Software testing ensures systems meet requirements and identifies defects while minimizing manual effort, reducing costs, and enhancing automation. Model-Based Testing (MBT) leverages abstract models for automated test case generation and execution. However, traditional exploration techniques often struggle to balance exploration and exploitation, limiting coverage and error detection. Reinforcement Learning (RL) offers a promising alternative, adapting dynamically for test case generation, execution, and prioritization. This study evaluates state-of-the-art RL algorithms—A2C, PPO, and Recur-rentPPO—utilizing an Agent-Based MBT framework. Using four Software Under Test (SUT) models with four error sizes, we assess the effectiveness of RL in error detection. Results suggest RL could outperform traditional techniques in both efficacy and efficiency, excelling at detecting complex errors and exploring extended execution paths. These findings highlight the potential of RL to advance MBT and broader software testing tasks.

Continuous software engineering is increasingly ingrained and widely adopted in development projects. These projects have extended their processes by utilizing software measurement tools with dashboards to monitor and understand development best practices, quality, and evolution. Exploring development perceptions and behaviors in software engineering education and training contexts is needed to better understand how tools could aid development processes. The goal is to explore students' perceptions and development behaviors in software engineering projects that continuously measure contributions. We conducted a case study, mainly focusing on agile software engineering and quality best practices, and gathered behaviors and perceptions through mining repositories and applying surveys. Our results show acceptance of continuous measurement, where most respondents agreed that it benefited their software development process. We found benefits in engineering and quality practices such as continuous integration and configuration management. Some of the perceived benefits related to agile practices did not always align with observed behaviors. Our results suggest that continuous measurement in software engineering education had mostly positive perceptions and outlooks on behaviors. These findings are encouraging for adopters of continuous measurement tools in education and training contexts.

Software visualization is a program comprehension technique that can support activities such as the analysis of the quality during the evolution of development. The empirical evaluation of software visualizations is necessary to provide evidence of perceptions and experiences of use, and the benefits provided during software analysis. Empirical data can help evidence the advantages and limitations of software visualizations, improving their acceptance in academia and industry. This study presents an analysis of the perceptions of 16 participants using a 3D city metaphor visualization to understand the structure of a web application and identify aspects related to development and quality best practices in a software engineering university course. For this, we studied the user experience through the UE Questionnaire (UEQ) and the ease of use, the perceived usefulness, and the intention of future use through the Technology Acceptance Model (TAM). Finally, benefits and opportunities were identified based on the perceptions of the participants and the results of the activity. The results indicate that the visualization of the city can support comprehension activities and help identify improvements in development and quality practices. Furthermore, this tool was positively perceived in the context of software engineering courses. Most of the acceptance and experience constructs were evaluated positively, with some opportunities identified regarding ease of use and visualization comprehension. In general, the visualization helped identify possible opportunities required for software applications under review.

Measuring developers’ and teams’ contributions in project-based software engineering courses is challenging. Instructors require measurement tools to identify early improvement opportunities and simplify the assessment of students’ contributions to projects. Besides, students could appreciate detailed, timely, and valuable feedback that encapsulates the multi-faceted nature of their contribution to improve their skills. With the rise of software repositories, data-driven tools that mine and visualize repository data have been proposed to help assess and improve software engineering courses. Still, little is known about students’ perceptions of integrating these tools to measure project contributions. This paper analyzes students’ perceptions of integrating a contribution measurement tool in agile software engineering projects. We collected the perceptions of 65 undergraduate students developing projects through a mixed-method approach. Our results show several benefits of using these tools in software engineering courses, providing valuable feedback, intending to use such tools in projects, and facilitating timely and helpful feedback. Additionally, students described the benefits of nine agile practices, including coding standards, reviews and inspections, and planning games. These tools were perceived as useful, easy to use, and intended to be used in future projects. Six challenges for integrating these tools in a course setting related to adoption, learning curves, configuration issues, measurement completeness, team dynamics, and Goodhart’s law were found. Our results showed the acceptance and utility of such tools to aid students in software engineering projects.

