| 1. |
- Eliasson, Ulf, 1984, et al.
(författare)
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Agile Model-Driven Engineering in Mechatronic Systems - An Industrial Case Study
- 2014
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Ingår i: 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. - 9783319116532 ; 8767, s. 433-449
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Tidskriftsartikel (refereegranskat)abstract
- Model-driven engineering focuses on structuring systems as well as permitting domain experts to be directly involved in the software development. Agile methods aim for fast feedback and providing crucial knowledge early in the project. In our study, we have seen a successful combination of MDE and agile methods to support the development of complex, software-driven mechatronic systems. We have investigated how combining MDE and agile methods can reduce the number of issues caused by erroneous assumptions in the software of these mechatronic systems. Our results show that plant models to simulate mechanical systems are needed to enable agile MDE during the mechatronic development. They enable developers to run, verify, and validate models before the mechanical systems are delivered from suppliers. While two case studies conducted at Volvo Car Group confirm that combining MDE and agile works, there are still challenges e.g. how to optimize the development of plant models.
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| 2. |
- Eliasson, Ulf, 1984, et al.
(författare)
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Architecting in the Automotive Domain: Descriptive vs Prescriptive Architecture
- 2015
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Ingår i: 12th Working IEEE/IFIP Conference on Software Architecture (WICSA), 2015. - : IEEE. - 9781479919222 ; , s. 115-118
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Konferensbidrag (refereegranskat)abstract
- To investigate the new requirements and challenges of architecting often safety critical software in the automotive domain, we have performed two case studies on Volvo Car Group and Volvo Group Truck Technology. Our findings suggest that automotive software architects produce two different architectures (or views) of the same system. The first one is a high-level descriptive architecture, mainly documenting system design decisions and describing principles and guidelines that should govern the overall system. The second architecture is the working architecture, defining the actual blueprint for the implementation teams and being used in their daily work. The working architecture is characterized by high complexity and considerably lower readability than the high-level architecture. Unfortunately, the team responsible for the high-level architecture tends to get isolated from the rest of the development organization, with few communications except regarding the working architecture. This creates tensions within the organizations, sub-optimal design of the communication matrix and limited usage of the high-level architecture in the development teams. To adapt to the current pace of software development and rapidly growing software systems new ways of working are required, both on technical and on an organizational level.
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| 3. |
- Heldal, Rogardt, 1964, et al.
(författare)
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Descriptive vs Prescriptive Models in Industry
- 2016
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Ingår i: 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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Konferensbidrag (refereegranskat)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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| 4. |
- Issa Mattos, David, 1990, et al.
(författare)
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Automotive A/B testing : Challenges and Lessons Learned from Practice
- 2020
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Ingår i: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). - : IEEE. - 9781728195322 - 9781728195339 ; , s. 101-109
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Konferensbidrag (refereegranskat)abstract
- Over the past 15 years, A/B testing has been a critical tool for accurate prioritization of development efforts in online and web-facing companies. As automotive companies progress on their digitalization process, A/B testing and other experimentation techniques start to be adopted. However, specific characteristics of the automotive software industry create additional challenges to the successful adoption of A/B testing. Recently, research has been conducted to investigate the challenges and opportunities for experimentation techniques in the automotive and more generally in the embedded systems domain. However, despite the collaboration with industry, previous research was based on either hypothesized or toy scenarios in companies seeking, but not yet running experimentation. Utilizing a case study method, we investigate the challenges of adopting A/B testing in two large-scale automotive companies that are currently running or preparing for their first A/B testing. The contribution of this paper is two-fold. First, we present our main findings in terms of the challenges of real A/B testing iterations in automotive vehicles. Second, we present the current, potential solutions and lessons learned from applying A/B testing in the automotive domain.
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| 5. |
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| 6. |
- Lantz, Jonn, et al.
(författare)
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Josephson junction qubit network with current-controlled interaction
- 2004
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Ingår i: Physical Review B. - 2469-9969 .- 2469-9950. ; 70
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Tidskriftsartikel (refereegranskat)abstract
- We design and evaluate a scalable charge qubit chain network with controllable current-current coupling of neighboring qubit loops via local de-current gates. The network allows construction of general N-qubit gates. The proposed design is in line with current main stream experiments.
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| 7. |
- Liu, Yuchu, 1992, et al.
(författare)
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An architecture for enabling A/B experiments in automotive embedded software
- 2021
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Ingår i: Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021. - : IEEE. ; , s. 992-997
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Konferensbidrag (refereegranskat)abstract
- A/B experimentation is a known technique for data-driven product development and has demonstrated its value in web-facing businesses. With the digitalisation of the automotive industry, the focus in the industry is shifting towards software. For automotive embedded software to continuously improve, A/B experimentation is considered an important technique. However, the adoption of such a technique is not without challenge. In this paper, we present an architecture to enable A/B testing in automotive embedded software. The design addresses challenges that are unique to the automotive industry in a systematic fashion. Going from hypothesis to practice, our architecture was also applied in practice for running online experiments on a considerable scale. Furthermore, a case study approach was used to compare our proposal with state-of-practice in the automotive industry. We found our architecture design to be relevant and applicable in the efforts of adopting continuous A/B experiments in automotive embedded software.
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| 8. |
- Liu, Yuchu, 1992, et al.
