The Influence of Organizational Structure on Software Quality
Nachiappan Nagappan et al. (via Graham Lee):
In our case study, the organizational metrics when applied to data from Windows Vista were statistically significant predictors of failure-proneness. The precision and recall measures for identifying failure-prone binaries, using the organizational metrics, was significantly higher than using traditional metrics like churn, complexity, coverage, dependencies, and pre-release bug measures that have been used to date to predict failure-proneness. Our results provide empirical evidence that the organizational metrics are related to, and are effective predictors of failure-proneness.
The full paper is available from Microsoft Research.
Update (2019-12-20): August Lilleaas (via Hacker News):
The distance to decision makers and the number of developers working on a project is clearly and unambiguously the issue that is the best predictor of future problems with a code base.
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Another shocking discovery for me personally, is that the only one that I've actually used myself - code coverage - has the lowest recall. It has a high precision, so bad code coverage does mean a high chance of bugs, i.e. low amount of false flags. But with a low recall, there are lots of bug that code coverage doesn't actually catch.
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In the replicated study the predictive value of organizational structure is not as high. Out of 4 measured models, it gets the 2nd highest precision and the 3rd highest recall. The study itself does conclude that organizational complexity as a bug prediction method is worth investigating further. The study is also based on individual functions in C/C++, and not entire modules like Microsoft Research did, which can be a reason for at least a part of the discrepancy.
