(AIAM2019) Artificial Intelligence in Software Engineering and inverse: Review

Shehab, Mohammad; Abualigah, Laith; Jarrah, Muath; Alomari, Osama Ahmad; Daoud, Mohammad Sh.

doi:10.1080/0951192x.2020.1780320

Cited by 23 publications

(14 citation statements)

References 74 publications

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“…Intelligent SE is a new field with two goals creating intelligent solutions for SE and creating SE solutions for AI [13]. Complementarity between AI and SE presents opportunity for hybridizing AI techniques and SE processes [81]. Transdisciplinary innovation and methods accelerate divergent thinking.…”

Section: Towards a Transdisciplinarity Ai-se Trans-fieldmentioning

confidence: 99%

“…Transdisciplinary innovation and methods accelerate divergent thinking. Transdisciplinarity improves creating minimum viable products by software startups through enabling them navigate challenges [81] like lack of capital, manpower and saleable product. Single owner software startups have to be transdisciplinary since the owner performs all functions in relevant disciplines of: SE, innovation, business and accounting best handled by unifying knowledge.…”

Section: Towards a Transdisciplinarity Ai-se Trans-fieldmentioning

confidence: 99%

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Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

Mambo¹

2022

tjes

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Study examined AI and SE transdisciplinarity to find ways of aligning them to enable development of AI-SE transdisciplinary theory. Literature review and analysis method was used. The findings are AI and SE transdisciplinarity is tacit with islands within and between them that can be linked to accelerate their transdisciplinary orientation by codification, internally developing and externally borrowing and adapting transdisciplinary theories. Lack of theory has been identified as the major barrier toward towards maturing the two disciplines as engineering disciplines. Creating AI and SE transdisciplinary theory would contribute to maturing AI and SE engineering disciplines. Implications of study are transdisciplinary theory can support mode 2 and 3 AI and SE innovations; provide an alternative for maturing two disciplines as engineering disciplines. Study’s originality it’s first in SE, AI or their intersections.

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Section: Towards a Transdisciplinarity Ai-se Trans-fieldmentioning

confidence: 99%

Section: Towards a Transdisciplinarity Ai-se Trans-fieldmentioning

confidence: 99%

Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

Mambo¹

2022

tjes

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“…Many articles, reviews, books, conference proceedings are devoted to the use of Artificial Intelligence (AI) in software engineering in general. (e.g., Barenkamp et al, 2020;Batarseh et al, 2021;Shehab et al, 2020). The application of AI techniques covers all stages of software life-circle: software project planning, problem analysis, software design, software implementation, software testing and integration, and software maintenance.…”

Section: Application Of Artificial Intelligence To the Software Devel...mentioning

confidence: 99%

Transcendental Logic-Based Formalism for Semantic Representation of Software Project Requirements Architecture

Moroz¹,

Pysarchuk²,

Konrad³

2022

CIS

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This article is devoted to the analysis of the situation that has arisen in the practice of using artificial intelligence methods for software development. Nowadays there are many disparate approaches, models, and practices based on the use of narrow intelligence for decision-making at different stages of the life cycle of software products, and an almost complete lack of solutions brought to wide practical use. The article provides a comprehensive overview of the main reasons for the lack of the expected effect from the implementation of Agile and suggests a way to solve this problem based on the use of a self-organizing knowledge model. Based on the heuristic usage of transcendental logic in the terms of "ontological predicates", such a model makes it possible to create a formalism of the semantic representation of the requirements architecture of a software project, which could provide semantic interoperability and an executable semantic framework for automated ontology generation from unstructured informal software requirements text. The main benefit of this model is that it is flexible and ensures the accumulation of knowledge without the need to change the initial infrastructure as well as that the ontology inference engine is the part of the mechanism of collective interaction of active elements of knowledge and not some externally programmed system of rules that imitate the process of thinking.

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“…The concepts that are easily confused with DL include artificial intelligence (AI) and machine learning (ML). AI is a technical science that researches and develops theories, methods, technologies and application systems for simulating and expanding human intelligence (Shehab 2020). The object of its research and development is 'theory, technology and application system', and the purpose of the research is to 'simulate and expand human intelligence'.…”

Section: Deep Learning Machine Learning and Artificial Intelligencementioning

confidence: 99%

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Liu

Bao

Wang

et al. 2021

International Journal of Computer Integrated Manufacturing

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Deep learning (DL) is an important enabling technology for intelligent manufacturing. The DLbased industrial image pattern recognition (DLBIIPR) plays a vital role in the improvement of product quality and production efficiency. Although DL technology has been widely used in the field of natural image, industrial image often has some mixed characteristics, such as small sample, imbalance, small target, strong interference, fine-grained, temporality and semantical, which reduce the feasibility and generalization of DLBIIPR. To solve this problem, this paper provides an overview of approaches commonly used in industry by enriching the sample space and limiting the hypothesis space. In order to improve the confidence of front-line workers in using DL models, the explainable deep learning (XDL) methods are reviewed, and a case study is used to verify the effectiveness of XDL.

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(AIAM2019) Artificial Intelligence in Software Engineering and inverse: Review

Cited by 23 publications

References 74 publications

Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

Transcendental Logic-Based Formalism for Semantic Representation of Software Project Requirements Architecture

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

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