The study addresses a recurring practical problem: how to obtain high confidence in the correctness of an Eclipse/Xtext-based DSL toolchain—spanning language services, IDE integration, and UI workflows—while keeping builds and releases reproducible and CI-friendly across platforms. We present a general, practice-oriented testing and CI framework for such toolchains and instantiate it in Edelta, an open-source framework and Xtext-based DSL for the evolution and refactoring of EMF metamodels and models. We describe a comprehensive testing strategy, based on test-driven development, spanning fast unit tests for the runtime and refactoring libraries, headless DSL tests for parsing, scoping, validation, type checking, interpretation, and code generation, IDE integration tests for the Eclipse editor, and end-to-end SWTBot tests that exercise complete user workflows. These tests are orchestrated in a single Maven/Tycho build and continuously executed on GitHub Actions across platforms, with SonarCloud providing code-quality and coverage feedback. Our CI setup also builds and verifies distributable Eclipse products and a self-contained p2 update site. An empirical analysis of a representative CI run shows a clear testing-pyramid distribution, with most checks implemented at the fastest layers, keeping the overall CI turnaround time low while providing high confidence in correctness. We summarize lessons learned, actionable guidelines, and pitfalls to avoid when engineering Eclipse- and Xtext-based research software that must be both feature-rich and continuously releasable.

Abstract The increasing complexity of software development has prompted a shift in project management practices toward agile methodologies, often supported by specialized software tools for planning and reporting. However, many widely adopted tools offer only limited decision support capabilities. This work introduces ProjectION, an intelligent software tool designed to enhance decision-making in agile project management through techniques relative to the Search-Based Software Engineering field. Based on an in-depth analysis of the challenges faced in agile environments, ProjectION assists decision makers in monitoring project status, forecasting progress, and automating software release planning. A comprehensive usability study was conducted with a small sample of experienced IT professionals, following the ISO/IEC 25062:2006 Common Industry Format. Key usability metrics of effectiveness, efficiency, and user satisfaction were assessed. Despite minor usability issues, results indicate that ProjectION effectively supports agile release planning. A second experiment further demonstrates the tool’s utility in generating optimal release plans, outperforming manual solutions proposed by decision makers. To foster collaboration and future development, the core algorithms and execution service have been made publicly available, enabling integration of novel approaches to the Next Release Problem.

Testing real-time embedded systems requires intelligent strategies that balance test coverage, timing constraints, and resource limitations. The traditional test case generation methods, such as random testing and conventional Q-learning, often fail to adapt to dynamic workloads and maintain real-time responsiveness. To address these limitations, an automated test case generation method based on adaptive Q-learning (AQL) is proposed in this study; the method is specifically designed for real-time embedded software. The proposed method introduces dynamic parameter adjustment and adaptive time-window control schemes to optimize multiple objectives including test coverage, resource utilization, and empirical real-time performance under varying workloads. Experiments were conducted on an ATV dashboard-embedded platform, and AQL was compared with random testing (RT) and traditional Q-learning (QL). The results demonstrated that AQL achieved significant performance improvements: the statement coverage level reached 92%, the average CPU utilization rate decreased to 63%, and under experimental loads, the deadline miss rate remained below 2% across all scenarios (e.g., 1.2% under high CPU load), while faster response times were achieved. A statistical analysis (ANOVA, p < 0.01) confirmed the significance of these improvements. In summary, the proposed AQL method provides an efficient and scalable intelligent solution for testing embedded systems in real time. Its feedback-driven adaptive structure effectively overcomes the static limitations of the conventional reinforcement learning approaches, offering both academic innovation and practical potential for testing intelligent software in resource-constrained real-time environments.

Abstract Function Point Analysis (FPA) is a method in software engineering that focuses on identifying the functions provided by a software system to users, such as data input, processing, output, and database management. These functions are classified according to complexity to quantify the system’s size in functional point units. In this paper, we propose two graph neural networks: a Graph-based Similarity Detection Neural Network (GSDNN) and a Prior-Structural Information Graph Neural Network (PSI-GNN) with a pre-trained layer using transfer learning, to define the best model for functional size prediction and uncover patterns and trends in data. Additionally, the NESMA (Netherlands Software Metrics Users Association) method, from the functional families approach, will be in focus, where the ISBSG (International Software Benchmarking Standards Group) dataset, which provides standardized and relevant data for comparing software performance, was used to analyze 1704 industrial software projects. The goal was to identify the graph architecture with the smallest number of experiments to be performed and the lowest Mean Magnitude Relative Error (MMRE) using orthogonal-array tuning optimization via Latin Square extraction. In the proposed approach, the number of experiments is fewer than 8 for each dataset, and a minimum MMRE value of 0.97% was obtained using PSI-GNN. Additionally, the impact of five input features on the change in MMRE value was analyzed with the top-performing model, employing the SHAP (SHapley Additive exPlanations) feature importance method, visualized through GraphExplainer. The frequency of user-initiated transactions, quantified technically, emerged as the most significant determinant within the NESMA framework.