Demo Data from EDBT 2026 / reviewers in the wild / expert
Pavel Reich
dblp:344/1587
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · unresolved
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 testing · 56% Software maintenance and evolution · 36% Empirical software engineering · 8% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution
refactoring |
0.7 | 1 | 2023 | Testability Refactoring in Pull Requests: Patterns and Trends · ICSE 2023 |
Software testing
testability |
0.7 | 1 | 2023 | Testability Refactoring in Pull Requests: Patterns and Trends · ICSE 2023 |
Software testing
unit testing |
0.7 | 1 | 2023 | Testability Refactoring in Pull Requests: Patterns and Trends · ICSE 2023 |
Empirical software engineering
mining software repositories |
0.2 | 1 | 2023 | Testability Refactoring in Pull Requests: Patterns and Trends · ICSE 2023 |
Software maintenance and evolution › code review › code review analysis
pull request analysis |
0.2 | 1 | 2023 | Testability Refactoring in Pull Requests: Patterns and Trends · ICSE 2023 |
Methods — techniques the papers use, named apart from their topics
mining software repositories · 0.7manual code analysis · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Testability Refactoring in Pull Requests: Patterns and TrendsabstractTo create unit tests, it may be necessary to refactor the production code, e.g. by widening access to specific methods or by decomposing classes into smaller units that are easier to test independently. We report on an extensive study to understand such composite refactoring procedures for the purpose of improving testability. We collected and studied 346,841 java pull requests from 621 GitHub projects. First, we compared the atomic refactorings in two populations: pull requests with changed test-pairs (i.e. with co-changes in production and test code and thus potentially including testability refactoring) and pull requests without test-pairs. We found significantly more atomic refactorings in test-pairs pull requests, such as Change Variable Type Operation or Change Parameter Type. Second, we manually analyzed the code changes of 200 pull requests, where developers explicitly mention the terms “testability” or “refactor + test”. We identified ten composite refactoring procedures for the purpose of testability, which we call testability refactoring patterns. Third, we manually analyzed additional 524 test-pairs pull requests: both randomly selected and where we assumed to find testability refactorings, e.g. in pull requests about dependency or concurrency issues. About 25% of all analyzed pull requests actually included testability refactoring patterns. The most frequent were extract a method for override or for invocation, widen access to a method for invocation, and extract a class for invocation. We also report on frequent atomic refactorings which co-occur with the patterns and discuss the implications of our findings for research, practice, and education. Pavel Reich, Walid Maalej |
ICSE | 1 |