A Hybrid LTR-based System via Social Context Embedding for Recommending Solutions of Software Bugs in Developer Communities
This system addresses the problem of efficiently finding relevant solutions to software bugs for developers, offering an incremental improvement over existing search methods.
The paper proposes a recommender system that mines Stack Overflow to help developers find solutions to software bugs. It leverages deep learning and social context embedding to construct a Learning-to-Rank (LTR)-based model, achieving nearly 78% correct solutions when recommending the top 10 answers for each question.
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.