Automated test data generation using an iterative relaxation method

Abstract

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 on the path to evaluate to the desired outcome.When the branch conditions on a path are linear functions of input variables, our technique either finds a solution for such paths in one iteration or it guarantees that the path is infeasible. In contrast, existing execution based approaches may require an unacceptably large number of iterations for relatively long paths because they consider only one input variable and one branch predicate at a time and use backtracking. When the branch conditions on a path are nonlinear functions of input variables, though it may take more then one iteration to derive a desired input, the set of constraints to be solved in each iteration is linear and is solved using Gaussian elimination. This makes our technique practical and suitable for automation.

Relevant inputs analysis and its applications

“…While static analysis techniques are aimed at identifying properties that hold during all executions of a program, our work is aimed at analyzing the flow of input values during a particular execution. Dynamic analysis is much more suitable for applications such as debugging [11], test input generation [14], [17], and security [4]. As mentioned earlier, the most closely-related effort to our relevant input analysis is lineage tracing [22], [23], [24].…”

“…Test Input Generation: Dynamic techniques require multiple program executions for generating desired test inputs [14]. The cost of test input generation can be reduced by using symbolic execution [15] for Java programs.…”

“…A variety of dynamic analysis techniques have been developed to assist with these tasks (e.g., dynamic slicing [5], [8], delta debugging [10], [11], [13], generating test inputs [14], [15], [16], [17], information flow [18], [19]). In these tasks, understanding the role that input values play during execution can be very useful in understanding program behavior.…”

High-Coverage Symbolic Patch Testing

“…They start with an initial random guess, and then iteratively refine the guess to discover a path likely to hit the desired statement. Gupta et al [14] use a combination of static analysis and generated test cases to hit a specified path. They define a loss function consisting of "predicate residuals" which roughly measures by "how much" the branch conditions for that path were not satisfied and then use a numerical solver to find test case values that can trigger the given path.…”

Java-HCT: An approach to increase MC/DC using Hybrid Concolic Testing for Java programs

“…Manually created test cases are expensive, error-prone, and generally not exhaustive [2]. Therefore, automated software testing techniques have been discovered [3], [4].…”