Related papers: Product line Development Architectural Model
Model driven development is an effective method due to its benefits such as code transformation, increasing productivity and reducing human based error possibilities. Meanwhile, agile software development increases the software flexibility…
The traditional cycle of industrial products has been linear since its inception. Raw resources are acquired, processed, distributed, used and ultimately disposed of. This linearity has led to a dangerously low efficiency degree in resource…
Pipelining is a well understood and often used implementation technique for increasing the performance of a hardware system. We develop several SystemC/C++ modeling techniques that allow us to quickly model, simulate, and evaluate…
Manufacturers have long benefited from reusing returned products and parts. This benevolent approach can minimize cost and help the manufacturer to play a role in sustaining the environment, something which is of utmost importance these…
In software applications, user models can be used to specify the profile of the typical users of the application, including personality traits, preferences, skills, etc. In theory, this would enable an adaptive application behavior that…
Modeling the logical architecture is an often underestimated development step to gain an early insight into the fundamental functional properties of an automotive system. An architectural description supports developers in making design…
Modern software development methodologies include reuse of open source code. Reuse can be facilitated by architectural knowledge of the software, not necessarily provided in the documentation of open source software. The effort required to…
Software product line (SPL) engineers put a lot of effort to ensure that, through the setting of a large number of possible configuration options, products are acceptable and well-tailored to customers' needs. Unfortunately, options and…
Engineering problems that apply machine learning often involve computationally intensive methods but rely on limited datasets. As engineering data evolves with new designs and constraints, models must incorporate new knowledge over time.…
Multilevel modeling and simulation (M&S) is becoming increasingly relevant due to the benefits that this methodology offers. Multilevel models allow users to describe a system at multiple levels of detail. From one side, this can make…
Context and motivation: Software Product Lines (SPL) enable the creation of software product families with shared core components using feature models to model variability. Choosing features from a feature model to generate a product may…
Software reliability models are an important tool in quality management and release planning. There is a large number of different models that often exhibit strengths in different areas. This paper proposes a model that is based on a…
Due to several issues arising in the rapidly-expanding Halal industry, among them the production of non-genuine or contaminated products and meats, there is a need to develop effective solutions for ensuring authenticity and quality. This…
Certainly, the success of the Unified Modeling Language (UML) as the de facto standard for modeling software systems does not imply closing the door on scientific exploration or experimentation with modeling in the field. Continuing studies…
Today's software projects include enhancements, fixes, and patches need to be delivered almost on a daily basis to clients. Weekly and daily releases are pretty much the norm and sit alongside larger feature upgrades and quarterly releases.…
Newly, the rates of energy and material consumption to augment industrial pro-duction are substantially high, thus the environmentally sustainable industrial de-velopment has emerged as the main issue of either developed or developing…
The increasing availability of data and advancements in computational intelligence have accelerated the adoption of data-driven methods (DDMs) in product development. However, their integration into product development remains fragmented.…
The digital transformation of the energy infrastructure enables new, data driven, applications often supported by machine learning models. However, domain specific data transformations, pre-processing and management in modern data driven…
Building of tools--from simple prototypes to industrial-strength applications--is a pervasive activity in academic research. When proposing a new technique for software maintenance, effective tool support is typically required to…
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…