Diagnosing diseases such as leukemia or anemia requires reliable counts of blood cells. Hematologists usually label and count microscopy images of blood cells manually. In many cases, however, cells in different maturity states are difficult to distinguish, and in combination with image noise and subjectivity, humans are prone to make labeling mistakes. This results in labels that are often not reproducible, which can directly affect the diagnoses. We introduce TIMELY, a probabilistic model that combines pseudotime inference methods with inhomogeneous hidden Markov trees, which addresses this challenge of label inconsistency. We show first on simulation data that TIMELY is able to identify and correct wrong labels with higher precision and recall than baseline methods for labeling correction. We then apply our method to two real-world datasets of blood cell data and show that TIMELY successfully finds inconsistent labels, thereby improving the quality of human-generated labels.

We investigate the relationship between class dependency and change propagation (cochange) in software written in Java. On the one hand, we find a strong correlation between dependency and cochange. Furthermore, we provide empirical evidence for the propagation of change along paths of dependency. These findings support the often alleged role of dependencies as propagators of change. On the other hand, we find that approximately half of all dependencies are never involved in cochanges and that the vast majority of cochanges pertain to only a small percentage of dependencies. This means that inferring the cochange characteristics of a software architecture solely from its dependency structure results in a severely distorted approximation of cochange characteristics. Any metric which uses dependencies alone to pass judgment on the evolvability of a piece of Java software is thus unreliable. As a consequence, we suggest to always take both the change characteristics and the dependency structure into account when evaluating software architecture.

In this paper we investigate the relationship between class dependency and change propagation in Java software. By analyzing 35 large Open Source Java projects, we find that in the majority of the projects more than half of the dependencies are never involved in change propagation. Furthermore, our analysis shows that only a few dependencies are transmitting the majority of change propagation events. An additional analysis reveals that this concentration cannot be explained by the different ages of the dependencies. The conclusion is that the dependency structure alone is a poor measure for the change dynamics. This contrasts with current literature.