| 1. |
- Beal, Jacob, et al.
(author)
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Robust estimation of bacterial cell count from optical density
- 2020
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In: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Journal article (peer-reviewed)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Burden, Håkan, 1976, et al.
(author)
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Comparing and Contrasting Model-Driven Engineering at Three Large Companies
- 2014
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In: Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. - New York, NY, USA : Association for Computing Machinery (ACM). - 1949-3789 .- 1949-3770. - 9781450327749
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Conference paper (peer-reviewed)abstract
- Hutchinson et al. conducted an interview-based study of how model-driven engineering, MDE, is practiced in 17 companies. Their results include that successful MDE companies develop domain-specific languages; are motivated by a clear business case; and are committed at all levels of the organization. Goal: Whilst the results are useful, the study is a very broad one, with one or two interviewees per company. This paper supplements Hutchinson's study by focusing on three large companies that are applying MDE and undergoing a parallel transition to agile methods. Method: Formal data collection strategies -- 25 semi-structured interviews, observations and progress meetings -- were combined with informal interaction. The data was analysed both inductively for new insights and deductively for comparison with the results of Hutchinson et al. Results: Our findings show how MDE can include domain experts in software development and how agile development and MDE can coexist. In general our results validate the findings of Hutchinson et al. There are two areas where our results differ -- the engineers' sense of control and the appropriateness of their skills and training. Conclusions: Using a combination of data collection strategies and analysis techniques our study casts new light on earlier research as well as contributes with novel insights regarding the adoption of MDE.
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| 3. |
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| 4. |
- Heldal, Rogardt, 1964, et al.
(author)
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Descriptive vs Prescriptive Models in Industry
- 2016
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In: Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MODELS 2016). - New York, NY, USA : ACM. - 9781450343213 ; , s. 216-226
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Conference paper (peer-reviewed)abstract
- To understand the importance, characteristics, and limitations of modeling we need to consider the context where models are used. Different organizations within the same company can use models for different purposes and modelling can involve different stakeholders and tools. Recently, several papers discussing how industries use MDE have been published and they have contradictory findings. In this paper we report lessons learned from our collaborations with three large companies. We found that it is important to distinguish between descriptive models (used for documentation) and prescriptive models (used for development) to better understand the adoption of modelling in industry. Our findings are valuable for both academia and industry. A better understanding of modeling in large companies can help academia conceiving innovative MDE solutions that can have a real impact in industry. On the other hand, industry can better understand how to properly exploit MDE and what to expect from it.
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| 5. |
- Mussbacher, G., et al.
(author)
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The Relevance of Model-Driven Engineering Thirty years from Now
- 2014
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 8767, s. 183-200
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Journal article (peer-reviewed)abstract
- Although model-driven engineering (MDE) is now an established approach for developing complex software systems, it has not been universally adopted by the software industry. In order to better understand the reasons for this, as well as to identify future opportunities for MDE, we carried out a week-long design thinking experiment with 15 MDE experts. Participants were facilitated to identify the biggest problems with current MDE technologies, to identify grand challenges for society in the near future, and to identify ways that MDE could help to address these challenges. The outcome is a reflection of the current strengths of MDE, an outlook of the most pressing challenges for society at large over the next three decades, and an analysis of key future MDE research opportunities.
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| 6. |
- Whittle, Jon, et al.
(author)
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Industrial Adoption of Model-Driven Engineering: Are the Tools Really the Problem?
- 2013
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642415333 ; 8107, s. 1-17
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Conference paper (peer-reviewed)abstract
- An oft-cited reason for lack of adoption of model-driven engineering (MDE) is poor tool support. However, studies have shown that adoption problems are as much to do with social and organizational factors as with tooling issues. This paper discusses the impact of tools on MDE adoption and places tooling within a broader organizational context. The paper revisits previous data on MDE adoption (19 in-depth interviews with MDE practitioners) and re-analyzes the data through the specific lens of MDE tools. In addition, the paper presents new data (20 new interviews in two specific companies) and analyzes it through the same lens. The key contribution of the paper is a taxonomy of tool-related considerations, based on industry data, which can be used to reflect on the tooling landscape as well as inform future research on MDE tools. © 2013 Springer-Verlag.
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