AI for Testing Today and Tomorrow: Industry Perspectives

King, Tariq M.; Arbon, Jason; Santiago, Dionny; Adamo, David Tuesday; Chin, Wendy; Shanmugam, Ramalingam

doi:10.1109/aitest.2019.000-3

Cited by 39 publications

(17 citation statements)

References 6 publications

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“…The fuzzing results are summarized in the form of an output protocol [76]. Xie et al [78] and Liang et al [79] use deep neural networks (DNN) to combine several error routines in order to identify complex code defects. The DNN adapts to program reactions in a metamorphic way in order to identify rare and linked errors and systematically enhance code quality.…”

Section: Ai At the Stage Of Software Testing And Integrationmentioning

confidence: 99%

“…Although automated AI based testing and integration functions today are self-improving and use dynamically changing routines, to date human coders are required to define the testing process and requirements to the program, while the test implementation can be done by the machine. A survey among 328 experts comes to the conclusion that about 35% assume that a complete substitution of human programmers by machines in the testing phase will never be possible [79]. AI however abbreviates the testing process and saves manpower to perform, document and evaluate the tests.…”

Section: Ai At the Stage Of Software Testing And Integrationmentioning

confidence: 99%

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Applications of AI in classical software engineering

Barenkamp¹,

Rebstadt

Thomas

2020

AI Perspect

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Although Artificial Intelligence (AI) has become a buzzword for self-organizing IT applications, its relevance to software engineering has hardly been analyzed systematically. This study combines a systematic review of previous research in the field and five qualitative interviews with software developers who use or want to use AI tools in their daily work routines, to assess the status of development, future development potentials and equally the risks of AI application to software engineering. The study classifies the insights in the software development life cycle. The analysis results that major achievements and future potentials of AI are a) the automation of lengthy routine jobs in software development and testing using algorithms, e.g. for debugging and documentation, b) the structured analysis of big data pools to discover patterns and novel information clusters and c) the systematic evaluation of these data in neural networks. AI thus contributes to speed up development processes, realize development cost reductions and efficiency gains. AI to date depends on man-made structures and is mainly reproductive, but the automation of software engineering routines entails a major advantage: Human developers multiply their creative potential when using AI tools effectively.

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Section: Ai At the Stage Of Software Testing And Integrationmentioning

confidence: 99%

Section: Ai At the Stage Of Software Testing And Integrationmentioning

confidence: 99%

Applications of AI in classical software engineering

Barenkamp¹,

Rebstadt

Thomas

2020

AI Perspect

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“…AI software testing is different from traditional software testing, because AI software is characterized by dependence on big data, difficulty in predicting all application scenarios, and constant self-learning from past behavior. King et al [4] discussed the issues and challenges in software testing. According to the authors, non-determinism is a huge issue.…”

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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“…Artificial intelligence (AI) can lower these figures, and the associate human capital and efficiency costs that cybersecurity teams face, in three ways (later, we shall refer to them as the 3R: robustness, response, and resilience). First, AI can improve a system's robustness, that is, the capacity of a system to keep behaving as expected even when it processes erroneous inputs, thanks to selftesting and self-healing software 6 . Second, AI can advance a system's response, that is, the capacity of a system to defeat an attack autonomously, refine future strategies on the basis of the achieved success, and possibly launch more aggressive counter operations with each iteration 7 .…”

mentioning

confidence: 99%