Feature-Aware Verification

A software product line is a set of software products that are distinguished in terms of features (i.e., end-user--visible units of behavior). Feature interactions ---situations in which the combination of features leads to emergent and possibly critical behavior--- are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLverifier for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only feature-local knowledge, and that variability encoding significantly improves the verification performance when proving the absence of interactions.Comment: 12 pages, 9 figures, 1 tabl

Probabilistic Model Checking for Energy Analysis in Software Product Lines

In a software product line (SPL), a collection of software products is defined by their commonalities in terms of features rather than explicitly specifying all products one-by-one. Several verification techniques were adapted to establish temporal properties of SPLs. Symbolic and family-based model checking have been proven to be successful for tackling the combinatorial blow-up arising when reasoning about several feature combinations. However, most formal verification approaches for SPLs presented in the literature focus on the static SPLs, where the features of a product are fixed and cannot be changed during runtime. This is in contrast to dynamic SPLs, allowing to adapt feature combinations of a product dynamically after deployment. The main contribution of the paper is a compositional modeling framework for dynamic SPLs, which supports probabilistic and nondeterministic choices and allows for quantitative analysis. We specify the feature changes during runtime within an automata-based coordination component, enabling to reason over strategies how to trigger dynamic feature changes for optimizing various quantitative objectives, e.g., energy or monetary costs and reliability. For our framework there is a natural and conceptually simple translation into the input language of the prominent probabilistic model checker PRISM. This facilitates the application of PRISM's powerful symbolic engine to the operational behavior of dynamic SPLs and their family-based analysis against various quantitative queries. We demonstrate feasibility of our approach by a case study issuing an energy-aware bonding network device.Comment: 14 pages, 11 figure

Underground Muon Physics with the MACRO experiment

Underground muon events detected by the MACRO experiment at Gran Sasso have been studied for different purposes. The studies include the vertical muon intensity measurement, multiplicity distribution, lateral and angular muon distribution and searches for substructures inside muon bundles. These analyses have contributed to bring new insights in cosmic ray physics, in particular in the framework of primary cosmic ray composition studies. Moreover, this activity allows the testing and tuning of Monte Carlo simulations, in particular for aspects associated with models of hadronic interactions and muon propagation through the rock.Comment: 6 pages, 4 EPS figures included with epsfig, uses espcrc2.sty Talk given at the Sixth Topical Seminar on Neutrino and Astroparticle Physics, San Miniato, Italy, 17-21 May 199

Emergent simplicity in microbial community assembly

Published in final edited form as: Science. 2018 August 03; 361(6401): 469–474. doi:10.1126/science.aat1168.A major unresolved question in microbiome research is whether the complex taxonomic architectures observed in surveys of natural communities can be explained and predicted by fundamental, quantitative principles. Bridging theory and experiment is hampered by the multiplicity of ecological processes that simultaneously affect community assembly in natural ecosystems. We addressed this challenge by monitoring the assembly of hundreds of soil- and plant-derived microbiomes in well-controlled minimal synthetic media. Both the community-level function and the coarse-grained taxonomy of the resulting communities are highly predictable and governed by nutrient availability, despite substantial species variability. By generalizing classical ecological models to include widespread nonspecific cross-feeding, we show that these features are all emergent properties of the assembly of large microbial communities, explaining their ubiquity in natural microbiomes.The funding for this work partly results from a Scialog Program sponsored jointly by the Research Corporation, for Science Advancement and. the Gordon and Betty Moore Foundation through grants to Yale University and Boston University by the Research Corporation and by the Simons Foundation. This work was also supported by a young; investigator award from the Human Frontier Science Program to A.S. (RGY0077/2016) and by NIH NIGMS grant 1R35GM119461 and a Simons Investigator as in the Mathematical Modeling of Living Systems (MMLS) to P.M.; D.S. and J.E.G. additionally acknowledge funding from the Defense Advanced Research Projects Agency (purchase request no. HR0011515303, contract no.. HR0011-15-0-0091), the U.S. Department of Energy (DE-SC0012627), the NIH (T32GM100842, 5R01DE024468, R01GM121950, and Sub_P30DK036836_P&F), the National Science Foundation (1457695), the Human Frontier Science Program (RGP0020/2016) and the Boston University Interdisciplinary Biomedical Research Office. (Research Corporation, for Science Advancement; Gordon and Betty Moore Foundation; Boston University by the Research Corporation; Simons Foundation.; RGY0077/2016 - uman Frontier Science Program; 1R35GM119461 - NIH NIGMS grant; Simons Investigator as in the Mathematical Modeling of Living Systems (MMLS); HR0011515303 - Defense Advanced Research Projects Agency; HR0011-15-0-0091 - Defense Advanced Research Projects Agency; T32GM100842 - NIH; 5R01DE024468 - NIH; R01GM121950 - NIH; ub_P30DK036836 - NIH; 1457695 - National Science Foundation; RGP0020/2016 - Human Frontier Science Program; Boston University Interdisciplinary Biomedical Research Office)Accepted manuscrip

