title: Canary
title: methodology for using social media to inform requirements modeling
titleType: Subtitle
Creators: name: Kanchev, Georgi
Publisher: name: Lancaster University
Year: 2018

description: Online discussions about software applications generate a large amount of
      requirementsrelated information. Social media serves as an extensive
      repository of user interaction related to software applications. Users
      discuss application features and express their sentiments about them in
      both qualitative (usually in natural language) and quantitative ways (for
      example, via votes). This information can potentially be usefully applied
      in requirements engineering; however currently, there are few systematic
      approaches for extracting such information. To address this gap, I applied
      a three-fold research approach in exploring interesting aspects of social
      media that can be useful to RE, pioneering a methodology for query based
      extraction of RE-related information from social media, and the systematic
      methodology for enriching established goal models with information
      extracted using Canary queries.
      
      First, a study of interaction among users about Google Maps on the forum
      Reddit. I highlight important artifacts relevant to requirements in these
      interactions. I discuss goal modeling as an archetypal requirements
      modeling approach and use that as a basis for enhancing requirements
      modeling with notions that capture user interaction. To back up my
      observations I systematically collect, annotate, and present empirical
      data on the structure and value of online discussion about software
      applications.
      
      Second, Canary, an approach for extracting and querying
      requirements-related information in online discussions. The highlight of
      my approach is a high-level query language that combines aspects of both
      requirements and discussion in online forums. I give the semantics of the
      query language in terms of relational databases and SQL. I demonstrate the
      usefulness of the language using examples on real data extracted from
      online discussions. My approach relies on human annotations of online
      discussions. I highlight the subtleties involved in interpreting the
      content in online discussions and the assumptions and choices I made to
      effectively address them. I demonstrate the feasibility of generating
      high-accuracy annotations by obtaining them from lay Amazon Mechanical
      Turk users.
      
      A topic of recent interest is how to apply crowdsourced information toward
      producing better software requirements. A research question that has
      received little attention so far is how to leverage crowdsourced
      information toward creating better-informed models of requirements.
      
      Third, I contribute a method following which information in online
      discussions may be leveraged toward constructing goal models. A salient
      feature of my method is that it applies high-level queries to draw out
      potentially relevant information from discussions. I also give a
      subjective logic-based method for deriving an ordering of the goals based
      on the amount of supporting and rebutting evidence in the discussions.
      Such an ordering can potentially be applied toward prioritizing goals for
      implementation.