Logo des Repositoriums

Auflistung nach Autor:in "Proksch, Sebastian"

1 - 2 von 2
  • Konferenzbeitrag
    An empirical study on program comprehension with reactive programming
    (Software Engineering 2016, 2016) Salvaneschi, Guido; Amann, Sven; Proksch, Sebastian; Mezini, Mira
    Starting from the first investigations with strictly functional languages, reactive programming has been proposed as the programming paradigm for reactive applications. The advantages of designs based on this style over designs based on the Observer design pattern have been studied for a long time. Over the years, researchers have enriched reactive languages with more powerful abstractions, embedded these abstractions into mainstream languages - including object-oriented languages - and applied reactive programming to several domains, like GUIs, animations, Web applications, robotics, and sensor networks. However, an important assumption behind this line of research - that, beside other advantages, reactive programming makes a wide class of otherwise cumbersome applications more comprehensible - has never been evaluated. In this paper, we present the design and the results of the first empirical study that evaluates the effect of reactive programming on comprehensibility compared to the traditional object-oriented style with the Observer design pattern. Results confirm the conjecture that comprehensibility is enhanced by reactive programming. In the experiment, the reactive programming group significantly outperforms the other group.
  • Konferenzbeitrag
    Intelligent code completion with Bayesian networks
    (Software Engineering 2016, 2016) Proksch, Sebastian; Lerch, Johannes; Mezini, Mira
    Code completion is an integral part of modern Integrated Development Environments (IDEs). Intelligent code completion systems can reduce long lists of type-correct proposals to relevant items. In this work, we replace an existing code completion engine named Best-Matching Neighbor (BMN) by an approach using Bayesian Networks named Pattern-based Bayesian Network (PBN).We use additional context information for more precise recommendations and apply clustering techniques to improve model sizes and to increase speed. We compare the new approach with the existing algorithm and, in addition to prediction quality, we also evaluate model size and inference speed. Our results show that the additional context information we collect improves prediction quality, and that PBN can obtain comparable prediction quality to BMN, while model size and inference speed scale better with large input sizes.