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<p>The success of technical Q&amp;A sites such as Stack Overflow depends on two key factors: (a) active user participation and (b) the quality of the shared knowledge. Stack Overflow introduced an edit system that allows users to suggest improvements to posts (i.e., questions and answers) to enhance the quality of the content. However, users, such as post owners or site moderators, can reject these suggested edits by rollbacks due to unsatisfactory or low-quality edits, or because they violate edit guidelines. Unfortunately, subjectivity bias in determining whether an edit is satisfactory or unsatisfactory can lead to inconsistencies in the rollback decisions. For example, one user might accept the formatting of a method name (e.g., <Emphasis FontCategory="NonProportional">getActivity()</Emphasis>) as a code term, while another might reject it. Such inconsistencies can demotivate and frustrate users whose edits are rejected. Furthermore, several post owners prefer to keep their content unchanged and even resist necessary edits. As a result, they sometimes roll back necessary edits and revert posts to a flawed version, which violates editing guidelines. The problems mentioned above are further compounded by the lack of specific guidelines and tools to assist users in ensuring consistency in rollback actions. In this study, we investigate the types, prevalence, and impact of rollback edit inconsistencies and propose a solution to address them. The outcomes of this research are fivefold. First, we manually investigated 764 rollback edits (382 questions + 382 answers) and identified eight types of inconsistent rollback. Second, we surveyed 44 practitioners to assess the impact of rollback inconsistencies. More than 80% of the participants found our identified inconsistency types detrimental to post quality. Third, we developed rule-based algorithms and machine learning (ML) models to detect the eight types of rollback inconsistencies. Both approaches achieve over 90% accuracy. Fourth, we introduced a tool, <Emphasis FontCategory="NonProportional">iEdit</Emphasis>, which integrates these algorithms into a browser extension and assists Stack Overflow users during their edits. Fifth, we surveyed 16 Stack Overflow users to evaluate the effectiveness of <Emphasis FontCategory="NonProportional">iEdit</Emphasis>. The participants found the tool’s suggestions helpful in avoiding inconsistent rollback edits.</p>

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Automatic assistance to mitigate rollback inconsistencies in collaborative edits

  • 期刊论文

  • 发表日期：

    2025年12月13日

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  • Saikat Mondal,
  • Gias Uddin,
  • Chanchal K. Roy

摘要

The success of technical Q&A sites such as Stack Overflow depends on two key factors: (a) active user participation and (b) the quality of the shared knowledge. Stack Overflow introduced an edit system that allows users to suggest improvements to posts (i.e., questions and answers) to enhance the quality of the content. However, users, such as post owners or site moderators, can reject these suggested edits by rollbacks due to unsatisfactory or low-quality edits, or because they violate edit guidelines. Unfortunately, subjectivity bias in determining whether an edit is satisfactory or unsatisfactory can lead to inconsistencies in the rollback decisions. For example, one user might accept the formatting of a method name (e.g., getActivity()) as a code term, while another might reject it. Such inconsistencies can demotivate and frustrate users whose edits are rejected. Furthermore, several post owners prefer to keep their content unchanged and even resist necessary edits. As a result, they sometimes roll back necessary edits and revert posts to a flawed version, which violates editing guidelines. The problems mentioned above are further compounded by the lack of specific guidelines and tools to assist users in ensuring consistency in rollback actions. In this study, we investigate the types, prevalence, and impact of rollback edit inconsistencies and propose a solution to address them. The outcomes of this research are fivefold. First, we manually investigated 764 rollback edits (382 questions + 382 answers) and identified eight types of inconsistent rollback. Second, we surveyed 44 practitioners to assess the impact of rollback inconsistencies. More than 80% of the participants found our identified inconsistency types detrimental to post quality. Third, we developed rule-based algorithms and machine learning (ML) models to detect the eight types of rollback inconsistencies. Both approaches achieve over 90% accuracy. Fourth, we introduced a tool, iEdit, which integrates these algorithms into a browser extension and assists Stack Overflow users during their edits. Fifth, we surveyed 16 Stack Overflow users to evaluate the effectiveness of iEdit. The participants found the tool’s suggestions helpful in avoiding inconsistent rollback edits.