Model-Based Ideal Testing of GUI Programs–Approach and Case Studies

Kılınççeker, Onur; Silistre, Alper; Belli, Fevzi; Challenger, Moharram

doi:10.1109/access.2021.3077518

Cited by 7 publications

(4 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kilincceker et al proposed a test path generation method from an FSM, which is further conversed to a regular expression [27]. In this approach, a Context Table is used, and the source code of the algorithms can be obtained freely [28]. Kilincceker et al also presented an approach for generating test paths using an FSM-based SUT model that is derived from a specification in Hardware Description Language (HDL) language [29] and further transformed to a regular expression, which is an extension of the model by Liu et al mentioned in [30].…”

Section: Related Workmentioning

confidence: 99%

Prioritized Variable-length Test Cases Generation for Finite State Machines

Rechtberger¹,

Bureš²,

Ahmed³

et al. 2022

Preprint

View full text Add to dashboard Cite

Model-based Testing (MBT) is an effective approach for testing when parts of a system-under-test have the characteristics of a finite state machine (FSM). Despite various strategies in the literature on this topic, little work exists to handle special testing situations. More specifically, when concurrently: (1) the test paths can start and end only in defined states of the FSM, (2) a prioritization mechanism that requires only defined states and transitions of the FSM to be visited by test cases is required, and (3) the test paths must be in a given length range, not necessarily of explicit uniform length. This paper presents a test generation strategy that satisfies all these requirements. A concurrent combination of these requirements is highly practical for real industrial testing. Six variants of possible algorithms to implement this strategy are described. Using a mixture of 180 problem instances from real automotive and defense projects and artificially generated FSMs, all variants are compared with a baseline strategy based on an established N-switch coverage concept modification. Various properties of the generated test paths and their potential to activate fictional defects defined in FSMs are evaluated. The presented strategy outperforms the baseline in most problem configurations. Out of the six analyzed variants, three give the best results even though a universal best performer is hard to identify. Depending on the application of the FSM, the strategy and evaluation presented in this paper are applicable both in testing functional and non-functional software requirements.

show abstract

Section: Related Workmentioning

confidence: 99%

Prioritized Variable-length Test Cases Generation for Finite State Machines

Rechtberger¹,

Bureš²,

Ahmed³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In this approach, the context table is used during the generation of the test paths. Details can be derived from the toolchain source code available in a GitHub repository [23]. The tool is available freely for further analysis and comparison with newly developed alternatives.…”

Section: Background and Related Workmentioning

confidence: 99%

Novel Strategy Generating Variable-length State Machine Test Paths

Rechtberger¹,

Bureš²,

Ahmed³

et al. 2022

Preprint

View full text Add to dashboard Cite

Finite State Machine is a popular modeling notation for various systems, especially software and electronic. Test paths can be automatically generated from the system model to test such systems using a suitable algorithm. This paper presents a strategy that generates test paths and allows to start and end test paths only in defined states of the finite state machine. The strategy also simultaneously supports generating test paths only of length in a given range. For this purpose, alternative system models, test coverage criteria, and a set of algorithms are developed. The strategy is compared with the best alternative based on the reduction of the test set generated by the established N-switch coverage approach on a mix of 171 industrial and artificially generated problem instances. The proposed strategy outperforms the compared variant in a smaller number of test path steps. The extent varies with the used test coverage criterion and preferred test path length range from none to two and half fold difference. Moreover, the proposed technique detected up to 30% more simple artificial defects inserted into experimental SUT models per one test step than the compared alternative technique. The proposed strategy is well applicable in situations where a possible test path starts and ends in a state machine needs to be reflected and, concurrently, the length of the test paths has to be in a defined range. KeywordsSystem testing • software testing • internet of things • model-based testing • path-based testing • finite state machines.

show abstract

“…In existing techniques, the GT tools are integrated with different Machine Learning (ML) and Deep Learning (DL) approaches to achieve GT based on TC generation [7,8]. The ML and DL models, including the Fuzzy Inference System (FIS), Long Short Term Memory (LSTM), and Deep Reinforcement Learning (DRL) are developed to perform GT [9].…”

Section: Introductionmentioning

confidence: 99%

An Effective GDP-LSTM and SDQL-Based Finite State Testing of GUI

Kumar,

Nitin,

Yadav

2024

Applied Sciences

View full text Add to dashboard Cite

The Graphical User Interface (GUI) is the most promising factor in the Software Development Lifecycle (SDL), which allows the users to interact with the system. To ensure user-friendliness, GUI Testing (GT) is required. The traditional testing techniques attained flawed results due to having inappropriate functions. Hence, Global Decaying Probabilistic Long Short-Term Memory (GDP-LSTM) and Standard Deviation Q-Learning (SDQL)-based automatic testing for GUI are proposed as solutions. Initially, the Test Case (TC) and GUI are extracted from the historical data and are subjected to Region of Interest (ROI) analysis. Here, an appropriate ROI is analyzed by Module Coupling Slice (MCS), and it is fed into Hadoop Parallelization (HP). Now, Spectral Kernelized Gaussian Clustering (SKGC) and Non-Linear Elite Guided Optimized Ant Colony (NE-GO-AC) are used to perform mapping and reducing, respectively. Likewise, the parallelized output is utilized to construct the Document Object Model (DOM) tree. Then, the attributes are extracted and given to the GDP-LSTM classifier that effectively predicts whether GUIs are desirable or undesirable. Then, the undesirable results are inputted into a SDQL-based deviation analysis. If the deviation is low, it is assumed as an update; otherwise, it is considered as an error. The experimental analysis depicted that the proposed system attained high dominance with 98.89% accuracy in the prevailing models.

show abstract

Model-Based Ideal Testing of GUI Programs–Approach and Case Studies

Cited by 7 publications

References 55 publications

Prioritized Variable-length Test Cases Generation for Finite State Machines

Prioritized Variable-length Test Cases Generation for Finite State Machines

Novel Strategy Generating Variable-length State Machine Test Paths

An Effective GDP-LSTM and SDQL-Based Finite State Testing of GUI

Contact Info

Product

Resources

About