Today, small and medium-sized enterprises (SMEs) in the software industry face major challenges. Their resource constraints require high efficiency in development. Furthermore, quality assurance (QA) measures need to be taken to mitigate the risk of additional, expensive effort for bug fixes or compensations. Automated static analysis (ASA) can reduce this risk because it promises low application effort. SMEs seem to take little advantage of this opportunity. Instead, they still mainly rely on the dynamic analysis approach of software testing.In this article, we report on our experiences from a technology transfer project. Our aim was to evaluate the results static analysis can provide for SMEs as well as the problems that occur when introducing and using static analysis in SMEs. We analysed five software projects from five collaborating SMEs using three different ASA techniques: code clone detection, bug pattern detection and architecture conformance analysis. Following the analysis, we applied a quality model to aggregate and evaluate the results.Our study shows that the effort required to introduce ASA techniques in SMEs is small (mostly below one person-hour each). Furthermore, we encountered only few technical problems. By means of the analyses, we could detect multiple defects in production code. The participating companies perceived the analysis results to be a helpful addition to their current QA and will include the analyses in their QA process. With the help of the Quamoco quality model, we could efficiently aggregate and rate static analysis results. However, we also encountered a partial mismatch with the opinions of the SMEs. We conclude, that ASA and quality models can be a valuable and affordable addition to the QA process of SMEs.
Smart energy systems seem a promising choice for countries worldwide to realign their power systems to the challenges predicted for the next decades. With the will to participate in this class of systems, many solution providers design custom systems, which sometimes consist of similar parts, but are on the contrary hard to compare to each other. However, a reference describing existing commonalities is needed as a basis for many activities such as regulation design, legislation, national discussion or standardization. This paper illustrates the challenges connected with the creation of reference architectures for smart energy systems, delineates their benefits and suggests a model and method for their incremental, bottom-up development and validation through concrete system architectures.Index Terms-Reference Architecture; Smart Energy Systems; Smart Grid; Ontologies; Conceptual Modeling; Domain Modeling; Bottom-up design; I. INTRODUCTIONThe existing energy systems will go through major changes within the next decades. Facts like the increasing of renewable, decentralized energy sources, the growing number of electric cars, national efforts on market liberalization and reduction of CO 2 -emissions, integration of different energy grid types (e.g. electric power, district heating or gas grids) or the need for advanced monitoring systems and increased power stability will drive this change. Many countries discuss new concepts to solve problems like the fluctuating supply of renewable energy with smart systems while having to ensure power stability.As a reaction to this worldwide trend, many actors think of a new class of systems, often labeled as "Smart Energy Systems" (SES) and develop new systems inside this class. However these systems focus on different aspects of the energy system, involve different stakeholders, include new components, functions and data structures and use different technologies, concepts, terminology and infrastructure. The class of SES comprises a variety of systems used in home appliances, energy management, district heating, intelligent devices, virtual power plants, demand side management, market places, data platforms, metering infrastructure, field devices, portal software, weather forecasting or grid operations.Many countries, national and international organizations are interested in SES, as this class of systems is expected to have an impact on national grid infrastructure, markets, customers and industries. In contrast the sheer amount of existing systems and their different architectures complicate the comprehension and comparison of different solutions or the elaboration of an abstract view on SES.
Abstract. Today's small and medium-sized enterprises (SMEs) in the software industry are faced with major challenges. While having to work efficiently using limited resources they have to perform quality assurance on their code to avoid the risk of further effort for bug fixes or compensations. Automated static analysis can reduce this risk because it promises little effort for running an analysis. We report on our experience in analysing five projects from and with SMEs by three different static analysis techniques: code clone detection, bug pattern detection and architecture conformance analysis. We found that the effort that was needed to introduce those techniques was small (mostly below one person-hour), that we can detect diverse defects in production code and that the participating companies perceived the usefulness of the presented techniques as well as our analysis results high enough to include the techniques in their quality assurance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.