Random Noise and Random Walk algorithms are local search strategies that have been used for the problem of satisfiability testing (SAT). We present a Markov-chain based analysis of the performance of these algorithms. The performance measures we consider are the probability of finding a satisfying assignment and the distribution of the best solution observed on a given SAT instance. The analysis provides exact statistics, but is restricted to small problems as it requires the storage and use of knowledge about the entire search space. We examine the effect of p, the probability of making non-greedy moves, on these algorithms and provide a justification for the practice of choosing this value empirically.