As software systems continue to grow in complexity, testing has become a fundamental part of ensuring the quality and reliability of software products. Yet, software testing is still often perceived, both in industry and academia, as a repetitive, low-skill activity. This perception fails to recognize the creativity, problem-solving, and adaptability required in testing work. Tasks such as designing complex test cases, automating testing processes, and handling shifting requirements illustrate the challenges testing professionals regularly face. To better understand these experiences, we conducted a study with software testing professionals to explore the nature of challenging tasks in software testing and how they affect these professionals. Our findings show that tasks involving creativity, ongoing learning, and time pressure are often seen as motivating and rewarding. On the other hand, a lack of challenge or overwhelming demands can lead to frustration and disengagement. These findings demonstrate the importance of balancing task complexity to sustain motivation and present software testing as a dynamic and intellectually engaging field.

Background: Software systems powered by large language models are becoming a routine part of everyday technologies, supporting applications across a wide range of domains. In software engineering, many studies have focused on how LLMs support tasks such as code generation, debugging, and documentation. However, there has been limited focus on how full systems that integrate LLMs are tested during development. Aims: This study explores how LLM-powered systems are tested in the context of real-world application development. Method: We conducted an exploratory case study using 99 individual reports written by students who built and deployed LLM-powered applications as part of a university course. Each report was independently analyzed using thematic analysis, supported by a structured coding process. Results: Testing strategies combined manual and automated methods to evaluate both system logic and model behavior. Common practices included exploratory testing, unit testing, and prompt iteration. Reported challenges included integration failures, unpredictable outputs, prompt sensitivity, hallucinations, and uncertainty about correctness. Conclusions: Testing LLM-powered systems required adaptations to traditional verification methods, blending source-level reasoning with behavior-aware evaluations. These findings provide evidence on the practical context of testing generative components in software systems.

Technology plays a crucial role in people’s lives. However, software engineering discriminates against individuals from underrepresented groups in several ways, either through algorithms that produce biased outcomes or for the lack of diversity and inclusion in software development environments and academic courses focused on technology. This reality contradicts the history of software engineering, which is filled with outstanding scientists from underrepresented groups who changed the world with their contributions to the field. Ada Lovelace, Alan Turing, and Clarence Ellis are only some individuals who made significant breakthroughs in the area and belonged to the population that is so underrepresented in undergraduate courses and the software industry. Previous research discusses that women, LGBTQIA+ people, and nonwhite individuals are examples of students who often feel unwelcome and ostracized in software engineering. However, do they know about the remarkable scientists that came before them and that share background similarities with them? Can we use these scientists as role models to motivate these students to continue pursuing a career in software engineering? In this study, we present the preliminary results of a survey with 128 undergraduate students about this topic. Our findings demonstrate that students’ knowledge of computer scientists from underrepresented groups is limited. This creates opportunities for investigations on fostering diversity in software engineering courses using strategies exploring computer science’s history.

Modern society is diverse, multicultural, and multifaceted. Because of these characteristics, we are currently observing an increase in the debates about equity, diversity, and inclusion in different areas, especially because several groups of individuals are underrepresented in many environments. In computer science and software engineering, it seems counterintuitive that these areas, which are responsible for creating technological solutions and systems for billions of users around the world, do not reflect the diversity of the society to which it serves. In trying to solve this diversity crisis in the software industry, researchers started to investigate strategies that can be applied to increase diversity and improve inclusion in academia and the software industry. However, the lack of diversity in computer science and related courses, including software engineering, is still a problem, in particular when some specific groups are considered. LGBTQIA+ students, for instance, face several challenges to fit into technology courses, even though most students in universities right now belong to Generation Z, which is described as open-minded to aspects of gender and sexuality. In this study, we aimed to discuss the state-of-art of publications about the inclusion of LGBTQIA+ students in computer science education. Using a mapping study, we identified eight studies published in the past six years that focused on this public. We present strategies developed to adapt curricula and lectures to be more inclusive to LGBTQIA+ students and discuss challenges and opportunities for future research.