(författare)
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Bayesian causal inference in automotive software engineering and online evaluation
- 2022
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating software changes. In the automotive domain, running randomised field experiments is not always desired, possible, or even ethical. In the face of such restrictions, we show how to utilise observational studies in combination with Bayesian causal inference to understand real-world impacts from complex automotive software updates and help development organisations arrive at causal conclusions. In this study, we present three causal inference models in the Bayesian framework and their corresponding cases to address three commonly experienced challenges of software evaluation in the automotive domain. We apply Bayesian propensity score matching for producing balanced control and treatment groups, Bayesian regression discontinuity for identifying covariate dependent treatment assignments, and Bayesian difference-in-differences for causal inference on treatment effect overtime. We demonstrate the potential of causal inference with our industry collaborators with studies conducted on a fleet of vehicles. The cases are presented in details as well as the related the theory of causal assumption to the practice of running observational studies. Finally, we discuss the potential and pitfalls of the Bayesian causal models.
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| 9. |
- Liu, Yuchu, 1992, et al.
(författare)
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Bayesian propensity score matching in automotive embedded software engineering
- 2021
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Ingår i: Proceedings - Asia-Pacific Software Engineering Conference, APSEC. - 1530-1362. ; 2021-December, s. 233-242
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Konferensbidrag (refereegranskat)abstract
- Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised field experiments is not always desired, possible or even ethical in the development of automotive embedded software. In the face of such restrictions, we propose the use of the Bayesian propensity score matching technique for causal inference of observational studies in the automotive domain. In this paper, we present a method based on the Bayesian propensity score matching framework, applied in the unique setting of automotive software engineering. This method is used to generate balanced control and treatment groups from an observational online evaluation and estimate causal treatment effects from the software changes, even with limited samples in the treatment group. We exemplify the method with a proof-of-concept in the automotive domain. In the example, we have a larger control (Nc = 1100) fleet of cars using the current software and a small treatment fleet (Nt = 38), in which we introduce a new software variant. We demonstrate a scenario that shipping of a new software to all users is restricted, as a result, a fully randomised experiment could not be conducted. Therefore, we utilised the Bayesian propensity score matching method with 14 observed covariates as inputs. The results show more balanced groups, suitable for estimating causal treatment effects from the collected observational data. We describe the method in detail and share our configuration. Furthermore, we discuss how can such a method be used for online evaluation of new software utilising small groups of samples.
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| 10. |
- Liu, Yuchu, et al.
(författare)
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Bayesian propensity score matching in automotive embedded software engineering
- 2021
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Ingår i: 2021 28th Asia-Pacific Software Engineering Conference (APSEC). - : IEEE. - 9781665437844 - 9781665437851
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Konferensbidrag (refereegranskat)abstract
- Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised field experiments is not always desired, possible or even ethical in the development of automotive embedded software. In the face of such restrictions, we propose the use of the Bayesian propensity score matching technique for causal inference of observational studies in the automotive domain. In this paper, we present a method based on the Bayesian propensity score matching framework, applied in the unique setting of automotive software engineering. This method is used to generate balanced control and treatment groups from an observational online evaluation and estimate causal treatment effects from the software changes, even with limited samples in the treatment group. We exemplify the method with a proof-of-concept in the automotive domain. In the example, we have a larger control (Nc = 1100) fleet of cars using the current software and a small treatment fleet (Nt = 38), in which we introduce a new software variant. We demonstrate a scenario that shipping of a new software to all users is restricted, as a result, a fully randomised experiment could not be conducted. Therefore, we utilised the Bayesian propensity score matching method with 14 observed covariates as inputs. The results show more balanced groups, suitable for estimating causal treatment effects from the collected observational data. We describe the method in detail and share our configuration. Furthermore, we discuss how can such a method be used for online evaluation of new software utilising small groups of samples.
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| 11. |
- Liu, Yuchu, 1992, et al.
(författare)
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Size matters? Or not : A/B testing with limited sample in automotive embedded software
- 2021
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Ingår i: 2021 47TH EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2021). - : IEEE. - 9781665427050 ; , s. 300-307
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Konferensbidrag (refereegranskat)abstract
- A/B testing is gaining attention in the automotive sector as a promising tool to measure casual effects from software changes. Different from the web-facing businesses, where A/B testing has been well-established, the automotive domain often suffers from limited eligible users to participate in online experiments. To address this shortcoming, we present a method for designing balanced control and treatment groups so that sound conclusions can be drawn from experiments with considerably small sample sizes. While the Balance Match Weighted method has been used in other domains such as medicine, this is the first paper to apply and evaluate it in the context of software development. Furthermore, we describe the Balance Match Weighted method in detail and we conduct a case study together with an automotive manufacturer to apply the group design method in a fleet of vehicles. Finally, we present our case study in the automotive software engineering domain, as well as a discussion on the benefits and limitations of the A/B group design method.
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| 12. |
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| 13. |
- Wallquist, Margareta, 1979, et al.
(författare)
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Superconducting qubit network with controllable nearest neighbor coupling
- 2005
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Ingår i: New Journal of Physics. - : IOP Publishing. - 1367-2630. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- We investigate the design and functionality of a network of loop-shaped charge qubits with switchable nearest-neighbour coupling. The qubit coupling is achieved by placing large Josephson junctions (JJs) at the intersections of the qubit loops and selectively applying bias currents. The network is scalable and makes it possible to perform a universal set of quantum gates. The coupling scheme allows gate operation at the charge degeneracy point of each qubit, and also applies to charge-phase qubits. Additional JJs included in the qubit loops for qubit readout can also be employed for qubit coupling.
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