A critical look at the relationship between AI and software engineering

Tsai, Wei‐Tek; Heisler, K.G.; Volovik, D.; Zualkernan, Imran A.

doi:10.1109/lfa.1988.24945

Cited by 26 publications

(3 citation statements)

References 55 publications

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“…The important thing is that the found studies are a good representation of the population, which we ensured in this study by adopting a rigorous paper selection process. All Stages Approach/Technique/Method RNN [63] All Stages Other Other [64] All Stages Other Other [56] All Stages Comparative Analysis Other [57] All Stages Other Other [69] All Stages Other RNN, RBM [14] All Stages Model/Framework Other [67] All Stages Tool RF [49] All Stages Other Other [62] All Stages Tool NLP [61] All Stages Other DT [48] All Stages Other Other [65] All Stages Other Other [1] All Stages Other Other [60] All Stages Other Other [68] All Stages Comparative Analysis LR, SVM, NB [66] All Stages Approach/Technique/Method LSTM [11] All Stages Other Other [59] All Stages Other Other [21] Requirements Approach/Technique/Method NB, KNN, RF [70] Requirements Approach/Technique/Method SVM, SMO, NB [82] Requirements Approach/Technique/Method PN [75] Requirements Model/Framework ProbPoly [71] Requirements Approach/Technique/Method Text2Model [84] Requirements Approach/Technique/Method RF [72] Requirements Approach/Technique/Method Other [22] Requirements Approach/Technique/Method NB, RF, LR, SGD, DT [23] Requirements Approach/Technique/Method Boosting [81] Requirements Approach/Technique/Method NSGA-II algorithm [24] Requirements Model/Framework CNN [73] Requirements Approach/Technique/Method FL [76] Requirements Approach/Technique/Method LP, SMO, NB, KNN [86] Requirements Approach/Technique/Method J48, FSS, CFS [25] Requirements Model/Framework RNN [85] Requirements Model/Framework KNN [...…”

Section: A External Validitymentioning

confidence: 99%

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Shafiq¹,

Mashkoor

Mayr-Dorn

et al. 2021

IEEE Access

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The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.

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Section: A External Validitymentioning

confidence: 99%

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Shafiq¹,

Mashkoor

Mayr-Dorn

et al. 2021

IEEE Access

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“…• Involves specialist domain experts (individuals) rather than teams 34) : is the system intelligent or is the human developer intelligent ? 20) • Mapping expert / real-world knowledge to objective and weighting functions, is an art and difficult to achieve.…”

Section: Human Process Aspectsmentioning

confidence: 99%

Harnessing agile concepts for the development of Intelligent Systems

Sharif¹

1999

NGCO

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Traditional and current approaches to intelligent systems design, have led to the creation of sophisticated and computationally-intensive packages and environments, for a wide range of applications. This paper proposes methods with which to extend the functionality of such systems, borrowing knowledge management concepts from the field of Agile Manufacturing. As such, this paper proposes that the future of intelligent systems design should be based not only upon the continuing development of artificial intelligence techniques, but also effective methods for harnessing human skills and core competencies to achieve these aims.

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“…paradigm consists of a tight integration of identification, conceptualization, formalization, implementation, and testing phases in a cyclical fashion. Expert system life cycle has also been studied with recent emphasis on specification techniques [16,15,23,22,5]. In this paper we take an approach that expert system lifecycle consists of the problem specification phase', solution specification phase, high-level design phase, implementation phase and testing phase (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%