Electronic Thesis/Dissertation


Predictive Model: Using Text Mining for Determining Factors Leading to High-Scoring Answers in Stack Overflow Open Access

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With the advent of knowledge-based economies, knowledge transfer within online forums has become increasingly important to the work of IT teams. Stack Overflow, for example, is an online community in which computer programmers can interact and consult with one another to achieve information flow efficiencies and bolster their reputations, which are numerical representations of their standings within the platform. The high volume of information available in Stack Overflow in the context of significant variance in members’ expertise and, hence, the quality of their posts hinders knowledge transfer and causes developers to waste valuable time locating good answers. Additionally, invalid answers can introduce security vulnerabilities and/or legal risks.By conducting text analytics and regression, this research presents a predictive model to optimize knowledge transfer among software developers. This model incorporates the identification of factors (e.g., good tagging, answer character count, tag frequency) that reliably lead to high-scoring answers in Stack Overflow. Upon applying natural language processing, the following variables were found to be significant: (a) the number of answers per question, (b) the cumulative tag score, (c) the cumulative comment score, and (d) the bags of words’ frequency. Additional methods were used to identify the factors that contribute to an answer being selected by the user who posted the question, the community at large, or both.Predicting what constitutes a good, accurate answer helps not only developers but also Stack Overflow, as the site can redesign its user interface to make better use of its knowledge repository to transfer knowledge more effectively. Likewise, companies who use the platform can decrease the amount of time and resources invested in training, fix software bugs faster, and complete challenging projects in a timely fashion.

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