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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Domain specific languages allow users to directly express domain concepts in their programs and thereby eliminate the accidental complexity resulting from implementation details irrelevant to the domain. Cleaner programs, written in DSLs are much easier to analyze formally. However, domain specific analyses need to be implemented over and over again for each new domain specific language. In this paper we show that the use of language engineering techniques for modularizing languages can drastically improve on this situation. Language fragments (aka. language modules) together with a set of analyses defined for them can be reused between different DSLs, making the implementation of analyses significantly easier. This paper presents our approach for using the Meta-Programming System to implement domain specific languages and analyses both as extensions of C and in the domain of intelligent buildings. The main lesson learned is that modularization at language and analysis level allows rapid instantiation of advanced DSLs and their corresponding analyses.

Abstract. Developers who use C model checkers have to overcome three usability challenges: First, it is difficult to express application level properties as C-level verification conditions, due to the abstraction gap. Second, without advanced IDE support, it is difficult to interpret the counterexamples produced by the model checker and understand what went wrong in terms of application level properties. Third, most C model checkers support only a subset of C and it is easy for developers to inadvertently use C constructs outside this subset. In this paper we report on our preliminary experience with using the MPS language workbench to integrate the CBMC model checker with a set of domain-specific extensions of C for developing embedded software. Higher level language constructs such as components and decision tables makes it easier for end users to bridge the abstraction gap, to write verification conditions and to interpret the analysis results. Furthermore, the use of language workbenches allows the definition of analyzable language subsets, making the implementation of analyses simpler and their use more predictable.