all AI news
Technical Reports Compilation: Detecting the Fire Drill Anti-pattern Using Source Code and Issue-Tracking Data. (arXiv:2104.15090v7 [cs.SE] UPDATED)
July 25, 2022, 1:11 a.m. | Sebastian Hönel
stat.ML updates on arXiv.org arxiv.org
Detecting the presence of project management anti-patterns (AP) currently
requires experts on the matter and is an expensive endeavor. Worse, experts may
introduce their individual subjectivity or bias. Using the Fire Drill AP, we
first introduce a novel way to translate descriptions into detectable AP that
are comprised of arbitrary metrics and events such as logged time or
maintenance activities, which are mined from the underlying source code or
issue-tracking data, thus making the description objective as it becomes
data-based. …
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Software Engineer, Data Tools - Full Stack
@ DoorDash | Pune, India
Senior Data Analyst
@ Artsy | New York City