Using artificial intelligence to automatically test GUI

Rauf, Abdul; Alanazi, Mohammad N.

doi:10.1109/iccse.2014.6926420

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…Software testing consumes a substantial amount of total software development resources and time. It is estimated that around 40%-50% available resources and almost 50% of development time is invested in software testing [151]. AI methods which are fueled by two parts, the data and the algorithm, pose a great candidate for effective and intelligent optimization of software testing.…”

Section: Software Testing (Validation and Verification)mentioning

confidence: 99%

“…AI planners began generating test cases, providing initial states, and setting the goal as testing for correct system behavior. In [151], Annealing Genetic Algorithm (AGA) and Restricted Genetic Algorithm (RGA), two modified versions of Genetic Algorithm (GA) have been applied to testing. Sangeetha et al [153] presented applications of GA through white box testing: Control Flow Testing, GA based Data Flow testing, and Particle Swarm Optimization (PSO) based testing.…”

Section: Software Testing (Validation and Verification)mentioning

confidence: 99%

“…The technique includes detecting application's reaction to mouse events, keyboard events, and reaction of components such as buttons, dialogs, menu bars, images, toolbars and text fields towards user input. Rauf [151] proposed how Graphical User Interface (GUI) can derive benefits from the use of AI techniques. The technique includes automating test case generation so that tests are also re-generated each time GUI changes.…”

Section: Figure 13: Ifn Based Black-box Testing [164]mentioning

confidence: 99%

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The application of artificial intelligence in software engineering

et al. 2020

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Section: Software Testing (Validation and Verification)mentioning

confidence: 99%

Section: Software Testing (Validation and Verification)mentioning

confidence: 99%

Section: Figure 13: Ifn Based Black-box Testing [164]mentioning

confidence: 99%

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The application of artificial intelligence in software engineering

et al. 2020

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“…Rauf et al [18] discussed the issues and challenges related to testing applications with a user interface. According to the authors, it becomes difficult to address the large number of states of GUI.…”

Section: Related Workmentioning

confidence: 99%

“…The testing can be platform-specific and thus shows a limitation. The methods prescribed and used in [18] did not address the problem of a huge number of states that even a small application's UI can have and thus can lead to a number of test cases. This paper has used particle swarm optimization (PSO), partition testing based on particle swarm optimization (PSO), test case minimization using an artificial neural network (ANN), Bayesian Network (BN).…”

Section: Related Workmentioning

confidence: 99%

A Case Study of Testing an Image Recognition

Tao¹

2021

JVLC

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High-quality Artificial intelligence (AI) software in different domains, like image recognition, has been widely emerged in people's daily life. They are built on machine learning models to implement intelligent features. However, the current research on image recognition software rarely discusses test questions, clear quality requirements, and evaluation methods. The quality of image recognition applications becomes more and more prominent. A three-dimensional(3D) classification decision table can help users to conduct classification-based test requirement analysis and modeling for any given mobile apps powered with AI functions in detection, classification, and prediction. This paper presents a case study of a realistic image recognition application called Calorie Mama using manual testing and automation testing with a 3D decision table. The study results indicate the proposed method is feasible and effective in quality evaluation.

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Integration of properties of virtual reality, artificial neural networks, and artificial intelligence in the automation of software tests: A review

Serna

Acevedo

Serna

2019

J Software Evolu Process

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The complete automation of software tests has been considered to be an unattainable goal. This article discusses the potential to achieve this goal with recent discoveries and innovations in the areas of virtual reality (VR), artificial neural networks (ANNs), and artificial intelligence (AI). In this study, a theoretical proposal is described to integrate the properties of each of these areas using a process of automation of software tests. This process is based on a classification and description of the properties after consulting the literature, interviews, and dialogs with specialists from Australia, the United States, Germany, and Colombia. In addition to the experiences of the researchers, the construction of two tools is proposed: (1) a robot to design and apply functional tests, and (2) a virtual machine to identify errors in the logical structure of the code. Both tools are expected to replace human factors; the advantage is that the first tool identifies procedural flaws and the second errors of operation. KEYWORDS artificial intelligence, artificial neural networks, automation, software testing, virtual reality 1 | INTRODUCTION For decades, why has one of the objectives of research in computer science been to automate the testing of software? The reality is that our society is software dependent. 1 Due to the current reality of technological development and the increasing consumption of this product, the demand for improvement in software quality has increased. In addition, the rate of software development has increased. This situation has caused industry to rethink its work methodologies to respond to the demands of society and the market, and satisfy the needs of customers and the current demand. However, industry hopes to achieve this goal without investing an excessive amount of time and money.The scenario becomes increasingly competitive as globalization and the free market offer opportunities to acquire software in any country in the world. Industry must verify and validate each system to evaluate its quality. A set of test cases is designed and manually applied, which involves time-consuming repetitive functions. 2 Automation is used to accelerate processes and reduce costs because the test can provide greater coverage. Another advantage of automation is that it can be executed without supervision with minimal human intervention, at times outside of business hours. In this manner, productivity in the area of quality control is increased, whereas a product can be delivered in less time with cost savings.Despite decades of research, experimentation, and application, software tests are not fully automated. Although progress has been made and powerful tools are available in the market for this purpose, progress is minimal as society demands quality, reliability, and product safety. Industry attributes this situation to the cost of automation, the lack of qualified personnel, and the ambiguity of the requirements specification documents because they generate delays in development and sufficient time ...

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Using artificial intelligence to automatically test GUI

Cited by 11 publications

References 10 publications

The application of artificial intelligence in software engineering

The application of artificial intelligence in software engineering

A Case Study of Testing an Image Recognition

Integration of properties of virtual reality, artificial neural networks, and artificial intelligence in the automation of software tests: A review

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