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Related Experiment Video

Updated: Aug 19, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Automatic prediction of rejected edits in Stack Overflow.

Saikat Mondal¹, Gias Uddin², Chanchal Roy¹

¹Software Research Lab, Department of Computer Science, University of Saskatchewan, Saskatoon, Canada.

Empirical Software Engineering

|December 5, 2022

Summary

This study helps Stack Overflow (SO) users improve their post edits by identifying common rejection reasons. An AI tool, EditEx, reduces rejected edits by 49% and cuts user workload in half.

Keywords:

Classification model Rejected edits Stack overflow Tool support User study

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Area of Science:

Computer Science
Software Engineering
Human-Computer Interaction

Background:

Stack Overflow (SO) is a vital platform for software developers seeking programming help.
Users can suggest edits to improve SO post quality, but many are rejected.
Rejected edits can demotivate users and hinder knowledge sharing.

Purpose of the Study:

To investigate reasons for rejected edits on Stack Overflow.
To develop a model for predicting edit rejections.
To create a tool assisting users in making acceptable edits.

Main Methods:

Manual analysis of 764 rejected edits to categorize 19 rejection reasons.
Extraction of 15 text and user-based features.
Development and evaluation of four machine learning models.
Implementation of an online tool, EditEx, integrated with the SO edit system.
User study with 20 participants comparing EditEx to the standard SO edit system.

Main Results:

The best machine learning model achieved 70.1% F1-score in predicting rejected edits.
EditEx demonstrated the potential to prevent 49% of rejected edits when integrated with the SO system.
EditEx reduced the median workload for suggesting edits by 50% compared to the standard SO system.
The treatment group found EditEx's suggested rejection reasons influential.

Conclusions:

EditEx effectively assists users in improving edit quality and reducing rejections on Stack Overflow.
The tool enhances the developer experience by lowering frustration and workload.
Findings suggest EditEx can significantly improve the collaborative editing process on knowledge-sharing platforms.