Supporting Feature Model Evolution by Lifting Code-Level Dependencies: A Research Preview

2019 ◽  
pp. 169-175
Author(s):  
Daniel Hinterreiter ◽  
Kevin Feichtinger ◽  
Lukas Linsbauer ◽  
Herbert Prähofer ◽  
Paul Grünbacher
Keyword(s):  
Feature Model ◽  
Model Evolution

scholarly journals Guiding feature model evolution by lifting code-level dependencies

2021 ◽  
pp. 101034
Author(s):  
Kevin Feichtinger ◽  
Daniel Hinterreiter ◽  
Lukas Linsbauer ◽  
Herbert Prähofer ◽  
Paul Grünbacher
Keyword(s):  
Feature Model ◽  
Model Evolution

Anomaly analyses for feature-model evolution

2018 ◽  
Author(s):  
Michael Nieke ◽  
Jacopo Mauro ◽  
Christoph Seidl ◽  
Thomas Thüm ◽  
Ingrid Chieh Yu ◽  
...  
Keyword(s):  
Feature Model ◽  
Model Evolution

Anomaly analyses for feature-model evolution

2020 ◽  
Vol 53 (9) ◽  
pp. 188-201
Author(s):  
Michael Nieke ◽  
Jacopo Mauro ◽  
Christoph Seidl ◽  
Thomas Thüm ◽  
Ingrid Chieh Yu ◽  
...  
Keyword(s):  
Feature Model ◽  
Model Evolution

Hybrid Model using Firefly and BBO for Feature Selection in Software Product Line

2020 ◽  
Vol 13 ◽  
Author(s):  
Hitesh Yadav ◽  
Rita Chhikara ◽  
Charan Kumari
Keyword(s):  
Hybrid Model ◽  
High Performance ◽  
Software Product Line ◽  
Product Line ◽  
Feature Model ◽  
Software Systems ◽  
Feature Sets ◽  
Research Article ◽  
Software Product ◽  
Multiple Variants

Background: Software Product Line is the group of multiple software systems which share the similar set of features with multiple variants. Feature model is used to capture and organize features used in different multiple organization. Objective: The objective of this research article is to obtain an optimized subset of features which are capable of providing high performance. Methods: In order to achieve the desired objective, two methods have been proposed. a) An improved objective function which is used to compute the contribution of each feature with weight based methodology. b) A hybrid model is employed to optimize the Software Product Line problem. Results: Feature sets varying in size from 100 to 1000 have been used to compute the performance of the Software Product Line. Conclusion: The results shows that proposed hybrid model outperforms the state of art metaheuristic algorithms.

Constraints

2012 ◽  
Vol 3 (2) ◽  
pp. 33-68 ◽  
Author(s):  
Raúl Mazo ◽  
Camille Salinesi ◽  
Daniel Diaz ◽  
Olfa Djebbi ◽  
Alberto Lora-Michiels
Keyword(s):  
Domain Engineering ◽  
Product Line ◽  
The Other ◽  
Feature Model ◽  
Product Configuration ◽  
Constraint Program ◽  
Engineering Strategy ◽  
Application Engineering ◽  
The One ◽  
Line Engineering

Drawing from an analogy between features based Product Line (PL) models and Constraint Programming (CP), this paper explores the use of CP in the Domain Engineering and Application Engineering activities that are put in motion in a Product Line Engineering strategy. Specifying a PL as a constraint program instead of a feature model carries out two important qualities of CP: expressiveness and direct automation. On the one hand, variables in CP can take values over boolean, integer, real or even complex domains and not only boolean values as in most PL languages such as the Feature-Oriented Domain Analysis (FODA). Specifying boolean, arithmetic, symbolic and reified constraint, provides a power of expression that spans beyond that provided by the boolean dependencies in FODA models. On the other hand, PL models expressed as constraint programs can directly be executed and analyzed by off-the-shelf solvers. This paper explores the issues of (a) how to specify a PL model using CP, including in the presence of multi-model representation, (b) how to verify PL specifications, (c) how to specify configuration requirements, and (d) how to support the product configuration activity. Tests performed on a benchmark of 50 PL models show that the approach is efficient and scales up easily to very large and complex PL specifications.

scholarly journals A metabolomic endotype of bioenergetic dysfunction predicts mortality in critically ill patients with acute respiratory failure

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Raymond J. Langley ◽  
Marie E. Migaud ◽  
Lori Flores ◽  
J. Will Thompson ◽  
Elizabeth A. Kean ◽  
...  
Keyword(s):  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Patient Outcomes ◽  
Medical Intensive Care Unit ◽  
Close Association ◽  
Treatment Options ◽  
Feature Model ◽  
High Morbidity ◽  
Medical Intensive Care ◽  
Poor Outcomes

AbstractAcute respiratory failure (ARF) requiring mechanical ventilation, a complicating factor in sepsis and other disorders, is associated with high morbidity and mortality. Despite its severity and prevalence, treatment options are limited. In light of accumulating evidence that mitochondrial abnormalities are common in ARF, here we applied broad spectrum quantitative and semiquantitative metabolomic analyses of serum from ARF patients to detect bioenergetic dysfunction and determine its association with survival. Plasma samples from surviving and non-surviving patients (N = 15/group) were taken at day 1 and day 3 after admission to the medical intensive care unit and, in survivors, at hospital discharge. Significant differences between survivors and non-survivors (ANOVA, 5% FDR) include bioenergetically relevant intermediates of redox cofactors nicotinamide adenine dinucleotide (NAD) and NAD phosphate (NADP), increased acyl-carnitines, bile acids, and decreased acyl-glycerophosphocholines. Many metabolites associated with poor outcomes are substrates of NAD(P)-dependent enzymatic processes, while alterations in NAD cofactors rely on bioavailability of dietary B-vitamins thiamine, riboflavin and pyridoxine. Changes in the efficiency of the nicotinamide-derived cofactors’ biosynthetic pathways also associate with alterations in glutathione-dependent drug metabolism characterized by substantial differences observed in the acetaminophen metabolome. Based on these findings, a four-feature model developed with semi-quantitative and quantitative metabolomic results predicted patient outcomes with high accuracy (AUROC = 0.91). Collectively, this metabolomic endotype points to a close association between mitochondrial and bioenergetic dysfunction and mortality in human ARF, thus pointing to new pharmacologic targets to reduce mortality in this condition.

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