Estimating the feasibility of transition paths in extended finite state machines

There has been significant interest in automating testing on the basis of an extended finite state machine (EFSM) model of the required behaviour of the implementation under test (IUT). Many test criteria require that certain parts of the EFSM are executed. For example, we may want to execute every transition of the EFSM. In order to find a test suite (set of input sequences) that achieves this we might first derive a set of paths through the EFSM that satisfy the criterion using, for example, algorithms from graph theory. We then attempt to produce input sequences that trigger these paths. Unfortunately, however, the EFSM might have infeasible paths and the problem of determining whether a path is feasible is generally undecidable. This paper describes an approach in which a fitness function is used to estimate how easy it is to find an input sequence to trigger a given path through an EFSM. Such a fitness function could be used in a search-based approach in which we search for a path with good fitness that achieves a test objective, such as executing a particular transition, and then search for an input sequence that triggers the path. If this second search fails then we search for another path with good fitness and repeat the process. We give a computationally inexpensive approach (fitness function) that estimates the feasibility of a path. In order to evaluate this fitness function we compared the fitness of a path with the ease with which an input sequence can be produced using search to trigger the path and we used random sampling in order to estimate this. The empirical evidence suggests that a reasonably good correlation (0.72 and 0.62) exists between the fitness of a path, produced using the proposed fitness function, and an estimate of the ease with which we can randomly generate an input sequence to trigger the path

Test Case Generation According to the Binary Search Strategy

One of the important tasks during software testing is the generation of test cases. Unfortunately, existing approaches to test case generation often have problems limiting their use. A problem of dynamic test case generation approaches, for instance, is that a large number of iterations can be necessary to obtain test cases. This article introduces a formal framework for the application of the well-known search strategy of binary search in path-oriented test case generation and explains the binary search-based test case generation (BINTEST) algorithm

How to Overcome the Equivalent Mutant Problem and Achieve Tailored Selective Mutation Using Co-evolution

The use of Genetic Algorithms in evolution of mutants and test cases offers new possibilities in addressing some of the main problems of mutation testing. Most specifically the problem of equivalent mutant detection, and the problem of the large number of mutants produced. In this paper we describe the above problems in detail and introduce a new methodology based on co-evolutionary search techniques using Genetic Algorithms in order to address them effectively. Co-evolution allows the parallel evolution of mutants and test cases. We discuss the advantages of this approach over other existing mutation testing techniques, showing details of some initial experimental results carried out

Testability transformation - program transformation to improve testability

Testability transformation is a new form of program transformation in which the goal is not to preserve the standard semantics of the program, but to preserve test sets that are adequate with respect to some chosen test adequacy criterion. The goal is to improve the testing process by transforming a program to one that is more amenable to testing while remaining within the same equivalence class of programs defined by the adequacy criterion. The approach to testing and the adequacy criterion are parameters to the overall approach. The transformations required are typically neither more abstract nor are they more concrete than standard “meaning preserving transformations”. This leads to interesting theoretical questions. but also has interesting practical implications. This chapter provides an introduction to testability transformation and a brief survey of existing results