Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Mining Stack Overflow: a Recommender Systems-Based Model

Version 1 : Received: 10 August 2020 / Approved: 11 August 2020 / Online: 11 August 2020 (10:16:33 CEST)
Version 2 : Received: 11 August 2020 / Approved: 4 September 2020 / Online: 4 September 2020 (11:20:33 CEST)

How to cite: Harrag, F.; Khamliche, M. Mining Stack Overflow: a Recommender Systems-Based Model. Preprints 2020, 2020080265. Harrag, F.; Khamliche, M. Mining Stack Overflow: a Recommender Systems-Based Model. Preprints 2020, 2020080265.


In software development, developers received bug reports that describe the software bug. Developers find the cause of bug through reviewing the code and reproducing the abnormal behavior that can be considered as tedious and time-consuming processes. The developers need an automated system that incorporates large domain knowledge and recommends a solution for those bugs to ease on developers rather than spending more manual efforts to fixing the bugs or waiting on Q&A websites for other users to reply to them. Stack Overflow is a popular question-answer site that is focusing on programming issues, thus we can benefit knowledge available in this rich platform. This paper, presents a survey covering the methods in the field of mining software repositories. We propose an architecture to build a recommender System using the learning to rank approach. Deep learning is used to construct a model that solve the problem of learning to rank using stack overflow data. Text mining techniques were invested to extract, evaluate and recommend the answers that have the best relevance with the solution of this bug report.


Ecommender system; learning to rank; Mining software repositories; Text Mining; Deep learning; Stack Overflow


Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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