Blogs (1) >>
ICSE 2019
Sat 25 - Fri 31 May 2019 Montreal, QC, Canada
Thu 30 May 2019 11:30 - 11:50 at Place du Canada - Software Analytics Chair(s): Christian Bird

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Overtime, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task. In this paper, we address this challenge by identifying the information types presented in comments of OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy diverse needs of OSS stakeholders.

Thu 30 May

Displayed time zone: Eastern Time (US & Canada) change

11:00 - 12:30
(SEIP Talk) Take Control: (On the Unreasonable Effectiveness of Software Analytics)SEIPIndustry Program
Software Engineering in Practice
Tim Menzies North Carolina State University
Analysis and Detection of Information Types of Open Source Software Issue DiscussionsArtifacts AvailableTechnical Track
Technical Track
Deeksha M. Arya McGill University, Cheryl Wang McGill University, Jin L.C. Guo McGill University, Jinghui Cheng Polytechnique Montreal
DOI Pre-print
Automating Intention MiningJournal-First
Journal-First Papers
Qiao Huang , Xin Xia Monash University, David Lo Singapore Management University, Gail Murphy University of British Columbia
Leveraging Historical Associations between Requirements and Source Code to Identify Impacted ClassesJournal-First
Journal-First Papers
Davide Falessi California Polytechnic State University, Justin Roll Cal Poly, USA, Jin L.C. Guo McGill University, Jane Cleland-Huang University of Notre Dame
Towards Predicting the Impact of Software Changes on Building ActivitiesNIER
New Ideas and Emerging Results
Michele Tufano College of William and Mary, Hitesh Sajnani Microsoft , Kim Herzig Tools for Software Engineers, Microsoft
Discussion Period