Rocks Coding, Not Development--A Human-Centric, Experimental Evaluation of LLM-Supported SE Tasks
This research addresses the gap in understanding LLM capabilities for real-world software engineering, providing insights for developers and tool designers, though it is incremental in assessing existing technology.
The study evaluated ChatGPT's performance in coding and software development tasks through a controlled experiment with 109 participants, finding it effective for simple coding but less so for typical development tasks, and identified interactions influencing outcomes.
Recently, large language models (LLM) based generative AI has been gaining momentum for their impressive high-quality performances in multiple domains, particularly after the release of the ChatGPT. Many believe that they have the potential to perform general-purpose problem-solving in software development and replace human software developers. Nevertheless, there are in a lack of serious investigation into the capability of these LLM techniques in fulfilling software development tasks. In a controlled 2 x 2 between-subject experiment with 109 participants, we examined whether and to what degree working with ChatGPT was helpful in the coding task and typical software development task and how people work with ChatGPT. We found that while ChatGPT performed well in solving simple coding problems, its performance in supporting typical software development tasks was not that good. We also observed the interactions between participants and ChatGPT and found the relations between the interactions and the outcomes. Our study thus provides first-hand insights into using ChatGPT to fulfill software engineering tasks with real-world developers and motivates the need for novel interaction mechanisms that help developers effectively work with large language models to achieve desired outcomes.