About 60% of the software development cost for online applications is related to developing user interfaces commonly used by the end users to interact with those applications. Frequent small changes to user interfaces (UIs) however easily break about 70% of the test cases intended to mimic users' interactions. Fixing broken tests results in extra costs of maintenance while lessening the benefits of test automation. One of the biggest challenges in creating resilient UI level test cases is to identify and locate the elements of the UI in a way that small UI changes do not break the way in which an element was originally located.

Execution traces can be overwhelmingly large. To reduce their size, sampling techniques, especially the ones based on random sampling, have been extensively used. Random sampling, however, may result in samples that are not representative of the original trace. We propose a trace sampling framework based on stratified sampling that not only reduces the size of a trace but also results in a sample that is representative of the original trace by ensuring that the desired characteristics of an execution are distributed similarly in both the sampled and the original trace.

We show that convex KL-regularized objective functions are obtained from a PAC-Bayes risk bound when using convex loss functions for the stochastic Gibbs classifier that upper-bound the standard zero-one loss used for the weighted majority vote. By restricting ourselves to a class of posteriors, that we call quasi uniform, we propose a simple coordinate descent learning algorithm to minimize the proposed KL-regularized cost function. We show that standard ellp-regularized objective functions currently used, such as ridge regression and ellp-regularized boosting, are obtained from a relaxation of the KL divergence between the quasi uniform posterior and the uniform prior. We present numerical experiments where the proposed learning algorithm generally outperforms ridge regression and AdaBoost.