Software Engineering for Machine-Learning Applications: The Road Ahead

Khomh, Foutse; Adams, Bram; Cheng, Jinghui; Fokaefs, Marios; Antoniol, Giuliano

doi:10.1109/ms.2018.3571224

Cited by 85 publications

(54 citation statements)

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“…However, the systematic development, deployment and operation of ML applications faces major difficulties (e.g., [1][2][3]18,21]). The methodologies and tools of software engineering (SE) have greatly contributed to a wide range of activities in the lifecycles of traditional information systems, but are difficult to implement in ML application projects because ML applications and traditional software systems differ in fundamental ways.…”

Section: Introductionmentioning

confidence: 99%

“…Given the various challenges in software engineering of ML, we surmise that SE challenges for ML applications cover a similarly wide range of topics. SE challenges for ML applications have been discussed in many papers [15][16][17][18]21], but to our knowledge, no survey paper has clarified the overview of SE challenges for ML applications, that is, what SE challenges have been discussed? and which SE research topics are closely related to each challenge?…”

Section: Introductionmentioning

confidence: 99%

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Software engineering challenges for machine learning applications: A literature review

Kumeno¹

2020

IDT

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Machine learning techniques, especially deep learning, have achieved remarkable breakthroughs over the past decade. At present, machine learning applications are deployed in many fields. However, the outcomes of software engineering researches are not always easily utilized in the development and deployment of machine learning applications. The main reason for this difficulty is the many differences between machine learning applications and traditional information systems. Machine learning techniques are evolving rapidly, but face inherent technical and non-technical challenges that complicate their lifecycle activities. This review paper attempts to clarify the software engineering challenges for machine learning applications that either exist or potentially exist by conducting a systematic literature collection and by mapping the identified challenge topics to knowledge areas defined by the Software Engineering Body of Knowledge (Swebok).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Software engineering challenges for machine learning applications: A literature review

Kumeno¹

2020

IDT

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“…There is a huge body of knowledge and practice that allow us to acknowledge which are, and which are not, the systems and software engineering practices that really represent timeless scientific and technological foundations for a proper development process. However, one of the main cornerstones of both disciplines will be its adaptation to address the challenges (Khomh et al 2018) imposed by the new and evolving digital environment. This integration (Morris 2016) will allow us to deliver up-to-date, safer, secure, cost-efficient and personalized software-based products and services and to overcome the "hidden" technical debt (Sculley et al 2015) of such new systems.…”

Section: Background and Contextmentioning

confidence: 99%

“…In this context, the unavoidable convergence of the Systems & Software Engineering and Artificial Intelligence and Machine Learning disciplines is considered a must (Khomh et al 2018) and one of the next major challenges within the engineering process. That is why, it is completely necessary to harmonize the disciplines of SE and AI/ML for integrating the AI/ML model lifecycle within the software process.…”

Section: Introductionmentioning

confidence: 99%

Challenges and opportunities in the integration of the Systems Engineering process and the AI/ML model lifecycle

Álvarez-Rodríguez

Zúñiga²,

Moreno

et al. 2019

INCOSE International Symp

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The Digital Age, the “Society 5.0” or the “4th Industrial Revolution” has created a challenging and evolving environment in which more up‐to‐date, secure, safer, cost‐efficient and personalized products and services must be timely delivered. Furthermore, the growing interest in equipping systems with intelligence implies that the engineering process must be adapted to consider the specific characteristics of Artificial Intelligent (AI) and Machine Learning (ML) systems. From the AI/ML point of view, the possibility of following an engineering process must also imply an improvement to overcome their “hidden” technical debt. To pave the way to the development of the next generation of smart systems, a retrospective in the current engineering practice and, in the development of AI/ML systems, is presented. Afterwards, the main challenges to harmonize both disciplines are outlined to finally describe the main opportunities and expected impacts.

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“…Khomh, et al [38] studied industrial software systems based on machine learning models, reviewing the testing and application of those systems.…”

Section: Machine Learning Softwarementioning

confidence: 99%

Untitled

2019

Review of Computer Engineering Research

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Article HistoryKeywords Soft computing Software efficiency Software development Programming technology Soft computing components.This paper examines the methods of increasing software efficiency based on soft computing technology by analyzing the benefits and challenges of the components used in software development. The functions and features of machine learning, comprising neural networks, perceptron, support vector machines, and fuzzy logic are highlighted and explained, along with those of evolutionary computation (e.g., evolutionary and genetic algorithms) and probability. Recommendations are presented in conclusion. Contribution/Originality:This paper is one of only a few to investigate and analyze the problems of soft computing technologies. By reviewing the utilization of new estimation methodologies, it contributes to the existing literature and comparative analyses.

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Software Engineering for Machine-Learning Applications: The Road Ahead

Cited by 85 publications

References 0 publications

Software engineering challenges for machine learning applications: A literature review

Software engineering challenges for machine learning applications: A literature review

Challenges and opportunities in the integration of the Systems Engineering process and the AI/ML model lifecycle

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