High Confidence Subsystem Modelling for Reuse

Penzenstadler, Birgit; Koss, Dagmar

doi:10.1007/978-3-540-68073-4_5

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Paludo [4] proposed the use of patterns to help the software designer to model business processes. Penzenstadler [5] proposed a concrete artefact model for integrated reuse from requirements to technical architecture, which satisfies documentation demands with respect to functionality and the context assumed by the subsystem. Gasparic [6] performed an industrial exploratory case study to analyze the software reuse process of a medium size company which is a technology leader in a niche market.…”

Section: Introductionmentioning

confidence: 99%

ACO-RR: Ant Colony Optimization Ridge Regression in Reuse of Smart City System

Yin

Niu

et al. 2019

Lecture Notes in Computer Science

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With the rapid development of artificial intelligence, governments of different countries have been focusing on building smart cities. To build a smart city is a system construction process which not only requires a lot of human and material resources, but also takes a long period of time. Due to the lack of enough human and material resources, it is a key challenge for lots of small and medium-sized cities to develop the intelligent construction, compared with the large cities with abundant resources. Reusing the existing smart city system to assist the intelligent construction of the small and medium-sizes cities is a reasonable way to solve this challenge. Following this idea, we propose a model of Ant Colony Optimization Ridge Regression (ACO-RR), which is an smart city evaluation method based on the ridge regression. The model helps small and medium-sized cities to select and reuse the existing smart city systems according to their personalized characteristics from different successful stories. Furthermore, the proposed model tackles the limitation of ridge parameters' selection affecting the stability and generalization ability, because the parameters of the traditional ridge regression is manually random selected. To evaluate our model performance, we conduct experiments on real-world smart city data set. The experimental results demonstrate that our model outperforms the baseline methods, such as support vector machine and neural network.

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Section: Introductionmentioning

confidence: 99%

ACO-RR: Ant Colony Optimization Ridge Regression in Reuse of Smart City System

Yin

Niu

et al. 2019

Lecture Notes in Computer Science

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Engineering and Employing Reusable Software Components for Modular Verification

Welch

Sitaraman

2017

Lecture Notes in Computer Science

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Empirical Validation of a Requirements Engineering Process Guide

Leuser

Porta

Bolz

et al. 2009

Electronic Workshops in Computing

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Current requirements engineering (RE) practice in industry lacks the availability of an integrated guide that defines and supports the requirements engineering part of embedded system development. Therefore, the RE part of a development project is not as structured and defined as desired. This paper presents results of an experiment validating a RE guide developed by a joint research project between industry and academia. In the experiment carried out for 14 weeks, embedded development projects using the guide were compared to projects following a state-of-the-practice process. The results suggest the guide to be suitable for mastering the RE part of an embedded system development.

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High Confidence Subsystem Modelling for Reuse

Cited by 3 publications

References 9 publications

ACO-RR: Ant Colony Optimization Ridge Regression in Reuse of Smart City System

ACO-RR: Ant Colony Optimization Ridge Regression in Reuse of Smart City System

Engineering and Employing Reusable Software Components for Modular Verification

Empirical Validation of a Requirements Engineering Process Guide

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