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This paper presents a technique that uses a genetic algorithm for automatic test-data generation. A genetic algorithm is a heuristic that mimics the evolution of natural species in searching for the optimal solution to a problem. In the test-data generation application, the solution sought by the genetic algorithm is test data that causes execution of a given statement, branch, path, or definition-use pair in the program under test. The test-data-generation technique was implemented in a tool called TGen in which parallel processing was used to improve the performance of the search. To experiment with TGen, a random test-data generator, called Random, was also implemented. Both TGen and Random were used to experiment with the generation of test-data for statement and branch coverage of six programs.

This paper presents a dynamic technique for statistically estimating three program characteristics that affect a program's computational behavior: (1) the probability that a particular section of a program is executed, (2) the probability that the particular section affects the data state, and (3) the probability that a data state produced by that section has an effect on program output. These three characteristics can be used to predict whether faults are likely to be uncovered by software testing.

This paper discusses the use of genetic algorithms (GAs) for automatic software test data generation. This research extends previous work on dynamic test data generation where the problem of test data generation is reduced to one of minimizing a function [Miller and Spooner, 1976, Korel, 1990]. In our work, the function is minimized by using one of two genetic algorithms in place of the local minimization techniques used in earlier research. We describe the implementation of our GA-based system, and examine the effectiveness of this approach on a number of programs, one of which is significantly larger than those for which results have previously been reported in the literature. We also examine the effect of program complexity on the test data generation problem by executing our system on a number of synthetic programs that have varying complexities. 1 Introduction An important aspect of software testing involves judging how well a series of test inputs tests a piece of code. Usuall...

One of the major costs in a software project is the construction of test-data. This paper outlines a generalised test-case data generation framework based on optimisation techniques. The framework can incorporate a number of testing criteria, for both functional and non-functional properties. Application of the optimisation framework to testing specification failures and exception conditions is illustrated. The results of a number of small case studies are presented and show the efficiency and effectiveness of this dynamic optimisation-base approach to generating test-data. 1.1 Keywords Automatic test-case generation, software testing, formal specifications, exception conditions, optimisation techniques, simulated annealing. 2

An important problem that arises in path oriented testing is the generation of test data that causes a program to follow a given path. In this paper, we present a novel program execution based approach using an iterative relaxation method to address the above problem. In this method, test data generation is initiated with an arbitrarily chosen input from a given domain. This input is then iteratively refined to obtain an input on which all the branch predicates on the given path evaluate to the desired outcome. In each iteration the program statements relevant to the evaluation of each branch predicate on the path are executed, and a set of linear constraints is derived. The constraints are then solved to obtain the increments for the input. These increments are added to the current input to obtain the input for the next iteration. The relaxation technique used in deriving the constraints provides feedback on the amount by which each input variable should be adjusted for the branches o...

Structural testing criteria are mandated in many software development standards and guidelines. The process of generating test-data to achieve 100 % coverage of a given structural coverage metric is labour intensive and expensive. This paper presents an approach to automate the generation of such test-data. The test-data generation is based on the application of a dynamic optimisation-based search for the required test-data. The same approach can be be generalised to solve other test-data generation problems. Three such applications are discussed { boundary value analysis, assertion/run-time exception testing and component re-use testing. Aprototype tool-set has been developed to facilitate the automatic generation of test-data for these structural testing problems. The results of preliminary experiments using this technique and the prototype tool-set are presented and show the e ciency and e ectiveness of this approach. 1

Model checking of software programs has two goals: one is the veri cation of correct software. The other is the discovery of errors in faulty software. Some techniques for dealing with the most crucial problem in model checking, the state space explosion problem, concentrate on the rst of these goals. In this paper we present an array of heuristic model checking techniques for combating the state space explosion when searching for errors. Previous work on this topic has mostly focused on property-speci c heuristics closely related to particular kinds of errors. We present structural heuristics that attempt to explore the structure (branching structure, thread inter-dependency structure, abstraction structure) of a program in a manner intended to expose errors eciently. Experimental results show the utility of this class of heuristics. In contrast to these very general heuristics, we also present very lightweight techniques for introducing program-speci c heuristic guidance. 1

Branch coverage is an important criteria used during the structural testing of programs. In this paper, we present a new program execution based approach to generate input data that exercises a selected branch in a program. The test data generation is initiated with an arbitrarily chosen input from the input domain of the program. A new input is derived from the initial input in an attempt to force execution through any of the paths through the selected branch. The method dynamically switches among the paths that reach the branch by refining the input. Using a numerical iterative technique that attempts to generate an input to exercise the branch, it dynamically selects a path that offers less resistance. We have implemented the technique and present experimental results of its performance for some programs. Our results show that our method is feasible and practical. Keywords - Path testing, branch testing, iterative relaxation technique, testing tools. 1 Introduction An important t...

this paper, we first describe the current Aristotle system, including its overall architecture and the tools and components that constitute its forthcoming release. We then discuss lessons that we have learned about system 1