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ICSE 2023
Sun 14 - Sat 20 May 2023 Melbourne, Australia
Fri 19 May 2023 12:00 - 12:07 at Meeting Room 102 - Developers' forums Chair(s): Omar Haggag

Stack Overflow is a popular platform for developers to seek solutions to programming-related problems. However, prior studies identified that developers may suffer from the redundant, useless, and incomplete information retrieved by the Stack Overflow search engine. To help developers better utilize the Stack Overflow knowledge, researchers proposed tools to summarize answers to a Stack Overflow question. However, existing tools use hand-craft features to assess the usefulness of each answer sentence and fail to remove semantically redundant information in the result. Besides, existing tools only focus on a certain programming language and cannot retrieve up-to-date new posted knowledge from Stack Overflow. In this paper, we propose TECHSUMBOT, an automatic answer summary generation tool for a technical problem. Given a question, TECHSUMBOT first retrieves answers using the Stack Overflow search engine, then TECHSUMBOT 1) ranks each answers sentence based on the sentence’s usefulness, 2) estimates the centrality of each sentence to all candidates, and 3) removes the semantic redundant information. Finally, TECHSUMBOT returns the top 5 ranked answer sentences as the answer summary. We implement TECHSUMBOT in the form of a search engine website. To evaluate TECHSUMBOT in both automatic and manual manners, we construct the first Stack Overflow multi-answer summarization benchmark and design a user study to assess the effectiveness of TECHSUMBOT and state-of-the-art baselines from the NLP and SE domains. Both results indicate that the summaries generated by TECHSUMBOT are more diverse, useful, and similar to the ground truth summaries.

Video Link: https://youtube.com/watch?v=ozuJOp_vILM Replication Package: https://github.com/TechSumBot/TechSumBot

Fri 19 May

Displayed time zone: Hobart change

11:00 - 12:30
11:00
15m
Talk
Automatic prediction of rejected edits in Stack Overflow
Journal-First Papers
Saikat Mondal University of Saskatchewan, Gias Uddin University of Calgary, Canada, Chanchal K. Roy University of Saskatchewan
Link to publication DOI Pre-print
11:15
15m
Talk
Automated Summarization of Stack Overflow Posts
Technical Track
Bonan Kou Purdue University, Muhao Chen University of Southern California, Tianyi Zhang Purdue University
11:30
15m
Talk
Semi-Automatic, Inline and Collaborative Web Page Code Curations
Technical Track
Roy Rutishauser University of Zurich, André N. Meyer University of Zurich, Reid Holmes University of British Columbia, Thomas Fritz University of Zurich
11:45
15m
Talk
You Don’t Know Search: Helping Users Find Code by Automatically Evaluating Alternative Queries
SEIP - Software Engineering in Practice
Rijnard van Tonder Sourcegraph
12:00
7m
Talk
TECHSUMBOT: A Stack Overflow Answer Summarization Tool for Technical Query
DEMO - Demonstrations
Chengran Yang Singapore Management University, Bowen Xu Singapore Management University, Jiakun Liu Singapore Management University, David Lo Singapore Management University
12:07
8m
Talk
An empirical study of question discussions on Stack Overflow
Journal-First Papers
Wenhan Zhu University of Waterloo, Haoxiang Zhang Centre for Software Excellence at Huawei Canada, Ahmed E. Hassan Queen’s University, Michael W. Godfrey University of Waterloo, Canada
12:15
15m
Talk
Faster or Slower? Performance Mystery of Python Idioms Unveiled with Empirical Evidence
Technical Track
zejun zhang Australian National University, Zhenchang Xing , Xin Xia Huawei, Xiwei (Sherry) Xu CSIRO’s Data61, Liming Zhu CSIRO’s Data61, Qinghua Lu CSIRO’s Data61