Using CLP to Automatically Generate Test Sequences for Synchronous Programs with Numeric Inputs and Outputs

Abstract

Lutess is a testing environment designed for synchronous software specified with Lustre, a synchronous data-flow language. It makes possible to automatically generate test input sequences in conformance with a specification of the software external behavior and of guiding directives such as operational profiles and behavioral patterns. Lutess deals with software and specifications involving only boolean inputs and outputs. In this paper we propose an extension of Lutess, using Constraint Logic Programming (CLP), making possible to deal with numeric inputs and outputs. In particular, we define an appropriate execution model for test input generation and show how test data generation according to the main guiding facilities of Lutess can still be performed. Furthermore, operational profile based generation becomes more powerful thanks to the introduction of CLP solvers capabilities which make possible to associate occurrence probabilities to any boolean expression.

Synchronous Programs Testing Language (SPTL)

“…-Lutess [9] has been designed as a black-box testing environment that automatically generates test input sequences from a formal specification of the software external behavior provided in a language similar to Lustre. Lutess requires three components: the software environment description (1), the executable code of the system under test (2), and a test oracle (3) describing the system requirements (see Figure 1).…”

Analysis of Testability Metrics for Lustre/Scade Programs

“…We have generated 20 testing sequences of 10 000 steps randomly with Lutess testing tool [22]. We have executed each testing sequence on each mutant.…”

Modelling Dynamic Memory Management in Constraint-Based Testing

“…Constraint-Based Testing (CBT) was introduced fifteen years ago in the context of mutation testing [1] to model processes of test case generation using constraint solving techniques. Since then it has been continuously developed to cover several applications area including hardware verification [2,3], test data generation for structural testing [4,5,6,7] and functional testing [8,9], counterexample generation [10,11], or software verification [12,4,8]. Among the tools that implement the CBT approach, InKa [13,14], ATGen [15] and PathCrawler [5] are automated test data generators based on constraint propagation over finite domains.…”