Domain-oriented architecture design for production control software

this paper, we present domain-oriented architectural design heuristics for production control software. Our approach is based upon the following premisses. First, software design, like all other forms of design, consists of the reduction of uncertainty about a final product by making design decisions. These decisions should as much as possible be based upon information that is certain, either because they represent laws of nature or because they represent previously made design decisions. An import class of information concerns the domain of the software. The domain of control software is the part of the world monitored and controlled by the software; it is the larger system into which the software is embedded. The software engineer should exploit system-level domain knowledge in order to make software design decisions. Second, in the case of production control software, using system-level knowledge is not only justified, it is also imposed on the software engineer by the necessity to cooperate with hardware engineers. These represent their designs by means of Process and Instrumentation Diagrams (PIDs) and Input-Output (IO) lists. They do not want to spend time, nor do they see the need, to duplicate the information represented by these diagrams by means of diagrams from software engineering methods. Such a duplication would be an occasion to introduce errors of omission (information lost during the translation process) or commission (misinterpretation, misguided but invisible design decisions made during the translation) anyway. We think it is up to the software engineer to adapt his or her notations to those of the system engineers he or she must work with. Third, work in patterns and software architectures started from the programminglanguage level and is now moving..

From a Domain Analysis to the Specification and Detection of Code and Design Smells

Code and design smells are recurring design problems in software systems that must be identified to avoid their possible negative consequences\ud on development and maintenance. Consequently, several smell detection\ud approaches and tools have been proposed in the literature. However,\ud so far, they allow the detection of predefined smells but the detection\ud of new smells or smells adapted to the context of the analysed systems\ud is possible only by implementing new detection algorithms manually.\ud Moreover, previous approaches do not explain the transition from\ud specifications of smells to their detection. Finally, the validation\ud of the existing approaches and tools has been limited on few proprietary\ud systems and on a reduced number of smells. In this paper, we introduce\ud an approach to automate the generation of detection algorithms from\ud specifications written using a domain-specific language. This language\ud is defined from a thorough domain analysis. It allows the specification\ud of smells using high-level domain-related abstractions. It allows\ud the adaptation of the specifications of smells to the context of\ud the analysed systems.We specify 10 smells, generate automatically\ud their detection algorithms using templates, and validate the algorithms\ud in terms of precision and recall on Xerces v2.7.0 and GanttProject\ud v1.10.2, two open-source object-oriented systems.We also compare\ud the detection results with those of a previous approach, iPlasma