Summary of Leveraging Large Language Models For Efficient Failure Analysis in Game Development, by Leonardo Marini et al.
Leveraging Large Language Models for Efficient Failure Analysis in Game Development
by Leonardo Marini, Linus Gisslén, Alessandro Sestini
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to identifying which code change is responsible for a test failure in large-scale software development projects. The proposed method leverages Large Language Models (LLMs) to associate error messages with the corresponding code changes, enabling developers to quickly pinpoint the root cause of issues. The study demonstrates an accuracy rate of 71% on a dataset created from developer-reported issues at EA over one year. Additionally, a user study shows that the tool significantly reduces the time spent investigating issues by up to 60%. This research has significant implications for software development, particularly in AAA games where thousands of developers contribute to a single code base. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find bugs faster! Imagine you’re working on a huge game with many people contributing code. When tests fail, it’s hard to figure out which change caused the problem. The researchers came up with an idea to use special language models that can understand error messages and match them to specific code changes. They tested their approach and found it works pretty well (71% accurate!). They even did a study with real developers to see if the tool would be helpful, and it saved them a lot of time (up to 60%). This could make game development much easier! |
