Ruhe, GuentherAhmadi, Alireza2020-12-222020-12-222020-12Ahmadi, A. (2020). Question-and-answer community mining in software project management – A deep learning approach (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/112894Software project management (SPM) is one of the most dominant fields in Software Engineering (SE). During recent years, excessive growth in data science has brought a new research opportunity for supporting project managers, referred to as SPM Analytics. The majority of the field efforts are concerned with using projects' data in the estimation problems and mining the general public data in Requirement Engineering applications. However, in more general SE applications, Question and Answer (QA) communities such as StackOverflow have been known as a rich data source. While most studies in SPM analytics use traditional Machine Learning (ML) methods, in this thesis, a method named DeepQA-Miner based on Deep Neural Networks (DNNs) is proposed to mine SPM QA communities. Project Management StackExchange (PMSE), a well-known community for project managers, is targeted. It provides project managers with the opportunity to share their questions, making it a great candidate for characterizing practitioners' needs. The DeepQA-Miner method would pre-process the data and feed it into a multi-input multi-head network. The network receives different data parts separately, embeds the text internally, extracts the essential patterns, and classifies it for multi-purposes, leveraging a single shared knowledge base. More than 5000 questions at PMSE are accessed, classified through four different perspectives, and analyzed by their tone to formulate SPM practitioners' needs. The DeepQA-Miner's performance is compared with four baseline methods. Overall, DeepQA-Miner outperforms the other classifiers. Even though two of the traditional methods achieved slightly higher accuracy in one of the binary classification tasks, there is a remarkable improvement by the DeepQA-Miner in multi-class tasks. Furthermore, the findings provide potential directions for further research and development. As an application, the findings are compared with SPM education status quo resulting from SPM-related courses in Canada's top 10 universities. A set of considerations for reducing the existing gap between the industry needs and courses' agenda is proposed. As a contribution to Open Science, all data parts are being made publicly available: https://github.com/alirzahmadi/DeepQA-MinerengUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Question-And-Answer CommunitySoftware Repositories MiningSoftware Project ManagementDeep LearningMachine LearningComputer Sciencemaster thesis10.11575/PRISM/38488