Context: Software Engineering solves real world problems and thus requires future engineers to amass experience solving problems in authentic contexts during their formal education. Active learning provides learning contexts where students can acquire experience by doing contextualized and meaningful activities that involve high-order thinking. Despite active learning being proved to be more effective providing hands-on experience in software engineering situations traditional learning activities such as lectures still rival the former. Objectives: Identify and define what active learning strategies are used in software engineering higher education (1) and what application experiences of such strategies have been conducted between 2018 and 2023 (2). Method: A secondary study with Rapid Review as a research method. A Rapid Review is a secondary study which balances literature search breadth and resources available with the purpose of accelerating a systematic mapping study. Results: 18 active learning strategies on software engineering were found across 49 studies. Games and Project-Based Learning were the most frequently used strategies. Conclusions: There are diverse active learning strategies used in software engineering higher education. Active learning strategies can be deployed in varied higher education settings and promote competencies development. Active learning strategies can be combined with other active learning strategies as well as combined with traditional learning activities like lectures in order to foster both lower and higher order thinking skills.

A software startup is an emerging company focused on the creation and development of an innovative software-intensive product or service as the basis for creating business value. Given the limited resources that startups have, the Lean Startup model advocates the creation of a Minimum Viable Product (MVP) to validate the assumptions about the product or service, and the target market. This study presents the results of a series of interviews with founders of software ventures in relation to the type of MVP they implement, the choice of potential clients, validation mechanisms and difficulties they face in the process of developing and validating the MVP. The main results indicate that the preferred MVP types are landing pages and mock-ups with some functionalities, validation with selected early adopters, obtaining feedback based on interviews and comments, and that the main difficulties encountered are due to lack of experience in software development and in choosing the right technology.

This paper describes and discusses a pilot study about code quality practices from the software development leader perspective. Semi-structured interview and thematic analysis have been selected as qualitative analysis methods. This article introduces the design of the research protocol, the execution of the interviews and the analysis process. The results drawn from the present study suggest potential dependence of the practices according to the context of the organisation: type of client and methodology. Code review, pull request in distributed repositories and coding standards emerge as the main code quality practices.

Technical Debt is a metaphor that explains a phenomenon that occurs in software development when programmers are faced with trade-off decisions (usually ship first vs. quality assurance). This work analyses the amount of technical debt incurred by undergraduate students using different coding techniques. This observational study uses source code from seventy-five students. We provided students with similar exercises to compare techniques by measuring with static code analyzers (Sonar, FindBugs). The techniques are TDD, Test Last, and ad hoc programming. Our results could not find a statistically significant difference of technical debt incurred by each development practices. Nonetheless, with both tools ad hoc programming measures less technical debt than TDD, and TDD measures less than Test Last. Furthermore, we observed that the two measurement tools outputs are not statistically correlated. Finally, we discuss implications for the research of technical debt stemming from our observations.

In Software Engineering technical literature the references about the benefits of software inspections are abundant. In contrast, some authors raise the problem of low adoption of this process. From this issue a literature review is made to produce a map on most researched topics in the area, factors causing low adoption and possible solutions. Results showed a list of 64 articles selected using a search protocol, which were classified according to a defined taxonomy. The founded factors were codified and a list of solutions founded in the reviewed papers was made. The main conclusion was that most of the factors causing low adoption are related to developers perceptions about the process, lack of training and some characteristics of the process as the rigidity, complexity and the difficulty of connecting the effort made with the final product quality. These factors should be studied in future works.

In the literature there are many empirical studies to evaluate techniques for automatic structural test case generation. However, there isn't a systematic study about the kind of experiments that are conducted to automate the process. The main objective of this paper is the classification and thematic analysis of the experiments reported in the literature to increase the efficacy and efficiency of the automation of structural testing and a classification of the techniques for generating test data. The methodology is a systematic mapping study. The results indicate that the experiments are mainly focused in three areas (test data generation, reduction of test suites and techniques for dealing with complex structures of programs), and that there are different combined approaches to make the generation more effective and efficient.