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This book constitutes the refereed proceedings of the S3E 2023 Topical Area, 24th Conference on Practical Aspects of and Solutions for Software Engineering, KKIO 2023, and 8th Workshop on Advances in Programming Languages, WAPL 2023, as Part of FedCSIS 2023, held in Warsaw, Poland, during September 17–20, 2023. The 6 revised papers presented in this book were carefully reviewed and selected from a total of 55 submissions. They focus on new ideas and developments in practical aspects and solutions for software engineering., NODLA Non-Technical Debt in Large-Scale Agile Software Development, KK-foundation

Maintenance of Data Warehouse (DW) systems is a critical task because any downtime or data loss can have significant consequences on business applications. Existing DW maintenance solutions mostly rely on concrete technologies and tools that are dependent on: the platform on which the DW system was created; the specific data extraction, transformation, and loading (ETL) tool; and the database language the DW uses. Different languages for different versions of DW systems make organizing DW processes difficult, as minimal changes in the structure require major changes in the application code for managing ETL processes. This article proposes a domain-specific language (DSL) for ETL process management that mitigates these problems by centralizing all program logic, making it independent from a particular platform. This approach would simplify DW system maintenance. The platform-independent language proposed in this article also provides an easier way to create a unified environment to control DW processes, regardless of the language, environment, or ETL tool the DW uses.

In the paper, we present a data warehouse system to analyze the unemployment rate in the Republic of Serbia. The goal of our research is to improve the analytical capabilities of the unemployment rate in Serbia by creating a new business intelligence tool and predictive machine learning models. First, we discuss research motives and the unemployment problem, and then we present the development process of the proposed data warehouse system. The Data Warehouse Quality methodology has been deployed to assess the quality of the data. Machine learning algorithms have been utilized to build predictive models and gain insights into the differences in unemployment rates between young and experienced workers. Finally, we have created several reports to visually present the results of the proposed data analyses., U radu predstavljamo sistem skladišta podataka za analizu stope nezaposlenosti u Republici Srbiji. Cilj našeg istraživanja je da unapredimo analitičke mogućnosti stope nezaposlenosti u Srbiji kreiranjem novog alata poslovne inteligencije i prediktivnih modela mašinskog učenja. Prvo razmatramo motive istraživanja i problem nezaposlenosti, a zatim predstavljamo proces razvoja predloženog sistema skladišta podataka. Metodologija kvaliteta skladišta podataka je primenjena za procenu kvaliteta podataka. Algoritmi mašinskog učenja su korišćeni za izgradnju prediktivnih modela i sticanje uvida u razlike u stopama nezaposlenosti između mladih i iskusnih radnika. Konačno, napravili smo nekoliko izveštaja kako bismo vizuelno predstavili rezultate predloženih analiza podataka.

Industry 4.0 aims to establish highly flexible production, enabling effective and efficient mass customisation of products. Modelling techniques and simulation of production processes are among the core techniques of the manufacturing industry that facilitate flexibility and automation of a shop floor in the era of Industry 4.0. In this paper, we present an approach to support production process modelling and process model management. The approach is based on Model-Driven (MD) principles and comprises a Domain-Specific Modelling Language (DSML) named Multi-Level Production Process Modelling Language (MultiProLan). MultiProLan uses a set of concepts to specify production process models suitable for automatic instruction generation and execution of the instructions in a simulation or on a shop floor. By using MultiProLan, process designers may create process models independent of the specific production system. Such process models can either be automatically enriched by matching and scheduling algorithms or manually enriched by a process designer via MultiProLan's modelling tool. In this paper, we also present an application of our approach in the assembly industry to showcase its dynamic resource management, generation of production documentation, error handling and process monitoring.

Due to strong competition and rapidly shifting market conditions, it is becoming harder for Small and Medium-sized Enterprises (SMEs) to achieve business success. To deal with rising challenges, SMEs form Virtual Organizations (VOs) and seize business opportunities jointly. In this paper, we present an outline of a novel methodological approach that promotes trustworthy collaborative production execution within a non-hierarchical VO. Furthermore, we propose using Distributed Ledger Technology (DLT) platforms and smart contracts to facilitate VO integration. The approach is based on the MultiProLan Domain-Specific Modeling Language (DSML) extended with concepts required to allow process designers to (i) model collaborative production processes while preserving the confidentiality of private enterprise data and (ii) configure what data should be shared between participants during the collaborative production execution. Designed process models are used to automatically generate smart contracts by following the Model-Driven (MD) principles. Finally, generated smart contracts are stored in a DLT network and used to distribute production data between VO participants and monitor production execution in near real-time. The application of our methodological approach is demonstrated by showcasing the use of the Collaborative Extension of MultiProLan (CE-MultiProLan) modeling language and its concepts for modeling collaborative production processes.

A selection of Computer Science, Informatics or similar study programs for academic studies evidently becomes an emerging choice of a vast number of students in recent years. To address some of the open questions, we performed an empirical study based on a survey, with a goal to find out the main motivating factors directing students to select computer science, informatics or similar programs for studying in a much greater extent. The survey was conducted on a sample of 1517 students from five well established, and most traditional faculties of computer science and informatics at three largest university cities in Serbia: Belgrade, Novi Sad, and Nis. The created sample is representative enough to illustrate the current situation and trends common for many similar societies. Our first analysis shows that the main motivating factor to select computer science or informatics study program at all faculties is the students' motivation to study just that topic, while at management faculty it is significantly less important. However, we also noticed that significant number of students wished to study something else but chose computer science and informatics due to possibility of finding jobs easier and of earning higher salaries in industry. The most important influential factors to choose a computer science or informatics major come from family members, and close relatives. The perceived brand and reputation of a faculty also plays a significant role. Students being examined prevalently tend to be satisfied with the faculty they have chosen. However, many of them see themselves leaving the country in a near or far future.

Technological advances and increasing customer need for highly customized products have triggered a fourth industrial revolution. A digital revolution in the manufacturing industry is enforced by introducing smart devices and knowledge bases to form intelligent manufacturing information systems. One of the goals of the digital revolution is to allow flexibility of smart factories by automating shop floor changes based on the changes in input production processes and ordered products. In order to make this possible, a formal language to describe production processes is needed, together with a code generator for its models and an engine to execute the code on smart devices. Existing process modeling languages are not usually tailored to model production processes, especially if models are needed for automatic code generation. In this paper we propose a research on Industry 4.0 manufacturing using a Domain-Specific Modeling Language (DSML) within a Model-Driven Software Development (MDSD) approach to model production processes. The models would be used to generate instructions to smart devices and human workers, and gather a feedback from them during the process execution. A pilot comparative analysis of three modeling languages that are commonly used for process modeling is given with the goal of identifying supported modeling concepts, good practices and usage patterns.