Constraint Patterns and Search Procedures for CP-Based Random Test Generation

Abstract

Constraint Programming (CP) technology has been extensively used in Random Functional Test Generation during the recent years. However, while the existing CP methodologies are well tuned for traditional combinatorial applications e.g. logistics or scheduling, the problem domain of functional test generation remains largely unexplored by the CP community and many of its domain specific features and challenges are still unaddressed. In this paper we focus on the distinctive features of CP for the random functional test generation domain and show how these features can be addressed using a classical CP engine with custom extensions. We present some modeling and solving problems arising in this context and propose solutions. In particular, we address the way of model building in the problem domain of test generation which we refer to as multi-layer modeling. In this context we introduce constraint patterns of composite variable, implied condition and implied composite variable condition, define their semantics and propose schemes for their CSP modeling. The paper also addresses specific problems arising at the solving stage in the problem domain of random test generation. We propose solutions to these problems by means of custom random search algorithms. This approach is illustrated on the examples of the disjunction constraint and conditional variable instantiation. The latter algorithm addresses the feature of dynamic modeling required in the test generation task. To demonstrate the effectiveness of our approach we present experimental results based on the implementation using ILOG Solver as a CP engine.