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.

Abstract-Published software quality models either provide abstract quality attributes or concrete quality assessments. There are no models that seamlessly integrate both aspects. In the project Quamoco, we built a comprehensive approach with the aim to close this gap.For this, we developed in several iterations a meta quality model specifying general concepts, a quality base model covering the most important quality factors and a quality assessment approach. The meta model introduces the new concept of a product factor, which bridges the gap between concrete measurements and abstract quality aspects. Product factors have measures and instruments to operationalise quality by measurements from manual inspection and tool analysis. The base model uses the ISO 25010 quality attributes, which we refine by 200 factors and 600 measures for Java and C# systems.We found in several empirical validations that the assessment results fit to the expectations of experts for the corresponding systems. The empirical analyses also showed that several of the correlations are statistically significant and that the maintainability part of the base model has the highest correlation, which fits to the fact that this part is the most comprehensive. Although we still see room for extending and improving the base model, it shows a high correspondence with expert opinions and hence is able to form the basis for repeatable and understandable quality assessments in practice.

Model-based testing relies on behavior models for the generation of model traces: input and expected output-test cases-for an implementation. We use the case study of an automotive network controller to assess different test suites in terms of error detection, model coverage, and implementation coverage. Some of these suites were generated automatically with and without models, purely at random, and with dedicated functional test selection criteria. Other suites were derived manually, with and without the model at hand. Both automatically and manually derived model-based test suites detected significantly more requirements errors than handcrafted test suites that were directly derived from the requirements. The number of detected programming errors did not depend on the use of models. Automatically generated model-based test suites detected as many errors as hand-crafted model-based suites with the same number of tests. A sixfold increase in the number of modelbased tests led to an 11% increase in detected errors.

Context: Bad requirements quality can cause expensive consequences during the software development lifecycle, especially if iterations are long and feedback comes late. Objectives: We aim at a light-weight static requirements analysis approach that allows for rapid checks immediately when requirements are written down. Method: We transfer the concept of code smells to Requirements Engineering as Requirements Smells. To evaluate the benefits and limitations, we define Requirements Smells, realize our concepts for a smell detection in a prototype called Smella and apply Smella in a series of cases provided by three industrial and a university context. Results: The automatic detection yields an average precision of 59% at an average recall of 82% with high variation. The evaluation in practical environments indicates benefits such as an increase of the awareness of quality defects. Yet, some smells were not clearly distinguishable. Conclusion: Lightweight smell detection can uncover many practically relevant requirements defects in a reasonably precise way. Although some smells need to be defined more clearly, smell detection provides a helpful means to support quality assurance in Requirements Engineering, for instance, as a supplement to reviews.

Context: For many years, we have observed industry struggling in defining a high quality requirements engineering (RE) and researchers trying to understand industrial expectations and problems. Although we are investigating the discipline with a plethora of empirical studies, those studies either concentrate on validating specific methods or on single companies or countries. Therefore, they allow only for limited empirical generalisations. Objective: To lay an empirical and generalisable foundation about the state of the practice in RE, we aim at a series of open and reproducible surveys that allow us to steer future research in a problem-driven manner. Method: We designed a globally distributed family of surveys in joint collaborations with different researchers from different countries. The instrument is based on an initial theory inferred from available studies. As a long-term goal, the survey will be regularly replicated to manifest a clear understanding on the status quo and practical needs in RE. In this paper, we present the design of the family of surveys and first results of its start in Germany. Results: Our first results contain responses from 30 German companies. The results are not yet generalisable, but already indicate several trends and problems. For instance, a commonly stated problem respondents see in their company standards are artefacts being underrepresented, and important problems they experience in their projects are incomplete and inconsistent requirements. Conclusion: The results suggest that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice.

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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.