Model-based Test Suite Research Articles

Efficient increasing of the mutation score during model-based test suite generation

The purpose of the method is to increase the sensitivity of an automatically generated test suite to mutations of a model. Unlike existing methods for generating test scenarios that use the mutational approach to assess the resulting test set, the proposed method analyzes the possibility of detecting mutations on the fly, in the process of analyzing the model’s behavior space, by adding of special coverage goals. Two types of mutants manifestation are considered: deviations in the behavior of paths for (weak case) and in the observed output (strong case). A new algorithm is proposed for efficient search of a path with observable effect of a mutation.

Enhanced Adaptive Random Test Case Prioritization for Model-based Test Suites

Enhanced Adaptive Random Test Case Prioritization for Model-based Test Suites

Model-based test suite generation for graph transformation system using model simulation and search-based techniques

Abstract Context Test generation by model checking is a useful technique in model-based testing that allows automatic generation of test cases from models by utilizing the counter-examples/witnesses produced through a model checker. However, generating redundant test cases and state space explosion problem are two major obstacles to transfer this technique into industrial practice. Objective An idea to cope with these challenges consists in an intelligent model checking for exploring only a portion of the state space according to the test objectives. Motivated by this idea, we propose an approach that exploits meta-heuristic algorithms to adapt a model checker when used for integration testing of systems formally specified by graph transformations. Method This method is not based on model checking algorithms, but rather uses the modeling and simulation features of the underlying model checker. In the proposed approach, a population of test suites that each of which is a set of paths on the state space, is evolved towards satisfying the all def-use test objectives. Consequently, a test suite with high coverage is generated. Results To assess the efficiency of our approach, it is implemented in GROOVE, an open source toolset for designing and model checking graph transformation systems. Empirical results based on some case studies, confirm a significant improvement in terms of coverage, speed and memory usage, in comparison with the state of the art techniques. Conclusion Our analysis reveals that intelligent model checking can appropriately address the challenges of traditional model-checking-assisted testing. We further conclude that graph transformation specification is an efficient modeling solution to behavioral testing and graph transformation tools have a great potential for developing a model-based testing tool.

A NOVEL PRIORITIZATION ALGORITHM MODEL BASED TEST-SUITE GENERATION USING REGRESSION TESTING

The fully automatic Graphical User Interface tool for any application using novel model based test suite generation techniques for a GUI. They are unable to control response time and time intervals are based on relationship between GUI events handlers and test cases with their responsibilities. We present a novel prioritization algorithm that enhances event handlers for the automated GUI tool. The proposed tool generates GUI events, it Captures and Playback event responses to automatic verification point of the results for the test cases which are written to a log file and corresponding report will be generated. This novel algorithm was able to detect new test suite and ordering of test cases to reduce a GUI fault integration defects. The number of faults detected for a single event are found after generating test cases for the application. The Average Percentage of Fault Detection (APFD) and charts has been used to show the effectiveness of proposed algorithm to find fault detection rate.

Incremental Model-based Test Suite Reduction with Formal Concept Analysis

Abstract —Test scenarios can be derived based on some system models for requirements validation purposes. Model-based test suite reduction aims to provide a smaller set of test scenarios which can preserve the original test coverage with respect to some testing criteria. We are proposing to apply Formal Concept Analysis (FCA) in analyzing the association between a set of test scenarios and a set of transitions specified in a state machine model. By utilizing the properties of concept lattice, we are able to determine incrementally a minimal set of test scenarios with adequate test coverage. Keywords —Test Suite Reduction, Model-based Testing, State Machine Model, Formal Concept Analysis 1. I NTRODUCTION Test scenarios can be derived based on some system models for requirements validation pur-poses. Model-based testing [1, 16] refers to deriving a suite of test scenarios from a model that represents the behavior of a software system. In particular, state machine model has been widely used for this purpose in testing event-driven, reactive systems, and embedded software systems [1, 2]. State machine model can be used to specify the dynamic perspective of a system and its interactions with the users through sequences of transitions. The sequences of transitions can form a set of test scenarios for validation of functional requirements by test engineers and end users. However, since cycles in the state machine model may lead to an infinite number of test scenarios, exhaustive testing is usually not possible. Moreover, many test scenarios are part of some other test scenarios and thus lead to redundancy in the test suite. Model-based test suite reduction can be applied in this situation and derive a smaller set of test scenarios which still preserves the original test coverage with respect to some testing criteria. A default criterion of adequate testing with a state machine model is all-transition coverage criterion [1, 11, 16], which means each transition specified in the state machine model should be triggered at least once by executing the test scenarios. In this paper, we shall describe an incremental approach for reducing model-based test suite using Formal Concept Analysis (FCA) [6]. FCA is a mathematical technique for formulating concepts in terms of a set of formal objects and their associated formal attributes, and providing