VLDB 2026 Research / reviewers in the wild / expert
Will Shackleton
dblp:362/2151
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
0009-0000-7159-6604ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 56% Compilers and program optimization · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Compilers and program optimization
dead code elimination |
0.7 | 1 | 2023 | Dead Code Removal at Meta: Automatically Deleting Millions of Lines of Code and Petabytes of Deprecated Data · ESEC/SIGSOFT FSE 2023 |
Software maintenance and evolution
software maintenance |
0.7 | 1 | 2023 | Dead Code Removal at Meta: Automatically Deleting Millions of Lines of Code and Petabytes of Deprecated Data · ESEC/SIGSOFT FSE 2023 |
Software maintenance and evolution
software ecosystems |
0.2 | 1 | 2023 | Dead Code Removal at Meta: Automatically Deleting Millions of Lines of Code and Petabytes of Deprecated Data · ESEC/SIGSOFT FSE 2023 |
Methods — techniques the papers use, named apart from their topics
static analysis · 0.7automated code removal · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Dead Code Removal at Meta: Automatically Deleting Millions of Lines of Code and Petabytes of Deprecated DataabstractSoftware constantly evolves in response to user needs: new features are built, deployed, mature and grow old, and eventually their usage drops enough to merit switching them off. In any large codebase, this feature lifecycle can naturally lead to retaining unnecessary code and data. Removing these respects users’ privacy expectations, as well as helping engineers to work efficiently. In prior software engineering research, we have found little evidence of code deprecation or dead-code removal at industrial scale. We describe Systematic Code and Asset Removal Framework (SCARF), a product deprecation system to assist engineers working in large codebases. SCARF identifies unused code and data assets and safely removes them. It operates fully automatically, including committing code and dropping database tables. It also gathers developer input where it cannot take automated actions, leading to further removals. Dead code removal increases the quality and consistency of large codebases, aids with knowledge management and improves reliability. SCARF has had an important impact at Meta. In the last year alone, it has removed petabytes of data across 12.8 million distinct assets, and deleted over 104 million lines of code. Will Shackleton, Katriel Cohn-Gordon, Peter C. Rigby, Rui Abreu 0001, James Gill, Nachiappan Nagappan, Karim Nakad, Ioannis Papagiannis, Luke Petre, Giorgi Megreli, Patrick Riggs, James Saindon |
ESEC/SIGSOFT FSE | 1 |