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- Idowu, Samuel, 1985, et al.
(författare)
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Asset Management in Machine Learning: A Survey
- 2021
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Ingår i: Proceedings - International Conference on Software Engineering. - 0270-5257.
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Konferensbidrag (refereegranskat)abstract
- Machine Learning (ML) techniques are becoming essential components of many software systems today, causing an increasing need to adapt traditional software engineering practices and tools to the development of ML-based software systems. This need is especially pronounced due to the challenges associated with the large-scale development and deployment of ML systems. Among the most commonly reported challenges during the development, production, and operation of ML-based systems are experiment management, dependency management, monitoring, and logging of ML assets. In recent years, we have seen several efforts to address these challenges as witnessed by an increasing number of tools for tracking and managing ML experiments and their assets. To facilitate research and practice on engineering intelligent systems, it is essential to understand the nature of the current tool support for managing ML assets. What kind of support is provided? What asset types are tracked? What operations are offered to users for managing those assets? We discuss and position ML asset management as an important discipline that provides methods and tools for ML assets as structures and the ML development activities as their operations. We present a feature-based survey of 17 tools with ML asset management support identified in a systematic search. We overview these tools' features for managing the different types of assets used for engineering ML-based systems and performing experiments. We found that most of the asset management support depends on traditional version control systems, while only a few tools support an asset granularity level that differentiates between important ML assets, such as datasets and models.
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| 2. |
- Mahmood, Wardah, 1992, et al.
(författare)
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Seamless Variability Management with the Virtual Platform
- 2021
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Ingår i: Proceedings - International Conference on Software Engineering. - 0270-5257.
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Konferensbidrag (refereegranskat)abstract
- Customization is a general trend in software engineering, demanding systems that support variable stakeholder requirements. Two opposing strategies are commonly used to create variants: software clone&own and software configuration with an integrated platform. Organizations often start with the former, which is cheap, agile, and supports quick innovation, but does not scale. The latter scales by establishing an integrated platform that shares software assets between variants, but requires high up-front investments or risky migration processes. So, could we have a method that allows an easy transition or even combine the benefits of both strategies? We propose a method and tool that supports a truly incremental development of variant rich systems, exploiting a spectrum between both opposing strategies. We design, formalize, and prototype the variability management framework virtual platform . It bridges clone&own and platform-oriented development. Relying on programming language independent conceptual structures representing software assets, it offers operators for engineering and evolving a system, comprising: traditional, asset-oriented operators and novel, feature-oriented operators for incrementally adopting concepts of an integrated platform. The operators record meta-data that is exploited by other operators to support the transition. Among others, they eliminate expensive feature-location effort or the need to trace clones. Our evaluation simulates the evolution of a real-world, clone-based system, measuring its costs and benefits.
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