1. A method of analyzing behaviors of a software application operating in a computing device, comprising: receiving, via a processor of the computing device from a server device, a first behavior vector information structure; receiving, via the processor, a list of features that could contribute to a degradation of the computing device; generating, via the processor of the computing device, a second behavior vector information structure, wherein: the received first behavior vector information structure includes a plurality of values that collectively identify a range of expected behaviors that the computing device should exhibit when executing the software application, and the generated second behavior vector information structure includes values that collectively characterize an observed behavior of the software application; comparing the received first behavior vector information structure to the generated second behavior vector information structure to determine whether there are deviations between observed and expected behaviors; and generating a first machine learning classifier model based on the received list of features and applying the generated second behavior vector information structure to the first machine learning classifier model in response to determining, based on a result of comparing the received first behavior vector information structure to the generated second behavior vector information structure, that there are deviations between observed and expected behaviors.