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    Item type:Publication, Access status: without files ,
    Variability mining of state charts
    (Association for Computing Machinery (ACM), 2016-10)
    Wille, David
    ;
    Schulze, Sandro
    ;
    Schaefer, Ina
    Companies commonly use state charts to reduce the complexity of software development. To create variants with slightly different functionality from existing products, it is common practice to copy the corresponding state charts and modify them to changed requirements. Even though these so-called clone-and-own approaches save money in the short-term, they introduce severe risks for software evolution and product quality in the long term as the relation between the software variants is lost so that all products have to be maintained separately. In previous work, we introduced variability mining algorithms to identify the relations between related MATLAB/Simulink model variants regarding their common and varying parts. In this paper, we adapt these algorithms for state charts by applying guidelines from previous work to make them available for developers to better understand the relations between a set of state chart variants. Using this knowledge, maintenance of related variants can be improved and migration from clone-and-own based single variant development to more elaborate reuse strategies is possible to increase maintainability and the overall product quality. We demonstrate the feasibility of variability mining for state charts by means of a case study with models of realistic size.
    Publicationtype: Conference Paper
      152
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    Item type:Publication, Access status: without files ,
    Identifying variability in object-oriented code using model-based code mining
    (Springer, 2016)
    Wille, David
    ;
    Tiede, Michael
    ;
    Schulze, Sandro
    ;
    Seidl, Christoph
    ;
    Schaefer, Ina
    A large set of object-oriented programming (OOP) languages exists to realize software for different purposes. Companies often create variants of their existing software by copying and modifying them to changed requirements. While these so-called clone-and-own approaches allow to save money in short-term, they expose the company to severe risks regarding long-term evolution and product quality. The main reason is the high manual maintenance effort which is needed due to the unknown relations between variants. In this paper, we introduce a modelbased approach to identify variability information for OOP code, allowing companies to better understand and manage variability between their variants. This information allows to improve maintenance of the variants and to transition from single variant development to more elaborate reuse strategies such as software product lines. We demonstrate the applicability of our approach by means of a case study analyzing variants generated from an existing software product line and comparing our findings to the managed reuse strategy.
    Publicationtype: Conference Paper
      77