Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities

Core Concepts

In this research, the authors address the challenges of duplicate question retrieval and confirmation time prediction in software communities using innovative methods that outperform existing baselines.

Abstract

This study focuses on improving the efficiency of moderators in identifying duplicate questions and predicting confirmation times. By leveraging text and network-based features, the proposed methods show significant performance improvements over state-of-the-art techniques. The research highlights the importance of addressing these challenges in community question answering platforms to enhance user experience and reduce manual efforts.
The study introduces a Siamese neural network approach for duplicate question retrieval, achieving superior results compared to existing models like DupPredictor and DUPE. Additionally, for duplicate confirmation time prediction, both standard machine learning models and neural networks are utilized with text and graph-based features, demonstrating statistically significant improvements.
The dataset used consists of questions from the askubuntu platform, focusing on duplicates within the Ubuntu ecosystem. The research provides insights into handling duplicate questions efficiently by combining text embeddings with network features derived from tag co-occurrence networks.
Overall, this study contributes to enhancing the functionality of community question answering platforms by streamlining processes related to duplicate question identification and confirmation time prediction.

Stats

Our method outperforms DupPredictor [33] and DUPE [1] by 5% and 7% respectively.
We obtain Spearman’s rank correlation of 0.20 and 0.213 (statistically significant) for text and graph based features respectively.

Quotes

Key Insights Distilled From

Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities

by Rima Hazra,D... at arxiv.org 03-06-2024

https://arxiv.org/pdf/2309.05035.pdf

Deeper Inquiries

How can these methods be adapted to other community question answering platforms?

These methods can be adapted to other community question answering platforms by following a similar approach of utilizing text and network-based features for duplicate question retrieval. The key steps involve:

Text Embeddings: Use pre-trained models or train models specific to the platform to generate embeddings for question titles and bodies.
Network Features: Construct a tag co-occurrence network or any relevant network structure based on the platform's data to extract additional features.
Model Architecture: Implement Siamese neural networks or similar architectures that combine text and network embeddings for better performance.
Candidate Set Generation: Develop strategies like negative sampling based on bucketing techniques, tag similarity, and answer availability to create candidate sets efficiently.

What are the potential implications of automating duplicate question retrieval in software communities?

Automating duplicate question retrieval in software communities can have several significant implications:

Improved User Experience: Users get quicker access to relevant information as they are directed towards existing answers instead of waiting for new responses.
Reduced Redundancy: Automation helps reduce redundant questions, making it easier for moderators and users alike to navigate through the platform effectively.
Time Efficiency: Moderators save time by not having to manually identify duplicates, allowing them to focus on more critical tasks within the community.
Enhanced Knowledge Sharing: By consolidating information into fewer threads, knowledge sharing becomes more streamlined and accessible.

How can temporal characteristics of closed questions be leveraged to improve future predictions?

Leveraging temporal characteristics of closed questions can enhance future predictions in various ways:

Trend Analysis: Analyzing patterns in closure times over time can help identify trends related to user behavior or content relevance changes.
Feature Engineering: Incorporating timestamps as features in predictive models allows capturing seasonality or periodicity effects influencing closure times.
Predictive Modeling Refinement: Models trained with historical closure data combined with temporal features enable more accurate prediction of future confirmation times.
4Dynamic Adjustments: Real-time monitoring of closure trends enables dynamic adjustments in prediction algorithms based on evolving user engagement patterns.

By integrating temporal aspects into predictive modeling frameworks, software communities can optimize their operations and provide more timely assistance while managing duplicate questions effectively over time."