Conference proceeding
Empirical Standards for Repository Mining
2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR), pp 142-143
May 2022
Abstract
The purpose of scholarly peer review is to evaluate the quality of scientific manuscripts. However, study after study demonstrates that peer review neither effectively nor reliably assesses research quality. Empirical standards attempt to address this problem by modelling a scientific community's expectations for each kind of empirical study conducted in that community. This should enhance not only the quality of research but also the reliability and pre-dictability of peer review, as scientists adopt the standards in both their researcher and reviewer roles. However, these improvements depend on the quality and adoption of the standards. This tutorial will therefore present the empirical standard for mining software repositories, both to communicate its contents and to get feedback from the attendees. The tutorial will be organized into three parts: (1) brief overview of the empirical standards project; (2) detailed presentation of the repository mining standard; (3) discussion and suggestions for improvement.
Metrics
Details
- Title
- Empirical Standards for Repository Mining
- Creators
- Preetha Chatterjee - Drexel University,Philadelphia,PA,USATushar Sharma - Dalhousie University,Halifax,NS,CanadaPaul Ralph - Dalhousie University,Halifax,NS,Canada
- Publication Details
- 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR), pp 142-143
- Publisher
- Association for Computing Machinery (ACM)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000850208000022
- Scopus ID
- 2-s2.0-85134081291
- Other Identifier
- 991019173434004721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Software Engineering