Code duplication or copying a code fragment and then reuse by pasting with or without any modifications is a well known code smell in software maintenance. Several studies show that about 5 % to 20 % of a software systems can contain duplicated code, which is basically the results of copying existing code fragments and using then by pasting with or without minor modifications. One of the major shortcomings of such duplicated fragments is that if a bug is detected in a code fragment, all the other fragments similar to it should be investigated to check the possible existence of the same bug in the similar fragments. Refactoring of the duplicated code is another prime issue in software maintenance although several studies claim that refactoring of certain clones are not desirable and there is a risk of removing them. However, it is also widely agreed that clones should at least be detected. In this paper, we survey the state of the art in clone detection research. First, we describe the clone terms commonly used in the literature along with their corresponding mappings to the commonly used clone types. Second, we provide a review of the existing

Understanding the run-time behavior of object-oriented legacy systems is a complex task due to factors such as late binding and polymorphism. Current approaches extract and use information from the complete execution trace of a system. The sheer size and complexity of such traces make their handling, storage, and analysis difficult. Current software systems which run almost non-stop do not permit such a full analysis. In this paper we present a lightweight approach based on the extraction of a condensed amount of information, e.g., measurements, that does not require a full trace. Using this condensed information, we propose a visualization approach which allows us to identify and understand certain aspects of the objects' lifetime such as their role played in the creation of other objects and the communication architecture they support.

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. 1

Duplication of code is a common phenomenon in the development and maintenance of large software systems. The detection and removal of duplicated code has become a standard activity during the refactoring phases of a software life-cycle. However, code duplication identification tends to produce large amounts of data making the understanding of the duplication situation as a whole difficult. Reengineers can easily lose sight of the forest for the trees. There is a need to support a qualitative analysis of the duplicated code. In this paper we propose a number of visualizations of duplicated source elements that support reengineers in answering questions, e.g., which parts of the system are connected by copied code or which parts of the system are copied the most.

Versioning systems such as CVS or Subversion exhibit a large potential to investigate the evolution of software systems. They are used to record the development steps of software systems as they make it possible to reconstruct the whole evolution of single files. However, they provide no good means to understand how much a certain file has been changed over time and by whom. In this paper we present an approach to visualize files using fractal figures, which (1) convey the overall development effort, (2) illustrate the distribution of the effort among various developers, and (3) allow files to be categorized in terms of the distribution of the effort following gestalt principles. Our approach allows us to discover files of high development efforts in terms of team size and effort intensity of individual developers. The visualizations allow an analyst or a project manager to get first insights into team structures and code ownership principles. We have analyzed Mozilla as a case study and we show some of the recovered team development patterns in this paper as a validation of our approach. 1.

Operator Figure 4: A Class Hierarchy History Complexity View of the evolution of five hierarchies from the Jun case study. The cyan nodes and edges denote removed classes and inheritance relationships.

Understanding sets of classes, or packages, is an important activity in the development and reengineering of large object-oriented systems. Packages represent the coarse-grained structure of an application. They are artefacts to deploy and structure software, and therefore more than a simple generalization of classes. The relationships between packages and their contained classes are key in the decomposition of an application and its (re)-modularisation. However, it is difficult to quickly grasp the structure of a package and to understand how a package interacts with the rest of the system. We tackle this problem using butterfly visualizations, i.e., dedicated radar charts built from simple package metrics based on a language-independent meta-model. We illustrate our approach on two applications and show how we can retrieve the relevant characteristics of packages.

Without a clear understanding of how features of a software system are implemented, a maintenance change in one part of the code may risk adversely affecting other features. Feature implementation and relationships between features are not explicit in the code. To address this problem, we propose an interactive 3D visualization technique based on a combination of static and dynamic analysis which enables the software developer to step through visual representations of execution traces. We visualize dynamic behaviors of execution traces in terms of object creations and interactions and represent this in the context of a static classhierarchy view of a system. We describe how we apply our approach to a case study to visualize and identify common parts of the code that are active during feature execution. 1

Versioning systems such as CVS exhibit a large potential to investigate and understand the evolution of large software systems. Bug Reporting systems such as Bugzilla help to understand which parts of the system are affected by problems. In this article we present a novel visual approach to uncover the relationship between evolving software and the way it is affected by software bugs. By visually putting the two aspects close to each other, we can characterize the evolution of software artifacts. We validate our approach on 3 very large open source software systems.

To understand the evolution of software researchers have developed a plethora of tools to parse, model, and analyze the history of systems. Despite their usefulness, a common downside of such tools is that their use comes with many strings attached, such as installation, data formats, usability, etc. The result is that many tools are only used by their creators, which is detrimental to cross-fertilization of research ideas and collaborative analysis. In this paper we present the Churrasco framework, which supports software evolution modeling, visualization and analysis through a web interface. The user provides only the URL of the SubVersion repository to be analyzed and, if available, of the corresponding bug tracking system. Churrasco processes the given data and automatically creates and stores an evolutionary model in a centralized database. This database, called Meta-base is connected to Churrasco through object-relational persistence. The persistency mechanism is meta-described in terms of the EMOF meta-metamodel and automatically generated based on any given evolutionary meta-model. In case the meta-model changes, the persistency mechanism is automatically updated. After providing a detailed description of Churrasco, we provide evidence, by means of an example scenario, that it allows for collaborative software evolution analysis, based on visualizations available on our analysis web portal. 1