Shaokang's Blog

Note: This is a group project, and I am only responsible for a portion of the work. Citations have been removed to adjust for website rendering. The original PDF is at the end of this page.

ChatGPT’s potential for human-like communication is noteworthy, but the mental health implications of integrating a real human identity remain understudied. This research focuses on introducing an AvatarGPT, a human-like avatar, to the ChatGPT interface to delve into these effects. A between-subject study (N=10) was conducted to investigate users’ responses, evaluate, and compare the effectiveness of AvatarGPT and ChatGPT before and after a conversation using the UCLA Loneliness Scale. Results show that neither using an avatar (p≈0.39) nor conversing without the avatar (p≈0.11) significantly improved loneliness scores. Additionally, using the avatar did not enhance willingness to speak, as measured by increased word counts, or significantly reducing loneliness score in percentage (p≈0.59). We believe that with a larger participant pool and a longer experimental period, we would be able to observe a more significant increase in emotional change.

The original paper is under review, citation format:

Shaokang Jiang and Michael Coblenz. An Analysis of the Costs and Benefits of Autocomplete in IDEs. In review, ACM International Symposium on Foundations of Software Engineering (FSE 2024)

Key points:

• Worked with Michael Coblenz on the usability analysis of autocomplete.
• Designed and executed an experiment with 32 participants using an eye tracker to evaluate the costs and benefits of IDE-based autocomplete features to programmers who use an unfamiliar API; analyzed data using JMP; and wrote a paper for the study.
• Found that participants who used autocomplete learned more about the API while spending less time reading the documentation; found autocomplete did not significantly reduce the number of keystrokes required to finish tasks.

Are Python Type Hints helpful in competitive programming?

Abstract

Type hinting, which is a way to statically indicate the type of a value in the code, was introduced in Python 3.5. Before Python 3.5, python was a dynamic language in which variable type can only be inferred during runtime. Competitive programmers(CP) are a neglected group of people who need to solve algorithm challenges as fast as possible. In this project, we are trying to assess if using Type Hints is helpful for competitive programmers. We conducted a pilot study with 5 programmers from different backgrounds, 1 of them only provided interview data. We found that type hints may not be useful for CP in Python, and autocomplete suggestions are often not good enough to be accepted. The survey indicates that People also dislike type hints in Python for CP tasks. Keywords: Type hint. Competitive programmer. Python. Autocomplete.