VLDB 2026 Research / reviewers in the wild / expert
Anatoly Akkerman
dblp:15/2174
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 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% Empirical software engineering · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering
mining software repositories |
0.9 | 1 | 2025 | Metrics Driven Reengineering and Continuous Code Improvement at Meta · ASE 2025 |
Software maintenance and evolution
technical debt |
0.9 | 1 | 2025 | Metrics Driven Reengineering and Continuous Code Improvement at Meta · ASE 2025 |
Software maintenance and evolution
software reengineering |
0.3 | 1 | 2025 | Metrics Driven Reengineering and Continuous Code Improvement at Meta · ASE 2025 |
Methods — techniques the papers use, named apart from their topics
repository mining · 0.9mixed-methods · 0.9action research · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Metrics Driven Reengineering and Continuous Code Improvement at MetaabstractThe focus on rapid software delivery inevitably results in the accumulation of technical debt, which, in turn, affects quality and slows future development. Our primary aim is to discover how companies keep their codebases maintainable and how code improvements might be automated. Method: we investigate Meta practices by collaborating with engineers on code quality (via action research) and by analyzing rich source code change history using mixed-methods to reveal a range of practices used for continual improvement of the codebase. Results: Code improvements at Meta range from completely organic grass-roots done at the initiative of individual engineers, to regularly blocked time and engagement via gamification of Better Engineering (BE) work, to major explicit initiatives aimed at reengineering the complex parts of the codebase or deleting accumulations of dead code. Over 14% of changes are explicitly devoted to code improvement and the developers are given "badges" to acknowledge the type of work and the amount of effort. Based on the interactions with development teams we suggest metrics to help prioritization of code improvement efforts. Finally, our models of the impact of reengineering activities revealed substantial improvements in quality and speed and reductions in code complexity. Overall, code improvement activities are relatively effort intensive yet simple enough to be prime targets for automation. Audris Mockus, Peter C. Rigby, Rui Abreu 0001, Anatoly Akkerman, Yogesh Bhootada, Payal Bhuptani, Gurnit Ghardhora, Lan Hoang Dao, Chris Hawley, Renzhi He, Sagar Krishnamoorthy, Sergei Krauze, Anton Lunov, Dragos Martac, François Morin, Neil Mitchell, Venus Montes, Maher Saba, Matt Steiner, Andrea Valori, Shanchao Wang, Nachiappan Nagappan |
ASE | 4 |