The Software Reliability group at KTH-CSC has designed and built a novel test platform LBTest for black-box requirements testing of reactive and embedded software systems (e.g. web servers, automobile control units, etc). The main concept of LBTest is to create a large number of test cases by incorporation of an automata learning algorithm with a model checking algorithm (NuSMV). This platform aims to support different learned automata, learning algorithms and different model checking algorithms which can be combined to implement the paradigm of learning-based testing (LBT).This thesis project investigates an existing published algorithm for learning deterministic finite automata (DFA)known as Kearns algorithm. The aimof this thesis is to investigate how effective Kearns algorithm is from a software testing perspective.Angluin’s well-known L* DFA learning algorithm has a simple structure and implementation. On the other hand, Kearnsalgorithm has more complex, difficult structure and harder implementation than L* algorithm, however it is more efficient and faster. For this reason, the plan is to implement an advanced DFA learning algorithm, Kearns algorithm[4], from a description in the literature (using Java).We consider a methodology to compare Kearns algorithm with Angluin’s DFA learning algorithm based on the master thesis of Czerny[8].The comparisonsbetween the Kearns and the L* algorithmsare based on the number of membership and equivalence queriesto investigate the difficulty of learning