Office: HB 08.080
Phone: +31 6 23157647
E-mail: N.J.R.Dintzner (atDOMAIN) tudelft (dot) nl
Delft University of Technology
Department of Software Technology
Software Engineering Research Group
2628 CD Delft, The Netherlands
FEVER: Extracting Feature-oriented Changes from CommitsWe devised an approach we call FEVER: the Feature Evolution Extrator; an approach to automatically extract feature-oriented change information from Git commits. We have compiled all the information related to this study in a Git repository that you can find here. You'll find the source code of the tool and dataset related to our evaluation and findings on the evolution of the Linux kernel. Feel free to browse around! The source code is available with installation instruction. Due to size restriction, the dataset is in Dropbox rather than Git. Sorry. You can get here. To access the data, all you have to do is install a Neo4j server download, very easy and fast, and then replace the graph.db folder with the content of the zip files provided here. And you are all set!
Feature-oriented Software EvolutionOur doctoral symposium work on co-evolution patterns got accepted at ICSE 2015. The camera ready version is available here (preprint).
Feature model evolutionAs part of my research for the Allegio Project, I study the evolution of large scale product line. In this context, I developed a tool FMDiff to automatically extract feature model changes from the Linux kernel. The paper describing this work can be found here: Extracting feature model changes from the Linux kernel using FMDiff. This work was accepted and presented at the VaMoS 2014 workshop. The publication can be viewed here and the associated presentation is available here You can access the tool and the dataset we used for our study in our git repository
Change Impact analysis on Multi product lineWe developed an approach to evaluate the impact of feature changes on the existing configurations of a multi-product lines. The objective is to assist our industrial partner in assessing the effect of a component change on the external interfaces on a large variable system, modeled using feature modeling techniques. This work resulted in a paper that will be presented at 14th International Conference on Software Reuse. You can access the tool and dataset we used for its evaluation in this git repository: MPL Change Impact