Smart farming adopts advanced technology and the corresponding principles to increase the amount of production and economic returns, often also with the goal to reduce the impact on the environment. One of the key elements of smart farming is the farm management information systems (FMISs) that supports the automation of data acquisition and processing, monitoring, planning, decision making, documenting, and managing the farm operations. An increased number of FMISs now adopt internet of things (IoT) technology to further optimize the targeted business goals. Obviously IoT systems in agriculture typically have different functional and quality requirements such as choice of communication protocols, the data processing capacity, the security level, safety level, and time performance. For developing an IoT-based FMIS, it is important to design the proper architecture that meets the corresponding requirements. To guide the architect in designing the IoT based farm management information system that meets the business objectives a systematic approach is provided. To this end a design-driven research approach is adopted in which feature-driven domain analysis is used to model the various smart farming requirements. Further, based on a FMIS and IoT reference architectures the steps and the modeling approaches for designing IoT-based FMIS architectures are described. The approach is illustrated using two case studies on smart farming in Turkey, one for smart wheat production in Konya, and the other for smart green houses in Antalya.

The integration of Internet of Things (IoT) networks into smart cities is crucial to enhance the efficiency of city operations and services. Designing a smart city architecture that can adapt to the constantly changing functional and quality requirements of city services is essential. However, critical decisions must be made during this process, such as selecting communication protocols, ensuring security and safety, optimizing time performance, and processing data capacity. To address these challenges, this paper proposes a systematic approach to guide the system architecture design of IoT-based smart cities. The approach starts with feature-driven domain analysis to model smart city requirements, followed by the design of a reference architecture for IoT-based smart cities. The architecture is modeled using selected architectural views, while considering key stakeholders and their concerns. Additionally, this paper presents valuable insights into lessons learned and challenges encountered during the process of creating IoT-based smart cities. This information can assist practitioners in developing such smart cities and pave the way for future research in this field. By following this proposed approach, smart city architects can design a robust and adaptable system architecture that can meet the evolving needs of smart city services.

Software bug report classification is a critical process to understand the nature, implications, and causes of software failures. Furthermore, classification enables a fast and appropriate reaction to software bugs. However, for large-scale projects, one must deal with a broad set of bugs from multiple types. In this context, manually classifying bugs becomes cumbersome and time-consuming. Although several studies have addressed automated bug classification using machine learning techniques, they have mainly focused on academic case studies, open-source software, and unilingual text input. This paper presents our automated bug classification approach applied and validated in an industrial case study. In contrast to earlier studies, our study is applied to a commercial software system based on unstructured bilingual bug reports written in English and Turkish. The presented approach adopts and integrates machine learning (ML), text mining, and natural language processing (NLP) techniques to support the classification of software bugs. The approach has been applied within an industrial case study. Compared to manual classification, our results show that bug classification can be automated and even performs better than manual bug classification. Our study shows that the presented approach and the corresponding tools effectively reduce the manual classification time and effort.