SER&IP '21
Fri 4 Jun 2021
co-located with ICSE 2021
Fri 4 Jun 2021 20:05 - 20:20 at SER&IP Room - Session 2 Chair(s): Chetan Bansal

Usually, managers or technical leaders in software projects assign issues manually. This task may become more complex as more detailed is the issue description. This complexity can also make the process more prone to errors (misassignments) and time-consuming. In the literature, many studies aim to address this problem by using machine learning strategies. Although there is no specific solution that works for all companies, experience reports are useful to guide the choices in industrial auto-assignment projects. This paper presents an industrial initiative conducted in a global electronics company that aims to minimize the time spent and the errors that can arise in the issue assignment process. As main contributions, we present a literature review, an industrial report comparing different algorithms, and lessons learned during the project.

SER&IP@ICSE'21 - Issue Auto-Assignment in Software Projects with Machine Learning Techniques (Slides) (IssueAutoAssign-SER&IP-2021.pdf)2.78MiB

Fri 4 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

19:00 - 21:05
Session 2SER&IP '21 at SER&IP Room
Chair(s): Chetan Bansal Microsoft Research
19:00
45m
Keynote
Work-from-home during and after COVID 19
SER&IP '21
Sonia Jaffe Microsoft
19:50
15m
Talk
Exploring the Dimensions of University-Company Collaborations: Research, Talent, and Beyond in a Chaotic COVID-19 World
SER&IP '21
Steven D. Fraser Innoxec, Dennis Mancl MSWX Software Experts
Pre-print
20:05
15m
Talk
Issue Auto-Assignment in Software Projects with Machine Learning Techniques
SER&IP '21
Pre-print Media Attached File Attached
20:20
15m
Talk
Can GraphQL Replace REST? A Study of Their Efficiency and Viability
SER&IP '21
Sri Lakshmi Vadlamani Carleton University, Benjamin Emdon Carleton University, Joshua Arts Carleton University, Olga Baysal Carleton University
Pre-print Media Attached
20:35
15m
Talk
Leveraging Data Scientists and Business Expectations during the COVID-19 Pandemic
SER&IP '21
Wellington Rodrigo Monteiro Pontifícia Universidade Católica do Paraná (PUCPR), Márcio Leandro Do Prado Pontifícia Universidade Católica do Paraná, Gilberto Reynoso-Meza Pontifícia Universidade Católica do Paraná
File Attached
20:50
15m
Talk
On The Gap Between Software Maintenance Theory and Practitioners' Approache
SER&IP '21
Mívian Ferreira Universidade Federal de Minas Gerais, Mariza Bigonha Professor at Federal University of Minas Gerais, Kecia Ferreira Federal Center for Technological Education of Minas Gerais
Pre-print