A Hybrid LTR-based System via Social Context Embedding for Recommending Solutions of Software Bugs in Developer Communities

TL;DR

This paper presents a hybrid learning-to-rank system that uses social context embeddings from Stack Overflow to improve bug solution recommendations, achieving 78% accuracy in top-10 answer suggestions.

Contribution

It introduces a novel deep learning-based LTR model leveraging social context embeddings for more effective bug solution recommendations in developer communities.

Findings

Achieves 78% correct solutions in top-10 answer recommendations.

Utilizes deep learning and social context embeddings for improved accuracy.

Demonstrates effectiveness of data mining and machine learning in software bug resolution.

Abstract

Questions and Answering forums such as Stack Overflow play an important role in supporting software developers in finding answers to queries related to issues such as software errors and bugs. However, searching through a large set of candidate answers could be time consuming and may not lead to the best solution. In this research, the effectiveness of data mining models and machine learning techniques to solve this kind of problems is evaluated. We propose a recommender system to aid developers in finding solutions to their software bugs by carefully mining Stack Overflow. The proposed model leverages the knowledge available through crowdsourcing the Q&A available in Stack Overflow to recommend a solution to software bugs. We use deep learning techniques to construct the required Learning-to-Rank (LTR)-based model using the social context embedding the Stack Overflow features. Text mining, natural language processing and recommendation algorithms are used to extract, evaluate and recommend the best relevant bug solutions. Additionally, our model achieves nearly 78% correct solutions when recommending the 10 best answers for each question.