Extracting Build Changes with BUILDDIFF

Build systems are an essential part of modern software engineering projects. As software projects change continuously, it is crucial to understand how the build system changes because neglecting its maintenance can lead to expensive build breakage. Recent studies have investigated the (co-)evolution of build configurations and reasons for build breakage, but they did this only on a coarse grained level. In this paper, we present BUILDDIFF, an approach to extract detailed build changes from MAVEN build files and classify them into 95 change types. In a manual evaluation of 400 build changing commits, we show that BUILDDIFF can extract and classify build changes with an average precision and recall of 0.96 and 0.98, respectively. We then present two studies using the build changes extracted from 30 open source Java projects to study the frequency and time of build changes. The results show that the top 10 most frequent change types account for 73% of the build changes. Among them, changes to version numbers and changes to dependencies of the projects occur most frequently. Furthermore, our results show that build changes occur frequently around releases. With these results, we provide the basis for further research, such as for analyzing the (co-)evolution of build files with other artifacts or improving effort estimation approaches. Furthermore, our detailed change information enables improvements of refactoring approaches for build configurations and improvements of models to identify error-prone build files.Comment: Accepted at the International Conference of Mining Software Repositories (MSR), 201

Domain Specific Languages for Managing Feature Models: Advances and Challenges

International audienceManaging multiple and complex feature models is a tedious and error-prone activity in software product line engineering. Despite many advances in formal methods and analysis techniques, the supporting tools and APIs are not easily usable together, nor unified. In this paper, we report on the development and evolution of the Familiar Domain-Specific Language (DSL). Its toolset is dedicated to the large scale management of feature models through a good support for separating concerns, composing feature models and scripting manipulations. We overview various applications of Familiar and discuss both advantages and identified drawbacks. We then devise salient challenges to improve such DSL support in the near future

Extracting Feature Model Changes from the Linux Kernel Using FMDiff

The Linux kernel feature model has been studied as an example of large scale evolving feature model and yet details of its evolution are not known. We present here a classification of feature changes occurring on the Linux kernel feature model, as well as a tool, FMDiff, designed to automatically extract those changes. With this tool, we obtained the history of more than twenty architecture specific feature models, over ten releases and compared the recovered information with Kconfig file changes. We establish that FMDiff provides a comprehensive view of feature changes and show that the collected data contains promising information regarding the Linux feature model evolution. Preprint accepted for publication in the Proceedings of the 8th International Workshop on Variability Modelling of Software intensive Systems, VaMoS 2014. Nice, France, January 22-24, 2014.Software TechnologyElectrical Engineering, Mathematics and Computer Scienc