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Comments are valuable especially for program understanding and maintenance, but do developers comment their code? To which extent do they add comments or adapt them when they evolve the code? We examine the question whether source code and associated comments are really changed together along the evolutionary history of a software system. In this paper, we describe an approach to map code and comments to observe their co-evolution over multiple versions. We investigated three open source systems (i.e., ArgoUML, Azureus, and JDT Core) and describe how comments and code co-evolved over time. Some of our findings show that: 1) newly added code|despite its growth rate|barely gets commented; 2) class and method declarations are commented most frequently but far less, for example, method calls; and 3) that 97% of comment changes are done in the same revision as the associated source code change.

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Abstract. Code clones have long been recognized as bad smells in software systems and are considered to cause maintenance problems during evolution. It is broadly assumed that the more clones two files share, the more often they have to be changed together. This relation between clones and change couplings has been postulated but neither demonstrated nor quantified yet. However, given such a relation it would simplify the identification of restructuring candidates and reduce change couplings. In this paper, we examine this relation and discuss if a correlation between code clones and change couplings can be verified. For that, we propose a framework to examine code clones and relate them to change couplings taken from release history analysis. We validated our framework with the open source project Mozilla and the results of the validation show that although the relation is statistically unverifiable it derives a reasonable amount of cases where the relation exists. Therefore, to discover clone candidates for restructuring we additionally propose a set of metrics and a visualization technique. This allows one to spot where a correlation between cloning and change coupling exists and, as a result, which files should be restructured to ease further evolution.

Source code comments are a valuable instrument to preserve design decisions and to communicate the intent of the code to programmers and maintainers. Nevertheless, commenting source code and keeping them up-to-date is often neglected for reasons of time or programmers obliviousness. In this paper, we investigate the question whether developers comment their code and to which extent they add comments or adapt them when they evolve the code. We present an approach to associate comments with source code entities to track their co-evolution over multiple versions. A set of heuristics are used to decide whether a comment is associated to its preceding or its succeeding source code entity. We analyzed the co-evolution of code and comments in eight different open source and closed source software systems. We found with statistical significance that (1) the relative amount of comments and source code grows at about the same rate; (2) the type of a source code entity, such as a method declaration or an if-statement, has a significant influence on whether or not it gets commented; (3) in six out of the eight systems, code and comments co-evolve in 90 percent of the cases; and (4) surprisingly, API changes and comments do not co-evolve but they are re-documented in a later revision. As a result, our approach enables a quantitative assessment of the commenting process in a software system. We can, therefore, leverage the results to provide feedback during development to increase the awareness when to add comments or when to adapt comments because of source code changes.

The reasons why software is changed are manyfold; new features are added, bugs have to be fixed, or the consistency of coding rules has to be re-established. Since there are many types of of source code changes we want to explore whether they appear frequently together in time and whether they describe specific development activities. We describe a semi-automated approach to discover patterns of such change types using agglomerative hierarchical clustering. We extracted source code changes of one commercial and two open-source software systems and applied the clustering. We found that change type patterns do describe development activities and affect the control flow, the exception flow, or change the API. Discovering Patterns of Change Types

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