Analysis of Software Engineering for Agile Machine Learning Projects

Singla, Kushal; Bose, Joy; Naik, Chetan

doi:10.1109/indicon45594.2018.8987154

Cited by 11 publications

(9 citation statements)

References 4 publications

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“…Two disciplines research agenda can use analogy to converge their interests in AI-SE trans-field. SE agile software development methods can be adapted by orienting them towards exploration and research to develop AI systems [83]. AI based systems are software systems with SE and at least one AI component are becoming pervasive in society; however there is limited synthesized SE knowledge for building such systems [14].…”

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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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%

Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

Mambo¹

2022

tjes

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“…Singla, Bose and Naik [21] studied the logs related to software engineering following the agile methodology for a machine learning team and compared it with the logs for a nonmachine learning team, analyzing the trends and their reasons. The authors then provided a few suggestions about the way Agile could have a better use for machine learning teams and projects.…”

Section: A Rq1: What Are the Phases Addressed In Terms Of The Machine...mentioning

confidence: 99%

Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review

Lorenzoni¹,

Alencar²,

Nascimento³

et al. 2021

Preprint

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Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Development. The problems regarding Machine Learning Development involves the fact that such professionals do not realize that they usually perform ad-hoc practices that could be improved by the adoption of activities presented in the Software Engineering Development Lifecycle. Of course, since machine learning systems are different from traditional Software systems, some differences in their respective development processes are to be expected. In this context, this paper is an effort to investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective by focusing on understanding how software developers could benefit from applying or adapting the traditional software engineering process to the Machine Learning workflow.

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“…Because an ML systems lifecycle differs from the traditional software lifecycle, techniques and practices must be adapted to suit the particularities that involve the relationship between the data, the trained model, and the source code [6]. Since the publication of Google's paper on the challenges of maintaining and evolving Machine Learning models in the production environment in 2015 [9], industry efforts have concentrated on identifying and developing these practices.…”

Section: Introductionmentioning

confidence: 99%

Practices for Managing Machine Learning Products: a Multivocal Literature Review

alves¹,

Leite²,

Meirelles³

et al. 2023

Preprint

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<p>Machine Learning (ML) has grown in popularity in the software industry due to its ability to solve complex problems. Developing ML Systems involves more uncertainty and risk because it requires identifying a business opportunity and managing the source code, data, and trained model. Our research aims to identify the existing practices used in the industry for building ML applications. The goal is to comprehend the orga- nizational complexity of adopting ML Systems. We conducted a Multivocal Literature Review (MLR) and used Grounded Theory (GT) to build a taxonomy with the practices applied to the ML System lifecycle from the industry and academic perspectives. We selected 41 posts from grey literature and 37 papers from scientific repositories. Following a systematic GT protocol, we mapped 91 practices, grouped in 6 core categories related to designing, developing, testing, and deploying ML Systems. The results can help organizations identify the gaps in their current ML processes and practices, and provide a roadmap for improving and optimizing their ML systems. The comprehensive taxonomy of practices developed in this research serves as a valuable tool for managers, practitioners, and researchers in the ML field, providing a clear and organized understanding of the complexity of managing ML systems.</p>

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Analysis of Software Engineering for Agile Machine Learning Projects

Cited by 11 publications

References 4 publications

Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review

Practices for Managing Machine Learning Products: a Multivocal Literature Review

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