Attribute-based variability in feature models

Extended feature models enable the expression of complex cross-tree constraints involving feature attributes. The inclusion of attributes in cross-tree relations not only enriches the constraints, but also engenders an extended type of variability that involves attributes. In this article, we elaborate on the effects of this new variability type on feature models. We start by analyzing the nature of the variability involving attributes and extend the definitions of the configuration and the product to suit the emerging requirements. Next, we propose classifications for the features, configurations, and products to identify and formalize the ramifications that arise due to the new type of variability. Then, we provide a semantic foundation grounded on constraint satisfaction for our proposal. We introduce an ordering relation between configurations and show that the set of all the configurations represented by a feature model forms a semilattice. This is followed by a demonstration of how the feature model analyses will be affected using illustrative examples selected from existing and novel analysis operations. Finally, we summarize our experiences, gained from a commercial research and development project that employs an extended feature model

Mapping Extended Feature Models to Constraint Logic Programming over Finite Domains

As feature models for realistic product families may be quite complicated, automated analysis of feature models is desirable. Although several approaches reported in the literature addressed this issue, complex feature-attribute and attribute-attribute relationships in extended feature models were not handled effectively. In this article, we introduce a mapping from extended feature models to constraint logic programming over finite domains. This mapping is used to translate basic, cardinality-based, and extended feature models, which may include complex feature-feature, feature-attribute and attribute-attribute cross-tree relationships, into constraint logic programs. It thus enables use of off-the-shelf constraint solvers for the automated analysis of extended feature models involving such complex relationships. We also briefly discuss the ramifications of including feature-attribute relationships in operations of analysis. We believe that this proposal will be effective for further leveraging of constraint logic programming for automated analysis of feature models

Global Constraints on Feature Models

Feature modeling has been found very effective for modeling and managing variability in Software Product Lines. The nature of feature models invites, sometimes even requires, the use of global constraints. This paper lays the groundwork for the inclusion of global constraints in automated reasoning on feature models. We present a mapping from extended feature models to constraint logic programming over finite domains, and show that this mapping enables using global constraints on feature attributes, as well as features, for a variety of analysis operations on feature models. We also present performance test results and discuss the benefits of using global constraints

From extended feature models to constraint logic programming

Since feature models for realistic product families may be quite complicated, the automated analysis of feature models is desirable. Although several approaches reported in the literature address this issue, complex cross-tree relationships involving attributes in extended feature models have not been handled. In this article, we introduce a mapping from extended feature models to constraint logic programming over finite domains. This mapping is used to translate into constraint logic programs; basic, cardinality-based and extended feature models, which can include complex cross-tree relationships involving attributes. This translation enables the use of off-the-shelf constraint solvers for the automated analysis of extended feature models involving such complex relationships. We also present the performance results of some well-known analysis operations on an example translated model

FMML: A Feature Model Markup Language

Feature modeling is a common way of representing commonality and variability in Software Product Line Engineering. Alternative notations are available to represent feature models. Compared with graphical notations, text-based notations can be more amenable to automated processing and tool interoperability. In this paper, we propose an XML-based feature modeling language to represent extended feature models with complex relationships

Risk factors for febrile urinary tract infections in the first year after pediatric renal transplantation

Urinary tract infection is the most common infectious complication following kidney transplant. Anatomic abnormalities, bladder dysfunction, a positive history of febrile urinary tract infection, and recipient age are reported risk factors. The aim of this study was to determine the risk factors for fUTI, which necessitated hospitalization in the first year after renal transplantation in our pediatric transplant population. A retrospective review of 195 pediatric patients who underwent kidney transplant between 2008 and 2017 from a single institution was performed. All patients admitted to the hospital with fUTI were marked for further analyses. The risk factors including age, gender, dialysis type, history of urologic disorders, and preoperative proteinuria for fUTI in the first year after kidney transplantation and graft survivals were investigated. Independent-sample t test and chi-square tests were used for univariate analysis. Exhaustive CHAID algorithm was used for multivariate analysis. The data of 115 male and 80 female patients were retracted. The mean ages of our cohort for males and females were 9.5 +/- 5.1 and 10 +/- 4.8 years, respectively. The age of the patients at transplant and their gender were found to be a statistically significant risk factors for developing fUTIs. Multivariate analysis showed that fUTI was common in female patients and a subgroup of male patients who had preoperative proteinuria, but no neurogenic bladder had higher risk compared with male patients without proteinuria. Patient surveillance and antibiotic prophylaxis algorithms can be developed to prevent febrile urinary tract infections seen after pediatric kidney transplantation in risky population

Risk factors for febrile urinary tract infections in the first year after pediatric renal transplantation

Urinary tract infection is the most common infectious complication following kidney transplant. Anatomic abnormalities, bladder dysfunction, a positive history of febrile urinary tract infection, and recipient age are reported risk factors. The aim of this study was to determine the risk factors for fUTI, which necessitated hospitalization in the first year after renal transplantation in our pediatric transplant population. A retrospective review of 195 pediatric patients who underwent kidney transplant between 2008 and 2017 from a single institution was performed. All patients admitted to the hospital with fUTI were marked for further analyses. The risk factors including age, gender, dialysis type, history of urologic disorders, and preoperative proteinuria for fUTI in the first year after kidney transplantation and graft survivals were investigated. Independent-sample t test and chi-square tests were used for univariate analysis. Exhaustive CHAID algorithm was used for multivariate analysis. The data of 115 male and 80 female patients were retracted. The mean ages of our cohort for males and females were 9.5 +/- 5.1 and 10 +/- 4.8 years, respectively. The age of the patients at transplant and their gender were found to be a statistically significant risk factors for developing fUTIs. Multivariate analysis showed that fUTI was common in female patients and a subgroup of male patients who had preoperative proteinuria, but no neurogenic bladder had higher risk compared with male patients without proteinuria. Patient surveillance and antibiotic prophylaxis algorithms can be developed to prevent febrile urinary tract infections seen after pediatric kidney transplantation in risky population