against aggregated statistical assessments associated with a set of previous analyses using the same or different mobile applications; and performing predictive classification of the mobile application using the feature vector and the machine learning techniques on the accumulated analysis results, yielding predictor statistics describing quality and vulnerability characteristics of mobile applications. <EOS>