Automated generation of computationally hard feature models using evolutionary algorithms

This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2014 Elsevier B.V.A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size.European Commission (FEDER), the Spanish Government and the Andalusian Government

Analyzing the Importance of Learnability and Understandability Quality Attributes in Reference to SPL Feature Models

The modeling foundation of SPL is promoting software reuse by segregation of variant features of all the products whi ch belong to a family. The analysis of various quality attribute s is very important in reference to SPL feature models. Identifying whether a feature model is easy to use and learn will help develop a successful product line. Two important quality sub factors of usability i.e. understandability and communicativeness play a great role in development of successful product line feature model. If the understanding of any feature model is low, it will result in lesser use of that feature model. Same applies to communicativeness , the more the communicativeness of a feature model, the more the usability. In other words, the successful reuse of any feature model will depend on the degree of its understa nding and communicativeness. C urrent analysis methods usually focus only on functional requirements of the product lines and do not focus on product quality. Whereas, non functional requirement s like maintainability, dependability and usability etc are essential dimensions of variability. This paper is intended to study the role of understandability and communicativeness over feature models. It also throws light on t he effect of these quality su b factors on SPL feature models and suggests ways to improve their degr

Improving Speech Recognition Quality Using Grammar Training Phrases

When a new voice feature is to be launched on a device with a voice interface, e.g., a digital assistant application, the natural language understanding (NLU) model is built using training data for the new feature. Speech biasing models are typically added to improve recognition accuracy for queries that are specific to the feature or contain non-common words. Such biasing models are often built using traffic logs, collected with user permission, after the initial release of the feature. However, this approach may not provide high speech recognition quality during product testing and initial launch. This disclosure describes techniques to improve the ASR quality of a new feature from the time of initial release and without relying on traffic logs. To that end, speech biasing models are built using grammar training phrases