A Systematic Review of Tracing Solutions in Software Product Lines

Software Product Lines are large-scale, multi-unit systems that enable massive, customized production. They consist of a base of reusable artifacts and points of variation that provide the system with flexibility, allowing generating customized products. However, maintaining a system with such complexity and flexibility could be error prone and time consuming. Indeed, any modification (addition, deletion or update) at the level of a product or an artifact would impact other elements. It would therefore be interesting to adopt an efficient and organized traceability solution to maintain the Software Product Line. Still, traceability is not systematically implemented. It is usually set up for specific constraints (e.g. certification requirements), but abandoned in other situations. In order to draw a picture of the actual conditions of traceability solutions in Software Product Lines context, we decided to address a literature review. This review as well as its findings is detailed in the present article.Comment: 22 pages, 9 figures, 7 table

Automatic allocation of safety requirements to components of a software product line

Safety critical systems developed as part of a product line must still comply with safety standards. Standards use the concept of Safety Integrity Levels (SILs) to drive the assignment of system safety requirements to components of a system under design. However, for a Software Product Line (SPL), the safety requirements that need to be allocated to a component may vary in different products. Variation in design can indeed change the possible hazards incurred in each product, their causes, and can alter the safety requirements placed on individual components in different SPL products. Establishing common SILs for components of a large scale SPL by considering all possible usage scenarios, is desirable for economies of scale, but it also poses challenges to the safety engineering process. In this paper, we propose a method for automatic allocation of SILs to components of a product line. The approach is applied to a Hybrid Braking System SPL design

Functional foods : a conceptual model for assessing their safety and effectiveness

This report shows that the product-diet dilemma can be solved by developing a predictive model. The model integrates food intake data, dynamic consumption patterns and the production chain model and combines them with a risk-benefit approach

On the structure of problem variability: From feature diagrams to problem frames

Requirements for product families are expressed in terms of commonality and variability. This distinction allows early identification of an appropriate software architecture and opportunities for software reuse. Feature diagrams provide intuitive notations and techniques for representing requirements in product line development. In this paper, we observe that feature diagrams tend to obfuscate three important descriptions: requirements, domain properties and specifications. As a result, feature diagrams do not adequately capture the problem structures that underlie variability, and inform the solution structures of their complexity. With its emphasis on separation of the three descriptions, the problem frames approach provides a conceptual framework for a more detailed analysis of variability and its structure. With illustrations from an example, we demonstrate how problem frames analysis of variability can augment feature diagrams

UML-F: A Modeling Language for Object-Oriented Frameworks

The paper presents the essential features of a new member of the UML language family that supports working with object-oriented frameworks. This UML extension, called UML-F, allows the explicit representation of framework variation points. The paper discusses some of the relevant aspects of UML-F, which is based on standard UML extension mechanisms. A case study shows how it can be used to assist framework development. A discussion of additional tools for automating framework implementation and instantiation rounds out the paper.Comment: 22 pages, 10 figure

Synthesis of Attributed Feature Models From Product Descriptions: Foundations

Feature modeling is a widely used formalism to characterize a set of products (also called configurations). As a manual elaboration is a long and arduous task, numerous techniques have been proposed to reverse engineer feature models from various kinds of artefacts. But none of them synthesize feature attributes (or constraints over attributes) despite the practical relevance of attributes for documenting the different values across a range of products. In this report, we develop an algorithm for synthesizing attributed feature models given a set of product descriptions. We present sound, complete, and parametrizable techniques for computing all possible hierarchies, feature groups, placements of feature attributes, domain values, and constraints. We perform a complexity analysis w.r.t. number of features, attributes, configurations, and domain size. We also evaluate the scalability of our synthesis procedure using randomized configuration matrices. This report is a first step that aims to describe the foundations for synthesizing attributed feature models

CopulaDTA: An R Package for Copula Based Bivariate Beta-Binomial Models for Diagnostic Test Accuracy Studies in a Bayesian Framework

The current statistical procedures implemented in statistical software packages for pooling of diagnostic test accuracy data include hSROC regression and the bivariate random-effects meta-analysis model (BRMA). However, these models do not report the overall mean but rather the mean for a central study with random-effect equal to zero and have difficulties estimating the correlation between sensitivity and specificity when the number of studies in the meta-analysis is small and/or when the between-study variance is relatively large. This tutorial on advanced statistical methods for meta-analysis of diagnostic accuracy studies discusses and demonstrates Bayesian modeling using CopulaDTA package in R to fit different models to obtain the meta-analytic parameter estimates. The focus is on the joint modelling of sensitivity and specificity using copula based bivariate beta distribution. Essentially, we extend the work of Nikoloulopoulos by: i) presenting the Bayesian approach which offers flexibility and ability to perform complex statistical modelling even with small data sets and ii) including covariate information, and iii) providing an easy to use code. The statistical methods are illustrated by re-analysing data of two published meta-analyses. Modelling sensitivity and specificity using the bivariate beta distribution provides marginal as well as study-specific parameter estimates as opposed to using bivariate normal distribution (e.g., in BRMA) which only yields study-specific parameter estimates. Moreover, copula based models offer greater flexibility in modelling different correlation structures in contrast to the normal distribution which allows for only one correlation structure.Comment: 26 pages, 5 figure