Library migration is the process of replacing one library with another library that provides similar functionality. Manual library migration is time consuming and error prone, as it requires developers to understand the APIs of both libraries, map them, and perform the necessary code transformations. Large Language Models (LLMs) are shown to be effective at generating and transforming code as well as finding similar code, which are necessary upstream tasks for library migration. Such capabilities suggest that LLMs may be suitable for library migration. Accordingly, this paper investigates the effectiveness of LLMs for migration between Python libraries. We evaluate three LLMs, LLama 3.1, GPT-4o mini, and GPT-4o on PyMigBench, where we migrate 321 real-world library migrations that include 2,989 migration-related code changes. To measure correctness, we (1) compare the LLM’s migrated code with the developers’ migrated code in the benchmark and (2) run the unit tests available in the client repositories. We find that LLama 3.1, GPT-4o mini, and GPT-4o correctly migrate 89%, 89%, and 94% of the migration-related code changes, respectively. We also find that 36%, 52% and 64% of the LLama 3.1, GPT-4o mini, and GPT-4o migrations pass the same tests that passed in the developer’s migration. To ensure the LLMs are not reciting the migrations, we also evaluate them on 10 new repositories where the migration never happened. Overall, our results suggest that LLMs can be effective in migrating code between libraries, but we also identify some open challenges.

We present the Requirement Elicitation Tool that leverages Large Language Model (LLM) (gpt-4o-mini) to enable simulated real-world interactions of requirements gathering from three synthetic personas. We demonstrate the use case of Computer Science (CS) students in Database Management Systems leveraging the tool to build a conceptual model and Entity-Relationship (ER) diagrams. Our preliminary findings show the potential of this tool to engage students in discovery process without providing predefined solutions and set the directions for future work.

A considerable increase in enrollment numbers poses major challenges in course management, such as fragmented information sharing, inefficient meetings, and poor understanding of course activities among a large team of teaching assistants. To address these challenges, we restructured the course, drawing inspiration from successful management and educational practices. We developed an organized, three-tier structure for teams, each led by an experienced Lead TA. We also formed five functional teams, each focusing on a specific area of responsibility: communication, content, "lost student" support, plagiarism, and scheduling. In addition, we updated our recruitment method for undergraduate TAs, following a model similar to the one used in the software industry, while also deciding to mentor Lead TAs in place of traditional training. Our experiences, lessons learned, and future plans for enhancement have been detailed in this experience report. We emphasize the value of using management techniques in dealing with large-scale course handling and invite cooperation to improve the implementation of these strategies, inviting other institutions to consider and adapt this approach, tailoring it to their specific needs.

No abstract available.

Teaching assistants (TAs) are essential members of post-secondary instructional teams, who often have considerable student-facing time. The recently increasing demand for introductory computer-science (CS) courses has resulted in a corresponding increased demand for TAs. The existing TA literature has predominantly focused on investigating the role of training in the TA experience, particularly with respect to performance. This provides little insight into how TAs experience their management. Consequently, we investigate the role of management in the experiences of introductory-level CS TAs. We provide the structure for a formal, tiered management scheme employed in a high-enrolment introductory CS course. This scheme attempts to address the challenges associated with managing TAs at scale. As a case study, we compare the experience of TAs under this new management scheme with that of TAs under an informal, unstructured management style used in smaller introductory CS courses. Specifically, we used questionnaires to look at TAs' self-efficacy and understand their experiences. While we did not find a significant difference in TA self-efficacy between the two management styles, thematic analysis of the open-response data revealed a greater number of challenges reported by the TAs under the tiered management system. TAs characterized this tiered system as "organized" and frequently reported feeling overworked. Across both groups, TAs identified improved communication and additional training as factors that could improve their experience. Our findings suggest self-efficacy was not a sufficient measure to quantify TA experience. We propose framing TA experience based on their motivation and general well-being.

Developers heavily rely on Application Programming Interfaces (APIs) from libraries to build their projects. However, libraries might become obsolete, or new libraries with better APIs might become available. In such cases, developers replace the used libraries with alternative libraries, a process known as library migration. Since manually migrating between libraries is tedious and error prone, there has been a lot of effort towards automated library migration. However, most of the current research on automated library migration focuses on Java libraries, and even more so on version migrations of the same library. Despite the increasing popularity of Python, limited research has investigated migration between Python libraries. To provide the necessary data for advancing the development of Python library migration tools, this paper contributes PyMigBench, a benchmark of real Python library migrations.PyMigBench contains 59 analogous library pairs and 75 real migrations with migration-related code changes in 161 Python files across 57 client repositories.

As enrollment numbers grow, educators face a number of challenges. Lectures and interacting with students aren't much of our job anymore, and in the long run, our success as educators depends on our management skills.