Code edit histories can offer instructors valuable insight into students’ problem-solving processes, revealing unproductive behaviors that final code alone cannot capture. For example, a correct solution may contain large copy-and-pasted segments (suggesting the code originated elsewhere) or unguided trial-and-error (suggesting a lack of clear strategy). Timelines are a common way to visualize code histories, but existing timeline visualizations of code or document histories show only when and where edits occurred, not what changed. Without this context, it is difficult to answer key questions about how students invested effort or to infer their intentions. We present CodeStream, a visualization system that augments timelines with situational code annotations, whose granularity and visibility dynamically adapt to scale and interaction state. A comparison study shows that CodeStream enables context-aware navigation of coding histories, supporting fast and accurate pattern identification, and helping instructors reason about students’ coding behaviors and identify who may need intervention.

Programming instruction often focuses on syntax and algorithms. However, mastering programming also requires building strategic knowledge of skills such as debugging, problem solving, and program design. These critical skills are difficult to teach explicitly because they often involve tacit knowledge, context-specific understanding, and adaptive decision-making. Large Language Models (LLMs) can be effective in helping with syntactic and algorithmic questions but can fail to provide strategic knowledge. This is partly because strategic knowledge involves nuanced contexts that span code and runtime states, requires subjective judgments, and dynamically evolves based on the outcomes of users’ actions. We introduce Co-Advisor, a context-aware strategy recommendation tool that leverages LLM to evaluate problem context and monitor the programmer’s actions to provide personalized constructive feedback. Unlike prior work, Co-Advisor can dynamically align expert strategies with real-time programmer actions and code context, offering actionable, personalized strategic knowledge. In a formative evaluation with 14 programmers involved in two debugging tasks, we found that those using Co-Advisor to receive context-related feedback alongside expert strategies were significantly more successful than those without context-related feedback. They demonstrated greater engagement and had an improved learning experience, gaining insight into the reasons behind their mistakes, correcting them, and understanding the rationale behind their actions. Thus, Co-Advisor enhances conceptual understanding and strategic problem-solving.

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.

In many domains, strategic knowledge is documented and shared through checklists and handbooks. In software engineering, however, developers rarely share strategic knowledge for approaching programming problems, in contrast to other artifacts and despite its importance to productivity and success. To understand barriers to sharing, we simulated a programming strategy knowledge-sharing platform, asking experienced developers to articulate a programming strategy and others to use these strategies while providing feedback. Throughout, we asked strategy authors and users to reflect on the challenges they faced. Our analysis revealed that developers could share strategic knowledge. However, they struggled in choosing a level of detail and understanding the diversity of the potential audience. While authors required substantial feedback, users struggled to give it and authors to interpret it. Our results suggest that sharing strategic knowledge differs from sharing code and raises challenging questions about how knowledge-sharing platforms should support search and feedback.

Developers rely heavily on resources to find technical insights on how to use languages, APIs, and platforms, seeking help from Stack Overflow, GitHub, meetups, blogs, live streams, forums, documentation, and more. However, there is one kind of knowledge for which resources are hard to find: strategic knowledge. In contrast to technical knowledge, strategic knowledge provides insight into how to approach problem-solving. Prior work has demonstrated that developers can make use of written strategies to improve their problem-solving. However, there is currently no way for developers to share, curate, and search for this knowledge at scale. To address this gap, we contribute HowToo, a platform for sharing, finding, and using programming strategies. Its key insight is that there are many different approaches to the same problem, and developers may need different strategies depending on their situation. In a longitudinal evaluation with more than 30 students in a project-based software engineering course, we found that: 1) students viewed HowToo as complementary to technical resources; 2) students viewed strategies as helping them be more systematic and complete in their work; 3) HowToo helped students be more confident in their problem solving; 4) when students were under time pressure, they were less inclined to use HowToo to structure their work, as being mindful required them to slow down.

In heavily congested urban areas, the rapid growth of population is becoming more and more of an issue. Affected cities quickly demand solutions to areas such as quality of life, waste management, public transportation, and accessibility to main resources. However, since the number of impacted areas of population growth is endless, we focus on public parking. Studies show that drivers spend a large portion of their travel time locating vacant parking spots. For this reason, we present MagnoPark, a crowdsourced approach to identifying unoccupied spots accessible to the general public, who are typically free. MagnoPark is a smartphone based sensing solution that detects empty parking spots using internal sensors of cellphones. While a pedestrian is walking on the sidewalk, we exploit magnetometer changes near metal objects in identifying where cars are located. The amplitude and rate of change shift dramatically when approaching or passing cars that are parked beside the street, giving us a great platform towards solving the defined problem. With empirical evaluation, we show that not only is our solution a notable step towards economical parking management but also significantly efficient and as accurate as traditional sensor- based parking solutions.