MSR 2023
Dates to be announced Melbourne, Australia
co-located with ICSE 2023
Mon 15 May 2023 12:14 - 12:26 at Meeting Room 109 - Documentation + Q&A I Chair(s): Ahmad Abdellatif

While having options could be liberating, too many options could lead to the sub-optimal solution being chosen. This is not an exception in the software engineering domain. Nowa- days, API has become imperative in making software developers’ life easier. APIs help developers implement a function faster and more efficiently. However, given the large number of open- source libraries to choose from, choosing the right APIs is not a simple task. Previous studies on API recommendation leverage natural language (query) to identify which API would be suitable for the given task. However, these studies only consider one source of input, i.e., GitHub or Stack Overflow, independently. There are no existing approaches that utilize Stack Overflow to help generate better API sequence recommendations from queries obtained from GitHub. Therefore, in this study, we aim to provide a framework that could improve the result of the API sequence recommendation by leveraging information from Stack Overflow. In this work, we propose PICASO, which leverages a bi- encoder to do contrastive learning and a cross-encoder to build a classification model in order to find a semantically similar Stack Overflow given an annotation (i.e., code comment). Subsequently, PICASO then uses the Stack Overflow’s title as a query expansion. PICASO then uses the extended queries to fine-tune a CodeBERT, resulting in an API sequence generation model. Based on our experiments, we found that incorporating the Stack Overflow title into CodeBERT would improve the performance of API sequence generation’s BLEU-4 score by 10.8%.

Mon 15 May

Displayed time zone: Hobart change

11:50 - 12:35
Documentation + Q&A IData and Tool Showcase Track / Technical Papers at Meeting Room 109
Chair(s): Ahmad Abdellatif Concordia University
Evaluating Software Documentation Quality
Technical Papers
Henry Tang University of Alberta, Sarah Nadi University of Alberta
What Do Users Ask in Open-Source AI Repositories? An Empirical Study of GitHub Issues
Technical Papers
Zhou Yang Singapore Management University, Chenyu Wang Singapore Management University, Jieke Shi Singapore Management University, Thong Hoang CSIRO's Data61, Pavneet Singh Kochhar Microsoft, Qinghua Lu CSIRO’s Data61, Zhenchang Xing , David Lo Singapore Management University
PICASO: Enhancing API Recommendations with Relevant Stack Overflow Posts
Technical Papers
Ivana Clairine Irsan Singapore Management University, Ting Zhang Singapore Management University, Ferdian Thung Singapore Management University, Kisub Kim Singapore Management University, David Lo Singapore Management University
GIRT-Data: Sampling GitHub Issue Report Templates
Data and Tool Showcase Track
Nafiseh Nikehgbal Sharif University of Technology, Amir Hossein Kargaran LMU Munich, Abbas Heydarnoori Bowling Green State University, Hinrich Schütze LMU Munich