Comparison of Component Based Testing and Model based Functional Testing through Graphical User Interface

Jaganeshwari, K.; Djodilatchoumy, S.

doi:10.20894/ijwt.104.005.001.020

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Today, the Internet is a great resource for finding courses, seminars, credentials, and other educational activities. The efficiency of the traditional educational strategy still used at universities and other educational institutions has been called into question by this wave of instructional materials and e-learning [5,6]. As a result, these institutions are finding it difficult to redefine and restructure their approaches to offering information and education (Association of European Universities, 1996) [7].…”

Section: Introductionmentioning

confidence: 99%

Predictive Analytics for Sustainable E-Learning: Tracking Student Behaviors

Mudawi,

Pervaiz,

Alabduallah

et al. 2023

Sustainability

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The COVID-19 pandemic has sped up the acceptance of online education as a substitute for conventional classroom instruction. E-Learning emerged as an instant solution to avoid academic loss for students. As a result, educators and academics are becoming more and more interested in comprehending how students behave in e-learning settings. Behavior analysis of students in an e-learning environment can provide vision and influential factors that can improve learning outcomes and guide the creation of efficient interventions. The main objective of this work is to provide a system that analyzes the behavior and actions of students during e-learning which can help instructors to identify and track student attention levels so that they can design their content accordingly. This study has presented a fresh method for examining student behavior. Viola–Jones was used to recognize the student using the object’s movement factor, and a region-shrinking technique was used to isolate occluded items. Each object has been checked by a human using a template-matching approach, and for each object that has been confirmed, features are computed at the skeleton and silhouette levels. A genetic algorithm was used to categorize the behavior. Using this system, instructors can spot kids who might be failing or uninterested in learning and offer them specific interventions to enhance their learning environment. The average attained accuracy for the MED and Edu-Net datasets are 90.5% and 85.7%, respectively. These results are more accurate when compared to other methods currently in use.

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Section: Introductionmentioning

confidence: 99%

Predictive Analytics for Sustainable E-Learning: Tracking Student Behaviors

Mudawi,

Pervaiz,

Alabduallah

et al. 2023

Sustainability

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show abstract

“…However, conventional testing approaches cannot readily accommodate the extensive design space and diverse user interactions that modern applications demand [9,10]. The recurring patterns of user behavior and interactions might be automatically taught by the machine learning algorithms, which would facilitate more complete testing coverage without the need for operator involvement [11,12]. Additionally, the use of ML makes it possible for GUI testing to develop dynamically with the program, reacting to changes in the GUI as well as the associated functionality [13].…”

Section: Introductionmentioning

confidence: 99%

Finite State GUI Testing with Test Case Prioritization Using Z-BES and GK-GRU

Kumar,

Nitin,

Yadav

2023

Applied Sciences

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To deliver user-friendly experiences, modern software applications rely heavily on graphical user interfaces (GUIs). However, it is paramount to ensure the quality of these GUIs through effective testing. This paper proposes a novel “Finite state testing for GUI with test case prioritization using ZScore-Bald Eagle Search (Z-BES) and Gini Kernel-Gated recurrent unit (GK-GRU)” approach to enhance GUI testing accuracy and efficiency. First, historical project data is collected. Subsequently, by utilizing the Z-BES algorithm, test cases are prioritized, aiding in improving GUI testing. Attributes are then extracted from prioritized test cases, which contain crucial details. Additionally, a state transition diagram (STD) is generated to visualize system behavior. The state activity score (SAS) is then computed to quantify state importance using reinforcement learning (RL). Next, GUI components are identified, and their text values are extracted. Similarity scores between GUI text values and test case attributes are computed. Grounded on similarity scores and SAS, a fuzzy algorithm labels the test cases. Data representation is enhanced by word embedding using GS-BERT. Finally, the test case outcomes are predicted by the GK-GRU, validating the GUI performance. The proposed work attains 98% accuracy, precision, recall, f-measure, and sensitivity, and low FPR and FNR error rates of 14.2 and 7.5, demonstrating the reliability of the model. The proposed Z-BES requires only 5587 ms to prioritize the test cases, retaining less time complexity. Meanwhile, the GK-GRU technique requires 38945 ms to train the neurons, thus enhancing the computational efficiency of the system. In conclusion, experimental outcomes demonstrate that, compared with the prevailing approaches, the proposed technique attains superior performance.

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Comparison of Component Based Testing and Model based Functional Testing through Graphical User Interface

Cited by 2 publications

References 4 publications

Predictive Analytics for Sustainable E-Learning: Tracking Student Behaviors

Predictive Analytics for Sustainable E-Learning: Tracking Student Behaviors

Finite State GUI Testing with Test Case Prioritization Using Z-BES and GK-GRU

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