A Deep Reinforcement Learning Approach to Configuration Sampling Problem

Configurable software systems have become increasingly popular as they enable customized software variants. The main challenge in dealing with configuration problems is that the number of possible configurations grows exponentially as the number of features increases. Therefore, algorithms for testing customized software have to deal with the...

Uniform Sampling of SAT Solutions for Configurable Systems: Are We There Yet?

Uniform or near-uniform generation of solutions for large satisfiability formulas is a problem of theoretical and practical interest for the testing community. Recent works proposed two algorithms (namely UniGen and QuickSampler) for reaching a good compromise between execution time and uniformity guarantees, with empirical evidence on SAT...

Analysing the Linux kernel feature model changes using FMDiff

Evolving a large scale, highly variable system is a challenging task. For such a system, evolution operations often require to update consistently both their implementation and its feature model. In this context, the evolution of the feature model closely follows the evolution of the system. The purpose of this work is to show that fine...

Analysing Feature Model Changes using FMDiff

Evolving a large scale, highly variable systems is a challenging task. For such a system, evolution operations often require to update consistently both their implementation and its feature model. In this context, the evolution of the feature model closely follows the evolution of the system. The purpose of this work is to show that fine-grained...

Analysing the Linux kernel feature model changes using FMDiff

Evolving a large scale, highly variable system is a challenging task. For such a system, evolution operations often require to update consistently both their implementation and its feature model. In this context, the evolution of the feature model closely follows the evolution of the system. The purpose of this work is to show that fine-grained...

Analysing Feature Model Changes using FMDiff

Evolving a large scale, highly variable sys- tems is a challenging task. For such a system, evolution operations often require to update consistently both their implementation and its feature model. In this con- text, the evolution of the feature model closely follows the evolution of the system. The purpose of this work is to show that fine...

Evaluating Feature Change Impact on Multi-Product Line Configurations Using Partial Information

Evolving large-scale, complex and highly variable systems is known to be a difficult task, where a single change can ripple through various parts of the system with potentially undesirable effects. In the case of product lines, and moreover multi-product lines, a change may affect only certain variants or certain combinations of features, making...

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...