  Many software metrics are designed to measure aspects that are believed to be
related to software quality. Static software metrics, e.g., size, complexity
and coupling are used in defect prediction research as well as software quality
models to evaluate software quality. While this indicates a relationship
between quality and software metrics, the extent of it is not well understood.
Moreover, recent studies found that complexity metrics may be unreliable
indicators for understandability of the source code. To explore this
relationship, we leverage the intent of developers about what constitutes a
quality improvement in their own code base. We manually classify a randomized
sample of 2,533 commits from 54 Java open source projects as quality improving
depending on the intent of the developer by inspecting the commit message. We
distinguish between perfective and corrective maintenance via predefined
guidelines and use this data as ground truth for the fine-tuning of a
state-of-the art deep learning model for natural language processing. We use
the model to increase our data set to 125,482 commits. Based on the resulting
data set, we investigate the differences in size and 14 static source code
metrics between changes that increase quality, as indicated by the developer,
and other changes. We find that quality improving commits are smaller than
other commits. Perfective changes have a positive impact on static source code
metrics while corrective changes do tend to add complexity. Furthermore, we
find that files which are the target of perfective maintenance already have a
lower median complexity than other files. Our study results provide empirical
evidence for which static source code metrics capture quality improvement from
the developers point of view. This has implications for program understanding
as well as code smell detection and recommender systems.
