A survey on Test Suite Reduction frameworks and tools <s> Randomized Unit Testing <s> This paper deals with testing of nondeterministic software systems. We assume that a model of the nondeterministic system is given by a directed graph with two kind of vertices: states and choice points. Choice points represent the nondeterministic behaviour of the implementation under test (IUT). Edges represent transitions. They have costs and probabilities. Test case generation in this setting amounts to generation of a game strategy. The two players are the testing tool (TT) and the IUT. The game explores the graph. The TT leads the IUT by selecting an edge at the state vertices. At the choice points the control goes to the IUT. A game strategy decides which edge should be taken by the TT in each state. This paper presents three novel algorithms 1) to determine an optimal strategy for the bounded reachability game, where optimality means maximizing the probability to reach any of the given final states from a given start state while at the same time minimizing the costs of traversal; 2) to determine a winning strategy for the bounded reachability game, which guarantees that given final vertices are reached, regardless how the IUT reacts; 3) to determine a fast converging edge covering strategy, which guarantees that the probability to cover all edges quickly converges to 1 if TT follows the strategy. <s> BIB001 </s> A survey on Test Suite Reduction frameworks and tools <s> Randomized Unit Testing <s> Randomized testing is an effective method for testing software units. The thoroughness of randomized unit testing varies widely according to the settings of certain parameters, such as the relative frequencies with which methods are called. In this paper, we describe Nighthawk, a system which uses a genetic algorithm (GA) to find parameters for randomized unit testing that optimize test coverage. Designing GAs is somewhat of a black art. We therefore use a feature subset selection (FSS) tool to assess the size and content of the representations within the GA. Using that tool, we can reduce the size of the representation substantially while still achieving most of the coverage found using the full representation. Our reduced GA achieves almost the same results as the full system, but in only 10 percent of the time. These results suggest that FSS could significantly optimize metaheuristic search-based software engineering tools. <s> BIB002
Unit testing is one of the prominent software testing method that focuses on testing the smallest testable part of the SUT such as source code method, a group of methods, or classes along with related control data and operating procedures BIB001 . Randomized unit testing refers to such testing mechanism, where some random elements are involved in selecting the parameters and/or methods  . Therefore, it generates distinct test inputs easily and quickly from common data structures, which can be useful in exposing defects BIB002 . The following sub-sections debate on the existing randomized unit testing based TSR tools with special emphasis on their strengths and shortcomings.