Prompting LLMs for Code Editing: Struggles and Remedies

Large Language Models (LLMs) are rapidly transforming software engineering, with coding assistants embedded in an IDE becoming increasingly prevalent. While research has focused on improving the tools and understanding developer perceptions, a critical gap exists in understanding how developers actually use these tools in their daily workflows, and, crucially, where they struggle. This paper addresses part of this gap through a multi-phased investigation of developer interactions with an LLM-powered code editing and transformation feature, Transform Code, in an IDE widely used at Google. First, we analyze telemetry logs of the feature usage, revealing that frequent re-prompting can be an indicator of developer struggles with using Transform Code. Second, we conduct a qualitative analysis of unsatisfactory requests, identifying five key categories of information often missing from developer prompts. Finally, based on these findings, we propose and evaluate a tool, AutoPrompter, for automatically improving prompts by inferring missing information from the surrounding code context, leading to a 27% improvement in edit correctness on our test set.

From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions

Background and Context. The increasing integration of large language models (LLMs) in computing education presents an emerging challenge in understanding how students use LLMs and craft prompts to solve computational tasks. Prior research has used both qualitative and quantitative methods to analyze prompting behavior, but these approaches lack scalability or fail to effectively capture the semantic evolution of prompts. Objective. In this paper, we investigate whether students prompts can be systematically analyzed using propositional logic constraints. We examine whether this approach can identify patterns in prompt evolution, detect struggling students, and provide insights into effective and ineffective strategies. Method. We introduce Prompt2Constraints, a novel method that translates students prompts into logical constraints. The constraints are able to represent the intent of the prompts in succinct and quantifiable ways. We used this approach to analyze a dataset of 1,872 prompts from 203 students solving introductory programming tasks. Findings. We find that while successful and unsuccessful attempts tend to use a similar number of constraints overall, when students fail, they often modify their prompts more significantly, shifting problem-solving strategies midway. We also identify points where specific interventions could be most helpful to students for refining their prompts. Implications. This work offers a new and scalable way to detect students who struggle in solving natural language programming tasks. This work could be extended to investigate more complex tasks and integrated into programming tools to provide real-time support.

In-IDE Generation-based Information Support with a Large Language Model

Developers often face challenges in code understanding, which is crucial for building and maintaining high-quality software systems. Code comments and documentation can provide some context for the code, but are often scarce or missing. This challenge has become even more pressing with the rise of large language model (LLM) based code generation tools. To understand unfamiliar code, most software developers rely on general-purpose search engines to search through various programming information resources, which often requires multiple iterations of query rewriting and information foraging. More recently, developers have turned to online chatbots powered by LLMs, such as ChatGPT, which can provide more customized responses but also incur more overhead as developers need to communicate a significant amount of context to the LLM via a textual interface. In this study, we provide the investigation of an LLM-based conversational UI in the IDE. We aim to understand the promises and obstacles for tools powered by LLMs that are contextually aware, in that they automatically leverage the developer's programming context to answer queries. To this end, we develop an IDE Plugin that allows users to query back-ends such as OpenAI's GPT-3.5 and GPT-4 with high-level requests, like: explaining a highlighted section of code, explaining key domain-specific terms, or providing usage examples for an API. We conduct an exploratory user study with 32 participants to understand the usefulness and effectiveness, as well as individual preferences in the usage of, this LLM-powered information support tool. The study confirms that this approach can aid code understanding more effectively than web search, but the degree of the benefit differed by participants' experience levels.