Code cloning is not only assumed to inflate maintenance costs but also considered defect-prone as inconsistent changes to code duplicates can lead to unexpected behavior. Consequently, the identification of duplicated code, clone detection, has been a very active area of research in recent years. Up to now, however, no substantial investigation of the consequences of code cloning on program correctness has been carried out. To remedy this shortcoming, this paper presents the results of a large-scale case study that was undertaken to find out if inconsistent changes to cloned code can indicate faults. For the analyzed commercial and open source systems we not only found that inconsistent changes to clones are very frequent but also identified a significant number of faults induced by such changes. The clone detection tool used in the case study implements a novel algorithm for the detection of inconsistent clones. It is available as open source to enable other researchers to use it as basis for further investigations.

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Model-based development is becoming an increasingly common development methodology. In important domains like embedded systems already major parts of the code are generated from models specified with domain-specific modelling languages. Hence, such models are nowadays an integral part of the software development and maintenance process and therefore have a major economic and strategic value for the software-developing organisations. Nevertheless almost no work has been done on a quality defect that is known to seriously hamper maintenance productivity in classic codebased development: Cloning. This paper presents an approach for the automatic detection of clones in large models as they are used in model-based development of control systems. The approach is based on graph theory and hence can be applied to most graphical data-flow languages. An industrial case study demonstrates the applicability of our approach for the detection of clones in Matlab/Simulink models that are widely used in model-based development of embedded systems in the automotive domain.

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Cloned code is considered harmful for two reasons: (1) multiple, possibly unnecessary, duplicates of code increase maintenance costs and, (2) inconsistent changes to cloned code can create faults and, hence, lead to incorrect program behavior. Likewise, duplicated parts of models are problematic in model-based development. Recently, we and other authors proposed multiple approaches to automatically identify duplicates in graphical models. While it has been demonstrated that these approaches work in principal, a number of challenges remain for application in industrial practice. Based on an industrial case study undertaken with the BMW Group, this paper details on these challenges and presents solutions to the most pressing ones, namely scalability and relevance of the results. Moreover, we present tool support that eases the evaluation of detection results and thereby helps to make clone detection a standard technique in modelbased quality assurance.

Due to their pivotal role in software engineering, considerable effort is spent on the quality assurance of software requirements specifications. As they are mainly described in natural language, relatively few means of automated quality assessment exist. However, we found that clone detection, a technique widely applied to source code, is promising to assess one important quality aspect in an automated way, namely redundancy that stems from copy&paste operations. This paper describes a large-scale case study that applied clone detection to 28 requirements specifications with a total of 8,667 pages. We report on the amount of redundancy found in real-world specifications, discuss its nature as well as its consequences and evaluate in how far existing code clone detection approaches can be applied to assess the quality of requirements specifications in practice.

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