Demo Data from EDBT 2026 / reviewers in the wild / expert
Jeff Hong
dblp:119/7050
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1
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 maintenance and evolution · 38% Requirements engineering and software design · 12% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering
developer studies |
0.1 | 1 | 2012 | Structural Complexity and Programmer Team Strategy: An Experimental Test · IEEE Trans. Software Eng. 2012 |
Software maintenance and evolution
software maintenance |
0.1 | 1 | 2012 | Structural Complexity and Programmer Team Strategy: An Experimental Test · IEEE Trans. Software Eng. 2012 |
Requirements engineering and software design › software design principles
coupling and cohesion |
0.0 | 1 | 2012 | Structural Complexity and Programmer Team Strategy: An Experimental Test · IEEE Trans. Software Eng. 2012 |
Empirical software engineering
software metrics |
0.0 | 1 | 2012 | Structural Complexity and Programmer Team Strategy: An Experimental Test · IEEE Trans. Software Eng. 2012 |
Methods — techniques the papers use, named apart from their topics
distributed cognition · 0.1controlled experiment · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Structural Complexity and Programmer Team Strategy: An Experimental TestabstractThis study develops and empirically tests the idea that the impact of structural complexity on perfective maintenance of object-oriented software is significantly determined by the team strategy of programmers (independent or collaborative). We analyzed two key dimensions of software structure, coupling and cohesion, with respect to the maintenance effort and the perceived ease-of-maintenance by pairs of programmers. Hypotheses based on the distributed cognition and task interdependence theoretical frameworks were tested using data collected from a controlled lab experiment employing professional programmers. The results show a significant interaction effect between coupling, cohesion, and programmer team strategy on both maintenance effort and perceived ease-of-maintenance. Highly cohesive and low-coupled programs required lower maintenance effort and were perceived to be easier to maintain than the low-cohesive programs and high-coupled programs. Further, our results would predict that managers who strategically allocate maintenance tasks to either independent or collaborative programming teams depending on the structural complexity of software could lower their team's maintenance effort by as much as 70 percent over managers who use simple uniform resource allocation policies. These results highlight the importance of achieving congruence between team strategies employed by collaborating programmers and the structural complexity of software. Narayan Ramasubbu, Chris F. Kemerer, Jeff Hong |
IEEE Trans. Software Eng. | 3 |