Many courses in the software engineering area are centered around team-based project development. Evaluating these projects is a challenge due to the difficulty of measuring individual student contributions versus team contributions. The adoption of distributed version control systems like Git enables the measurement of students' and teams' contributions to the project. In this work, we analyze the contributions within five software development projects from undergraduate courses that used project-based learning. For this, we generate visualizations of aggregated Git metrics using inequality indexes and inter-decile ratios, which offer insights into the practices and processes followed by students and teams throughout the project development. This approach allowed us to identify both inequality among students' contributions and development processes with a non-steady pace, rendering a useful feedback tool for instructors and students during the development of the project. Further studies can be conducted to assess the complexity and value of students' contributions by analyzing their source code commits and other software artifacts.

Model‐based testing (MBT) automates the design and generation of test cases from a model. This process includes model building, test selection criteria, test case generation, and test case execution stages. Current tools support this process at various levels of automation, most of them supporting three out of four stages. Among them is MBT4J, a platform that extends ModelJUnit with several techniques, offering a high level of automation for testing Java applications. In this study, the authors evaluate the efficacy of the MBT4J platform, in terms of the number of test cases generated, errors detected, and coverage metrics. A case study is conducted using two open‐source Java systems from public repositories, and 15 different configurations. MBT4J was able to automatically generate five models from the source code. It was also able to generate up to 2025 unique test cases for one system and up to 1044 for the other, resulting in 167 and 349 failed tests, respectively. Transition and transition pair coverage reached 100% for all models. Code coverage ranged between 72 and 84% for the one system and between 59 and 76% for the other. The study found that Greedy and Random were the most effective testers for finding errors.

Project Based Learning (PBL) is a teaching and learning methodology where students develop projects under real conditions to acquire the knowledge and develop the skills required for their profession. In this article we discuss the lessons learned from two teachers who implemented different PBL approaches in three courses from the Bachelor in Computer and Informatics at the University of Costa Rica, during three semesters. We discuss the main considerations and decisions associated to project design elements and teaching practices. Among the main benefits identified by teachers when using PBL are the high motivation of students and the natural development of soft skills. Among the greatest challenges are (1) the design of learning assessment, considering technical aspects, soft skills, self- and peer- evaluations; (2) continuous feedback to students on their progress in the project and in team dynamics (and its consequent time investment); and (3) the necessary balance between designing a challenging and realistic project that enables the achievement of the course's learning objectives. We recommend the use of agile methodologies such as Scrum, which align very well with PBL principles, as well as the use of hybrid PBL methodologies, which include complementary didactic strategies to achieve the learning objectives of the course.

Functional size measurement automatically calculated for specific development frameworks is a challenge for the software industry. Automating function points counting involves benefits such as savings in time and costs and better reliability and accuracy of the measures, but it is hardly automatable. This paper presents an automated functional size measurement procedure, which has been systematically designed to obtain the functional size of software systems modeled in the development framework called FastWorks. In this study, we describe the framework architecture, the procedure design based on IFPUG FPA and the measurement prototype tool. Finally, the approach is preliminarily validated, the results are presented and lessons learned are discussed.

Background: The complexity of providing accurate software size estimation and effort prediction models is well known in the software industry, turning it into one of the most important research issues in empirical software engineering. Function points (FPA) is currently one of the most accepted software functional size metrics in the industry, but it is hardly automatable and generally requires a lengthy and costly process. Although accurate size estimation and effort prediction are very important for the success of any project, many practitioners have experienced difficulties in applying them. Objectives: This paper reports on a replicated study carried out on a subset of the ISBSG dataset to evaluate the structure and applicability of function points. The goal of this replication was to aggregate evidence and confirm results reported about internal issues of FPA as a metric using a different set of data. First, we examined FPA counting in order to determine which base functional components (BFC) were independent of each other and thus appropriate for an additive model of size. Second, we investigated the relationship between size and effort. Methods: A subset of the ISBSG dataset was used with 14 business application projects developed in C# from 2008 to 2011. We studied BFC independence and correlation between size, effort and productivity. FPA base functional components independence was checked with the Pearson and Kendall's Tau correlation coefficient. Besides, we studied the correlation between size and effort. Results: The replication aggregated evidence and confirmed that some BFC of the FPA method are correlated. There is a relationship between BFC unadjusted function points and effort. Limitations: This is an initial experiment of a research in progress that was performed on a small subset of 14 recent projects taken from the ISBSG dataset. Conclusions: Simplifying and automating a FPA measurement process based on counting BFC could encourage the adoption of FSM methods. Further research is needed.