Efficient coverage of parallel and hierarchical stateflow models for test case generation

Abstract

This paper is concerned with test case generation from Simulink/Stateflow (SL/SF) models with a focus on coverage of SF model elements. Coverage of the SF component in a model is a difficult task because of two primary reasons: (i) the SF component itself may lie deep in the SL/SF model in which case, inputs have to pass through a complex chain of SL blocks to reach the SF block and (ii) nonlinear constraints in the model are difficult to solve using constraint solvers. Hierarchy and parallelism in the SF model add further complexity to the problem. The existing approaches flatten such SF elements, and generate test cases from the flattened finite state machines. Handling of issues (i) and (ii) has already been discussed in earlier research. In this paper, we present a method of covering SF components, which does not require to flatten any hierarchy or parallelism in the components. This not only makes the test case generation problem efficient but also addresses the problem of scalability. We have implemented this method and performed a number of medium-sized case studies. The results show improved performance over the results obtained by some commercial tools.

Automated test suite generation for time-continuous simulink models

“…A number of model-based testing techniques have been applied to Simulink models with the aim of achieving high structural coverage or detecting a large number of mutants. For example, search-based approaches [54,55], reachability analysis [33,21], guided random testing [43,44], and a combination of these techniques [50,39,42,36,20,8] have been previously applied to Simulink models to generate coverage-adequate test suites. Alternatively, various search-based [61,62] and bounded reachability analysis [9] techniques have been used to generate mutant-killing test suites from Simulink models.…”

“…The existing approaches to testing and verifying Simulink models almost entirely focus on models with time-discrete behavior, i.e., code generation models. These approaches generate discrete test inputs for Simulink models with the goal of reaching runtime errors to reveal faults [50,23], violating assertions inserted into Simulink models based on some formal specification [41,14], and achieving high structural coverage [36,42]. Discrete test inputs, however, are seldom sufficient for testing Simulink models, in particular, for those models with time-continuous behaviors.…”

Improving fault localization for Simulink models using search-based testing and prediction models

“…RELATED WORK Many test generation techniques have been proposed for different purposes e.g., maximizing program coverage ( [22], [39]- [52]) and revealing faults ( [43], [44], [53]- [64]) for programs, or maximizing structural coverage [65]- [74] and revealing faults [19], [28], [75]- [83] for Simulink models. Nevertheless, only a few test generation techniques aim to improve fault localization accuracy.…”

Effective test suites for mixed discrete-continuous stateflow controllers

“…These model-based testing approaches often generate test cases from models using various automation mechanisms, e.g., search-based techniques [50], model checking [27,42], guided random testing [37,33,11] or a combination of these techniques [36,30]. In [31], a model-based testing approach for mixed discrete-continuous Stateflow models is proposed where test inputs are generated based on discrete fragments of Stateflow models, and are applied to the original models to obtain test oracles in terms of continuous signals.…”