Context: Software developers often ask questions on Technical Q&A forums like
Stack Overflow (SO) to seek solutions to their programming-related problems
(e.g., errors and unexpected behavior of code). Problem: Many questions miss
required code snippets due to the lack of readily available code, time
constraints, employer restrictions, confidentiality concerns, or uncertainty
about what code to share. Unfortunately, missing but required code snippets
prevent questions from getting prompt and appropriate solutions. Objective: We
plan to introduce GENCNIPPET, a tool designed to integrate with SO’s question
submission system. GENCNIPPET will generate relevant code examples (when
required) to support questions for their timely solutions. Methodology: We
first downloaded the SO April 2024 data dump, which contains 1.94 million
questions related to Python that have code snippets and 1.43 million questions
related to Java. Then, we filter these questions to identify those that
genuinely require code snippets using a state-of-the-art machine learning
model. Next, we select questions with positive scores to ensure high-quality
data. Our plan is to fine-tune Llama-3 models (e.g., Llama-3-8B), using 80% of
the selected questions for training and 10% for validation. The primary reasons
for choosing Llama models are their open-source accessibility and robust
fine-tuning capabilities, which are essential for deploying a freely accessible
tool. GENCNIPPET will be integrated with the SO question submission system as a
browser plugin. It will communicate with the fine-tuned model to generate code
snippets tailored to the target questions. The effectiveness of the generated
code examples will be assessed using automatic evaluation against ground truth,
user perspectives, and live (wild) testing in real-world scenarios.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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