Audris Mockus

Audris Mockus

Any global mirror of open-source version control is incomplete, but the reasons a commit is missing are not interchangeable: a project may have force-pushed it away (so it no longer exists upstream), or the mirror may never have ingested it (a true collection gap). We release a commit-provenance dataset that separates these cases at scale by comparing two views of the same commit graph: the GHArchive event stream, a historical witness of what GitHub advertised at push time, and the World of Code (WoC) V2604 object database, an accumulated union of periodic fetches that never deletes a collected commit. Walking each reference's PushEvent chain reconstructs force-pushes structurally (a before that is not the prior head breaks the fast-forward chain; no recorded flag exists across eras), and joining every advertised commit against WoC membership yields a three-way label. Over 1,118,116,350 advertised commits, 53.35% are present in WoC, 6.47% are rewritten (orphaned by a later force-push, an upstream edit and a correct absence), and 40.18% are never-ingested (the candidate collection gap). About one missing-commit case in fifteen is a rewrite the project erased, not a mirror gap. We release the force-push witness (166,710,831 events over 19,926,250 repositories) and a per-project rollup (78,125,788 repositories; force-pushing observed in 25.47%, a rewritten commit in 12.85%). We treat the 40% never-ingested share as an upper bound on the collection gap and state five reasons it overcounts. The dataset makes mirror-completeness reporting honest, flags rewritten commits as duplicate patch sources for contribution counting, and corrects a 10.82% corpus-wide undercount that history editing imposes on commit-based productivity, with a released per-project correction factor. All artifacts ship as a self-contained, independently hosted replication package keyed to WoC V2604.

Yalin Liu, Kosay Jabre, Rui Abreu et al.

Predictions by machine learning (ML) and artificial intelligence (AI) models are often received skeptically unless they are paired with intelligible explanations. In the context of just-in-time defect prediction, highlighting small portions of a software change (diff) -- beyond rule-based lints -- where risk may be concentrated has not yet been extensively investigated. In this work, we leverage attention weights from an LLM-based Diff Risk Score (DRS) model to highlight parts of a diff that the model focuses on when predicting risk. We aggregate token-level attention into interpretable code units (lines, hunks, and files), and present the top-K units to developers as a lightweight form of guidance during code review. We evaluate our approach using expert-labeled changes that have caused real outages. Results show that the highlighted snippets cover expert-labeled outage-causing change lines 53.85% of the time when highlighting the top-2 hunks, while requiring developers to review 26.28% of the changed lines on average. Because attention is produced during standard model inference, the approach is scalable for large development workflows and can be surfaced in the code review UI with low additional latency.