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Improving Answers in the Stack Overflow Q & A Website

Tavakkoli, Mohammad Reza | 2016

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 48924 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Heydarnoori, Abbas
  7. Abstract:
  8. In recent years, most of the programmer’s problems are solved using Q&A websites, e.g. Stack Overflow. Since the number of users, questions and answers in these websites are rapidly increasing, manually moderating and enhancing the quality of the posts (specially the answers) seems to be impossible. Despite the various studies on issues with Q&A websites and facilitating their use, there has been no studies on the field of improving the quality of answers. In this study, we intend to provide an appropriate solution to enhance the problem of low-quality answers. In order to demonstrate the proposed approach, we recommend a tool called StImp, which is a plugin for Eclipse. Using this plugin, the questions that need to be answered are recognized and recommended to an expert, with respect to the code he is developing. Also a code is recommended to the programmer for posting. In order to find the factor that characterizes the questions which should be answered, we have studied the influence of 6 different properties of Stack Overflow problems and concluded that the affecting factor is the proportion of the sum of the scores of answers over the number of the times the question is viewed. Using this method we can find the questions which should be answered and persuade expert programmers to answer them. We have evaluated this method in two parts. In the first part, 5 developers used the plugin and evaluated its performance. In the second part, 15 experienced programmers evaluated the results of the plugin in Stack Overflow questions. Participants of the first part of evaluation assessed the proposed extension to be helpful in their contributions to Stack Overflow. Participants of the second part believed that answers submitted by the tool, have improved the quality of answers
  9. Keywords:
  10. Recommender System ; Query Refinement ; Programs Comprehension ; Improving Answer in Stack Overflow

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