Artificial intelligence and expert systems in-flight software testing

DeMasie, M.P.; Muratore, John F.

doi:10.1109/dasc.1991.177202

Cited by 2 publications

(1 citation statement)

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“…Besides, other methods have been used to test AI software. The testing methods of RAP (Reconfiguration Automation Project) and FEID timeline set up were discussed in [21]. The automation of the several phases of the flight software testing procedure is the actual idea behind RAP.…”

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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Section: Related Workmentioning

confidence: 99%

A Case Study of Testing an Image Recognition

Tao¹

2021

JVLC

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Artificial Intelligence in Software Engineering

Meziane

Vadera

2010

Advances in Computational Intelligence and Robotics

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Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.

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Artificial intelligence and expert systems in-flight software testing

Cited by 2 publications

References 0 publications

A Case Study of Testing an Image Recognition

A Case Study of Testing an Image Recognition

Artificial Intelligence in Software Engineering

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