This dissertation presents novel approaches to test context aware applications that suffer from a cost prohibitive number of context and GUI events and event combinations. The contributions of this work to test context aware applications under test include: (1) a real-world context events dataset from 82 Android users over a 30-day period, (2) applications of Markov models, Closed Sequential Pattern Mining (CloSPAN), Deep Neural Networks- Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), and Conditional Random Fields (CRF) applied to predict context patterns, (3) data driven test case generation techniques that insert events at the beginning of each test case in a round-robin manner, iterate through multiple context events at the beginning of each test case in a round-robin manner, and interleave real-world context event sequences and GUI events, and (4) systematically interleaving context with a combinatorial-based approach. The results of our empirical studies indicate (1) CRF outperforms other models thereby predicting context events with F1 score of about 60% for our dataset, (2) the ISFreqOne that iterates over context events at the beginning of each test case in a round-robin manner as well as interleaves real-world context event sequences and GUI events at an interval one achieves up to four times better code coverage than not including context, 0.06 times better coverage than RSContext that inserts random context events at the beginning of each test case, 0.05 times better coverage than ISContext that iterates over context events to insert at the beginning of each test case in a round-robin manner, and 0.04 times better coverage than ISFreqTwo that iterates over context events at the beginning of each test case in a round-robin manner as well as interleaves real-world context event sequences and GUI events at an interval two on an average across four subject applications and, (3) the PairwiseInterleaved technique that selects a different context event at the beginning of each test case by iterating through context covering array in a round-robin manner and systematically interleaves context with GUI events by prioritizing the execution of GUI events in new contexts achieves higher code coverage up to a factor of six when compared to Monkey, up to a factor of 1.3 when compared to a technique that generates test suites without context events, and similar code coverage when compared to ISContext that iterates over context events to insert at the beginning of each test case in a round-robin manner on an average across five subject applications.