Studying Software Engineering Patterns for Designing Machine Learning Systems

Washizaki, Hironori; Uchida, Hiromu; Khomh, Foutse; Guéhéneuc, Yann‐Gaël

doi:10.1109/iwesep49350.2019.00017

Cited by 70 publications

(39 citation statements)

References 16 publications

(16 reference statements)

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“…The empirical study of SE practices in AI/ML projects is an emergent topic, with nascent work in this area investigating the challenges AI/ML presents for software teams via surveys [1,10], systematic literature reviews [23], and case studies [14]. We contribute to these discourses by drawing on a multi-year ethnographic study of SE projects at a large, global technology company building digital services infused with AI/ML capabilities.…”

Section: Introductionmentioning

confidence: 99%

Sensemaking Practices in the Everyday Work of AI/ML Software Engineering

Wolf

Paine

2020

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

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This paper considers sensemaking as it relates to everyday software engineering (SE) work practices and draws on a multi-year ethnographic study of SE projects at a large, global technology company building digital services infused with artificial intelligence (AI) and machine learning (ML) capabilities. Our findings highlight the breadth of sensemaking practices in AI/ML projects, noting developers' efforts to make sense of AI/ML environments (e.g., algorithms/methods and libraries), of AI/ML model ecosystems (e.g., pre-trained models and "upstream" models), and of business-AI relations (e.g., how the AI/ML service relates to the domain context and business problem at hand). This paper builds on recent scholarship drawing attention to the integral role of sensemaking in everyday SE practices by empirically investigating how and in what ways AI/ML projects present software teams with emergent sensemaking requirements and opportunities.

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Section: Introductionmentioning

confidence: 99%

Sensemaking Practices in the Everyday Work of AI/ML Software Engineering

Wolf

Paine

2020

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

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“…Pattern-based languages are a common solution to solve recurrent problems of many domains, including robotics [60,81], cyber-physical systems [82], self-adaptive systems [83,84], machine learning [85], IoT [86], and multi-agent systems [84,[87][88][89]. Existing design patterns in the field of multi-agent systems were classified in a recent survey [88].…”

Section: Pattern-based Languagesmentioning

confidence: 99%

Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

Cardoso

Ferrando

Briola

et al. 2021

JSAN

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Multi-agent systems, robotics and software engineering are large and active research areas with many applications in academia and industry. The First Workshop on Agents and Robots for reliable Engineered Autonomy (AREA), organised the first time in 2020, aims at encouraging cross-disciplinary collaborations and exchange of ideas among researchers working in these research areas. This paper presents a perspective of the organisers that aims at highlighting the latest research trends, future directions, challenges, and open problems. It also includes feedback from the discussions held during the AREA workshop. The goal of this perspective is to provide a high-level view of current research trends for researchers that aim at working in the intersection of these research areas.

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“…Most focus on the object-oriented design. Moreover, surveys on architecture and design patterns exist for specific domains and quality attributes, such as multiagent systems [10], machine learning systems [11], or secure systems [12].…”

Section: Related Workmentioning

confidence: 99%

“…We used Scopus as the initial document base of the SLR. Although many other SLRs, such as [11] and [51]- [53] have adopted it, relevant papers (such as IoT security pattern papers [54]) may have been missed. To mitigate this threat, we plan to use other databases, extend our SLR, and elicit public review of the results.…”

Section: Threats To Validitymentioning

confidence: 99%

Landscape of Architecture and Design Patterns for IoT Systems

Washizaki

Ogata

Hazeyama

et al. 2020

IEEE Internet Things J.

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Due to the widespread proliferation of today's Internet of Things (IoT), a system designer needs the IoT system and software design patterns to assist in designing scalable and replicable solutions. Patterns are encapsulations of reusable common problems and solutions under specific contexts. Many IoT patterns have been published, such as IoT design patterns and IoT architecture patterns to document the successes (and failures) in IoT systems and software development. However, because these patterns are not well classified, their adoption does not live up to their potential. To understand the reasons, we conducted a systematic literature review. From the 32 identified papers, 143 IoT architecture and design patterns were extracted. We analyzed these patterns according to several characteristics and outlined directions for improvements when publishing and adopting IoT patterns. Of the extracted patterns, 57% are non-IoT patterns, suggesting that IoT systems and software are often designed via conventional architecture and design patterns that are not specific to IoT design. Although most IoT design patterns are applicable to any domain, IoT architecture patterns tend to be domain specific, implying that the unique nature of IoT adoption in specific domains appears at the architecture level. As more domains adopt IoT, the number of domain-specific IoT design patterns should increase. In terms of quality attributes, many IoT patterns address compatibility, security, and maintainability.

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Studying Software Engineering Patterns for Designing Machine Learning Systems

Cited by 70 publications

References 16 publications

Sensemaking Practices in the Everyday Work of AI/ML Software Engineering

Sensemaking Practices in the Everyday Work of AI/ML Software Engineering

Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

Landscape of Architecture and Design Patterns for IoT Systems

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