Demo Data from SIGIR 2026 / reviewers in the wild / expert
Meysam Feghhi
dblp:179/6577
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2
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 |
Empirical software engineering · 56% Software testing · 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 |
0.3 | 1 | 2018 | Analyzing the effects of test driven development in GitHub · ICSE 2018 |
Software testing
test-driven development |
0.3 | 1 | 2018 | Analyzing the effects of test driven development in GitHub · ICSE 2018 |
Empirical software engineering
software quality and productivity |
0.1 | 1 | 2018 | Analyzing the effects of test driven development in GitHub · ICSE 2018 |
Methods — techniques the papers use, named apart from their topics
repository mining · 0.3comparative analysis · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Analyzing the effects of test driven development in GitHubabstractTesting is an integral part of the software development lifecycle, approached with varying degrees of rigor by different process models. Agile process models recommend Test Driven Development (TDD) as a key practice for reducing costs and improving code quality. The objective of this work is to perform a cost-benefit analysis of this practice. Previous work by Fucci et al. [2, 3] engaged in laboratory studies of developers actively engaged in test-driven development practices. Fucci et al. found little difference between test-first behaviour of TDD and test-later behaviour. To that end, we opted to conduct a study about TDD behaviours in the "wild" rather than in the laboratory. Thus we have conducted a comparative analysis of GitHub repositories that adopts TDD to a lesser or greater extent, in order to determine how TDD affects software development productivity and software quality. We classified GitHub repositories archived in 2015 in terms of how rigorously they practiced TDD, thus creating a TDD spectrum. We then matched and compared various subsets of these repositories on this TDD spectrum with control sets of equal size. The control sets were samples from all GitHub repositories that matched certain characteristics, and that contained at least one test file. We compared how the TDD sets differed from the control sets on the following characteristics: number of test files, average commit velocity, number of bug-referencing commits, number of issues recorded, usage of continuous integration, number of pull requests, and distribution of commits per author. We found that Java TDD projects were relatively rare. In addition, there were very few significant differences in any of the metrics we used to compare TDD-like and non-TDD projects; therefore, our results do not reveal any observable benefits from using TDD. Neil Borle, Meysam Feghhi, Eleni Stroulia, Russell Greiner, Abram Hindle |
ICSE | 2 |
| 2018 | Analyzing the effects of test driven development in GitHub
Neil Borle, Meysam Feghhi, Eleni Stroulia, Russell Greiner, Abram Hindle |
Empir. Softw. Eng. | 2 |