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.

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.

Maintainability is a key quality attribute of successful software systems. However, its management in practice is still problematic. Currently, there is no comprehensive basis for assessing and improving the maintainability of software systems. Quality models have been proposed to solve this problem. Nevertheless, existing approaches do not explicitly take into account the maintenance activities, that largely determine the software maintenance effort. This paper proposes a 2-dimensional model of maintainability that explicitly associates system properties with the activities carried out during maintenance. The separation of activities and properties facilitates the identification of sound quality criteria and allows to reason about their interdependencies. This transforms the quality model into a structured and comprehensive quality knowledge base that is usable in industrial project environments. For example, review guidelines can be generated from it. The model is based on an explicit quality metamodel that supports its systematic construction and fosters preciseness as well as completeness. An industrial case study demonstrates the applicability of the model for the evaluation of the maintainability of Matlab Simulink models that are frequently used in modelbased development of embedded systems.

Software quality models are a well-accepted means to support quality management of software systems. Over the last 30 years, a multitude of quality models have been proposed and applied with varying degrees of success. Despite successes and standardisation efforts, quality models are still being criticised, as their application in practice exhibits various problems. To some extent, this criticism is caused by an unclear definition of what quality models are and which purposes they serve. Beyond this, there is a lack of explicitly stated requirements for quality models with respect to their intended mode of application. To remedy this, this paper describes purposes and usage scenarios of quality models and, based on the literature and experiences from the authors, collects critique of existing models. From this, general requirements for quality models are derived. The requirements can be used to support the evaluation of existing quality models for a given context or to guide further quality model development.

Approximately 70% of the source code of a software system consists of identifiers. Hence, the names chosen as identifiers are of paramount importance for the readability of computer programs and therewith their comprehensibility. However, virtually every programming language allows programmers to use almost arbitrary sequences of characters as identifiers which far too often results in more or less meaningless or even misleading naming. Coding style guides somehow address this problem but are usually limited to general and hard to enforce rules like "identifiers should be self-describing". This paper renders adequate identifier naming far more precisely. A formal model, based on bijective mappings between concepts and names, provides a solid foundation for the definition of precise rules for concise and consistent naming. The enforcement of these rules is supported by a tool that incrementally builds and maintains a complete identifier dictionary while the system is being developed. The identifier dictionary explains the language used in the software system, aids in consistent naming, and supports programmers by proposing suitable names depending on the current context.

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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.