Towards Automated Test Sequence Generation

Farooq, Usman; Lam, Chiou Peng; Li, H.

doi:10.1109/aswec.2008.4483233

Cited by 34 publications

(37 citation statements)

References 27 publications

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“…Four operation scenarios associated with RBC will be modeled using the CPN and the corresponding test cases and sequences will be generated using the proposed path optimization algorithm. Compared with the RW-TSG [14] and traditional DFS algorithms, the numbers of the test cases and sequences will be seen as the indicators showing the superiority of the proposed algorithms.…”

Section: Application Of the Test Generation Algorithmmentioning

confidence: 99%

Automated Test Sequence Optimization Based on the Maze Algorithm and Ant Colony Algorithm

Zheng

Hu²

2015

INT J COMPUT COMMUN

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With the rapid development of China train operation and control system, validity and safety of behavioral functions of the system have attracted much attention in the railway domain. In this paper, an automated test sequence optimization method was presented from the system functional requirement specification of the high-speed railway. To overcome the local optimum of traditional ant colony algorithm, the maze algorithm is integrated with the ant colony algorithm to achieve the dynamical learning capacity and improve the adaptation capacity to the complex and changeable environment, and therefore, this algorithm can produce the optimal searching results. Several key railway operation scenarios are selected as the representative functional scenarios and Colored Petri Nets (CPN) is used to model the scenarios. After the CPN model is transformed into the extensible markup language (XML) model, the improved ant colony algorithm is employed to generate the optimal sequences. The shortest searching paths are found and the redundant test sequences are reduced based on the natural law of ants foraging. Finally, the Radio Blocking Center (RBC) test platform is designed and used to validate the optimal sequence. Testing results show that the proposed method is able to optimize the test sequences and improve the test efficiency successfully.

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Section: Application Of the Test Generation Algorithmmentioning

confidence: 99%

Automated Test Sequence Optimization Based on the Maze Algorithm and Ant Colony Algorithm

Zheng

Hu²

2015

INT J COMPUT COMMUN

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“…a UML 2.0 AD based branch coverage criteria as defined in [7]. The columns, for example, 'e2' and 'e3', indicate the branches in the model.…”

Section: B Illustrated Examplementioning

confidence: 99%

“…Unfortunately, a test suite generated using a stochastic technique with this property contains many redundant test cases. To illustrate the problem further, a test suite is generated as shown in table-1 using a stochastic test sequence generation (TSG) algorithm proposed in [7] for an example model as shown in figure-1.…”

Section: B Illustrated Examplementioning

confidence: 99%

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Evolving the Quality of a Model Based Test Suite

Farooq¹,

Lam²

2009

2009 International Conference on Software Testing, Verification, and Validation Workshops

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Abstract-Redundant test cases in newly generated test suites often remain undetected until execution and waste scarce project resources. In model-based testing, the testing process starts early on in the developmental phases and enables early fault detection. The redundancy in the test suites generated from models can be detected earlier as well and removed prior to its execution. The article presents a novel model-based test suite optimization technique involving UML Activity Diagrams by formulating the test suite optimization problem as an Equality Knapsack Problem. The aim here is the development of a test suite optimization framework that could optimize the model-based test suites by removing the redundant test cases. An evolution-based algorithm is incorporated into the framework and is compared with the performances of two other algorithms. An empirical study is conducted with four synthetic and industrial scale Activity Diagram models and results are presented.

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“…An objective of this exploration is to present a designer with confirmation that the diagrams in a system model include sufficient information for automatically generating a suite of test cases by sequence based test criteria generation [4]. There are two major aspects of the proposed study, which will be explored in five phase process.…”

Section: Introductionmentioning

confidence: 99%