Demo Data from EDBT 2026 / reviewers in the wild / expert
Sean Tucker
dblp:38/6861
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2010
—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 · 50% Software testing · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering › mining software repositories
bug repositories |
0.1 | 1 | 2008 | AFID: an automated fault identification tool · ISSTA 2008 |
Software testing
test generation |
0.1 | 1 | 2008 | AFID: an automated fault identification tool · ISSTA 2008 |
Methods — techniques the papers use, named apart from their topics
automated fault identification · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | AFID: an automated approach to collecting software faultsabstractWe present a new approach for creating repositories of real software faults. We have developed a tool, the Automatic Fault IDentification Tool (AFID), that implements this approach. AFID records both a fault revealing test case and a faulty version of the source code for any crashing faults that the developer discovers and a fault correcting source code change for any crashing faults that the developer corrects. The test cases are a significant contribution, because they enable new research that explores the dynamic behaviors of the software faults. AFID uses an operating system level monitoring mechanism to monitor both the compilation and execution of the application. This technique makes it straightforward for AFID to support a wide range of programming languages and compilers. We present our experience using AFID in a controlled case study and in a real development environment to collect software faults in the internal development of our group’s compiler. The case studies collected several real software faults and validated the basic approach. The longer term internal study revealed weaknesses in using the original version of AFID for real development. This experience led to a number of refinements to the tool for use in real software development. We have collected over 20 real software faults in large programs and continue to collect software faults. Alex Edwards, Sean Tucker, Brian Demsky |
Autom. Softw. Eng. | 2 |
| 2008 | AFID: an automated fault identification toolabstractWe present the Automatic Fault IDentification Tool (AFID). AFID automatically constructs repositories of real software faults by monitoring the software development process. AFID records both a fault revealing test case and a faulty version of the source code for any crashing faults that the developer discovers and a fault correcting source code change for any crashing faults that the developer corrects. The test cases are a significant contribution, because they enable new research that explores the dynamic behaviors of the software faults. Alex Edwards, Sean Tucker, Sébastien Worms, Rahul Vaidya, Brian Demsky |
ISSTA | 2 |