Question-And-Answer Community Mining in Software Project Management – A Deep Learning Approach

Ahmadi, Alireza

Question-And-Answer Community Mining in Software Project Management – A Deep Learning Approach

dc.contributor.advisor	Ruhe, Guenther
dc.contributor.author	Ahmadi, Alireza
dc.contributor.committeemember	Moussavi, Mahmood
dc.contributor.committeemember	Rokne, Jon G.
dc.date	2021-02
dc.date.accessioned	2020-12-22T16:29:07Z
dc.date.available	2020-12-22T16:29:07Z
dc.date.issued	2020-12
dc.description.abstract	Software 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-Miner	en_US
dc.identifier.citation	Ahmadi, 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.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/38488
dc.identifier.uri	http://hdl.handle.net/1880/112894
dc.language.iso	eng	en_US
dc.publisher.faculty	Science	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University 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.	en_US
dc.subject	Question-And-Answer Community	en_US
dc.subject	Software Repositories Mining	en_US
dc.subject	Software Project Management	en_US
dc.subject	Deep Learning	en_US
dc.subject	Machine Learning	en_US
dc.subject.classification	Computer Science	en_US
dc.title	Question-And-Answer Community Mining in Software Project Management – A Deep Learning Approach	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Computer Science	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US