Goal-oriented test data generation for pointer programs

Abstract

Goal-oriented test data generation; Constraint Logic Programming; Static Single Assignment formInternational audienceAutomatic test data generation leads to the identification of input values on which a selected path or a selected branch is executed within a program (path-oriented vs goal-oriented methods). In both cases, several approaches based on constraint solving exist, but in the presence of pointer variables only path-oriented methods have been proposed. Pointers are responsible for the existence of conditional aliasing problems that usually provoke the failure of the goal-oriented test data generation process. In this paper, we propose an overall constraint-based method that exploits the results of an intraprocedural points-to analysis and provides two specific constraint combinators for automatically generating goal-oriented test data. This approach correctly handles multi-levels stack-directed pointers that are mainly used in C programs. The method has been fully implemented in the test data generation tool INKA and first experiences in applying it to a variety of existing programs are presented

Constraint-Based Test Input Generation for Java Bytecode

“…Suppose that the labeling process has instantiated speed to 363 and this.y to 5206 using a random labeling heuristic, then we get the following refined input CMV: 4 , operand stacks and heap data. This section details the representation of data types and data storage locations in our memory model.…”

“…There are many approaches of automatic test input generation from source code. In our previous works [4], [18], we built memory models for constraint-based test data generator of C programs. Our previous model handled pointers toward named locations of the memory and dynamically allocated structures but they were limited in their scope.…”

“…Our previous model handled pointers toward named locations of the memory and dynamically allocated structures but they were limited in their scope. Unlike JAUT, the model of [4] did not contain representation of the heap and then it was unable to deal with dynamic memory allocation. The model of [18] was very complex and the generation took too much time.…”

“…Constraint-based testing, as introduced by Offutt in 1991 [1], combined symbolic execution and dynamic constraint solving [2] to generate test inputs able to execute specific paths in the code. It was then adapted for C programs and refined with standard constraint programming techniques to target specific locations in the code regardless of the particular path executed [3], [4]. Constraint-based testing was extended to the exhaustive generation of structured inputs for Java programs [5], [6] and the constraint solving of structurally complex constraints [7].…”

Improvement of the Baseline Method in Structural Testing

“…There are two basic methods which can be used to identify test cases, functional testing (also known as black-box testing) [1][2][3] and structural testing (also known as white-box testing) [4][5][6]. Therefore, the technology of test data generation is usually divided into function-oriented [7,8] and structure-oriented [9,10] test data generation technology.…”