Code reviews are a critical aspect of open-source software (OSS) development, ensuring quality and fostering collaboration. This study examines perceptions, challenges, and biases in OSS code review processes, focusing on the perspectives of Contributors and Maintainers. Through surveys (n = 289), interviews (n = 23), and repository analysis (n = 81), we identify key areas of alignment and disparity. While both groups share common objectives, differences emerge in priorities, e.g, with Maintainers emphasizing alignment with project goals while Contributors overestimated the value of novelty. Bias, particularly familiarity bias, disproportionately affects underrepresented groups, discouraging participation and limiting community growth. Misinterpretation of approach differences as bias further complicates reviews. Our findings underscore the need for improved documentation, better tools, and automated solutions to address delays and enhance inclusivity. This work provides actionable strategies to promote fairness and sustain the long-term innovation of OSS ecosystems.

Debugging is one of the most difficult and tedious tasks for software engineers. While various tools and techniques have been introduced to assist debugging, most programmers use print statement debugging to find and fix errors in their code. That is, they manually add code to print values to verify if the code is executing as expected and make sure a certain section of the program is reached. However, this process can be time-consuming and error-prone, especially in large and complex programs. To that end, we introduce AutoPrint, a tool that automatically inserts and removes print statements to streamline print statement debugging in Java code. We conducted a judgment study with 23 participants—students and practitioners—to elicit feedback on AutoPrint and gain insights on its utility in practical debugging tasks. Our results show participants perceive AutoPrint saves debugging time and effort through a faster, simpler, and more usable tool compared to other approaches.

Human subjects research is fundamental to advancing human-computer interaction (HCI), as it helps researchers understand user behaviors, needs, and experiences to enhance the design of software products [3]. However, conducting such human-centric studies is often hindered by challenges in participant recruitment, including inefficiencies in finding eligible participants and administrative burdens [1]. Participants are hindered from joining research studies due to logistical, eligibility, communication, or personal barriers that make participation impractical, inaccessible, or unappealing. These barriers not only slow down research progress, but also limit the diversity and representativeness of study participants. To address these issues, we propose ParticipantGuide-a structured labelbased approach to enhance participant recruitment through providing key and interpretable information to potential participants. We discuss existing work, present a preliminary design, and provide implications for future research.

Studying abroad can be a life-changing experience that can help students develop skills, make friends, and gain a global perspective. However, Computer Science (CS) students rarely encounter opportunities to participate in a study abroad program. We interviewed students who participated in our institution’s first CS Study Abroad program-focused on software engineering-to understand participants’ experiences and how the program impacted the students’ personal and computing identities. We found that students faced unique challenges, such as working with teammates who have different cultural backgrounds and programming styles. Through overcoming those challenges, students were able to strengthen their computing identity and gain confidence that they could work in diverse software engineering teams. Based on the lessons that we learned, we provide guidelines to enhance future CS Study Abroad experiences.

Recent developments in Artificial Intelligence (AI) have shifted the software development paradigm. Past studies demonstrated how effective AI can generate code for programming purposes. However, to our knowledge, no prior study has been done to evaluate the effectiveness of SQL queries generated by AI. We utilized nine AI assistants to generate SQL queries. Our results reveal that most AI assistants generate inaccurate SQL queries, and based on the results, we provide possible implications for SQL developers.

Decision-making is a key software engineering skill. Developers constantly make choices throughout the software development process, from requirements to implementation. While prior work has studied developer decision-making, the choices made while choosing what solution to write in code remain understudied. In this mixed-methods study, we examine the phenomenon where developers select one specific way to implement a behavior in code, given many potential alternatives. We call these decisions implementation design decisions. Our mixed-methods study includes 46 survey responses and 14 semi-structured interviews with professional developers about their decision types, considerations, processes, and expertise for implementation design decisions. We find that implementation design decisions, rather than being a natural outcome from higher levels of design, require constant monitoring of higher level design choices, such as requirements and architecture. We also show that developers have a consistent general structure to their implementation decision-making process, but no single process is exactly the same. We discuss the implications of our findings on research, education, and practice, including insights on teaching developers how to make implementation design decisions.

Debugging is one of the most expensive and time-consuming processes in software development. To support programmers, researchers, and developers have introduced a wide variety of debuggers or tools to automatically find errors in code, to make this process more efficient. However, there is a gap between industry developers and students regarding the skillful use of debuggers. We aim to understand this gap by studying barriers that hinder new programmers from using debuggers. We conducted a survey involving 73 students with various extents of programming experience and performed qualitative analysis. The goal was to extract insights into why students do not develop debugger usage skills. Our results suggest the general lack of academic course focus on debuggers is one of the primary reasons for avoidance. At the same time, complex user interfaces and a lack of visualization also seem intimidating for many students, making using a debugger unappealing. Based on the results, we provide guidelines to motivate future debugger designs and education materials to improve debugger usage. Our survey results summary is publicly available at https://github.com/minhyukko/vlhcc23