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Many software metrics are designed to measure aspects that are believed to berelated to software quality. Static software metrics, e.g., size, complexityand coupling are used in defect prediction research as well as software qualitymodels to evaluate software quality. While this indicates a relationshipbetween quality and software metrics, the extent of it is not well understood.Moreover, recent studies found that complexity metrics may be unreliableindicators for understandability of the source code. To explore thisrelationship, we leverage the intent of developers about what constitutes aquality improvement in their own code base. We manually classify a randomizedsample of 2,533 commits from 54 Java open source projects as quality improvingdepending on the intent of the developer by inspecting the commit message. Wedistinguish between perfective and corrective maintenance via predefinedguidelines and use this data as ground truth for the fine-tuning of astate-of-the art deep learning model for natural language processing. We usethe model to increase our data set to 125,482 commits. Based on the resultingdata set, we investigate the differences in size and 14 static source codemetrics between changes that increase quality, as indicated by the developer,and other changes. We find that quality improving commits are smaller thanother commits. Perfective changes have a positive impact on static source codemetrics while corrective changes do tend to add complexity. Furthermore, wefind that files which are the target of perfective maintenance already have alower median complexity than other files. Our study results provide empiricalevidence for which static source code metrics capture quality improvement fromthe developers point of view. This has implications for program understandingas well as code smell detection and recommender systems.

What really changes when developers intend to improve their source code:  a commit-level study of static metric value and static analysis warning  changes