Abstract
Onboarding new developers is a challenge for any software project. Addressing this challenge relies on human resources (e.g., having a senior developer write documentation or mentor the new developer). One promising solution is using annotated code tours. While this approach partially lifts the need for mentorship, it still requires a senior developer to write this interactive form of documentation. This paper argues that a Large Language Model (LLM) might help with this documentation process. Our approach is to record the stack trace between a failed test and a faulty method. We then extract code snippets from the methods in this stack trace using CodeQL, a static analysis tool and have them explained by gpt-3.5-turbo-1106, the LLM behind ChatGPT. Finally, we evaluate the quality of a sample of these generated tours using a checklist. We show that the automatic generation of code tours is feasible but has limitations like redundant and low-level explanations.
Original language | English |
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Title of host publication | 2024 ACM/IEEE International Workshop on NL-based Software Engineering (NLBSE ’24) |
Place of Publication | Lisbon, Portugal |
Publisher | ACM Press |
DOIs | |
Publication status | Published - Apr 2024 |
Event | 3rd Intl. Workshop on NL-based Software Engineering - Lisbon, Portugal Duration: 20 Apr 2024 → 20 Apr 2024 Conference number: 3 https://nlbse2024.github.io |
Conference
Conference | 3rd Intl. Workshop on NL-based Software Engineering |
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Abbreviated title | NLBSE '24 |
Country/Territory | Portugal |
City | Lisbon |
Period | 20/04/24 → 20/04/24 |
Internet address |