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This work presents a method to combine testing techniques adaptively during the testing process. It intends to mitigate the sources of uncertainty of software testing processes, by learning from past experience and, at the same time, adapting the technique selection to the current testing session. The method is based on machine learning strategies. It uses offline strategies to take historical information into account about the techniques performance collected in past testing sessions; then, online strategies are used to adapt the selection of test cases to the data observed as the testing proceeds. Experimental results show that techniques performance can be accurately characterized from features of the past testing sessions, by means of machine learning algorithms, and that integrating this result into the online algorithm allows improving the fault detection effectiveness with respect to single testing techniques, as well as to their random combination.