A Feature-Oriented Modelling Language and a Feature-Interaction Taxonomy for Product-Line Requirements

Many organizations specialize in the development of families of software systems, called software product lines (SPLs), for one or more domains (e.g., automotive, telephony, health care). SPLs are commonly developed as a shared set of assets representing the common and variable aspects of an SPL, and individual products are constructed by assembling the right combinations of assets. The feature-oriented software development (FOSD) paradigm advocates the use of system features as the primary unit of commonality and variability among the products of an SPL. A feature represents a coherent and identifiable bundle of system functionality, such as call waiting in telephony and cruise control in an automobile. Furthermore, FOSD aims at feature-oriented artifacts (FOAs); that is, software-development artifacts that explicate features, so that a clear mapping is established between a feature and its representation in different artifacts. The thesis first identifies the problem of developing a suitable language for expressing feature-oriented models of the functional requirements of an SPL, and then presents the feature-oriented requirements modelling language (FORML) as a solution to this problem. FORML's notation is based on standard software-engineering notations (e.g., UML class and state-machine models, feature models) to ease adoption by practitioners, and has a precise syntax and semantics to enable analysis. The novelty of FORML is in adding feature-orientation to state-of-the-art requirements modelling approaches (e.g., KAOS), and in the systematic treatment of modelling evolutions of an SPL via enhancements to existing features. An existing feature can be enhanced by extending or modifying its requirements. Enhancements that modify a feature's requirements are called intended feature interactions. For example, the call waiting feature in telephony intentionally overrides the basic call service feature's treatment of incoming calls when the subscriber is already involved in a call. FORML prescribes different constructs for specifying different types of enhancements in state-machine models of requirements. Furthermore, unlike some prominent approaches (e.g., AHEAD, DFC), FORML's constructs for modelling intended feature interactions do not depend on the order in which features are composed; this can lead to savings in analysis costs, since only one rather than (possibly) multiple composition orders need to be analyzed. A well-known challenge in FOSD is managing feature interactions, which, informally defined, are ways in which different features can influence one another in defining the overall properties and behaviours of their combination. Some feature interactions are intended, as described above, while other feature interactions are unintended: for example, the cruise control and anti-lock braking system features of an automobile may have incompatible affects on the automobile's acceleration, which would make their combination inconsistent. Unintended feature interactions should be detected and resolved. To detect unintended interactions in models of feature behaviour, we must first define a taxonomy of feature interactions for the modelling language: that is, we must understand the different ways that feature interactions can manifest among features expressed in the language. The thesis presents a taxonomy of feature interactions for FORML that is an adaptation of existing taxonomies for operational models of feature behaviour. The novelty of the proposed taxonomy is that it presents a definition of behaviour modification that generalizes special cases found in the literature; and it enables feature-interaction analyses that report only unintended interactions, by excluding interactions caused by FORML's constructs for modelling intended feature interactions

Associations among Wine Grape Microbiome, Metabolome, and Fermentation Behavior Suggest Microbial Contribution to Regional Wine Characteristics.

UnlabelledRegionally distinct wine characteristics (terroir) are an important aspect of wine production and consumer appreciation. Microbial activity is an integral part of wine production, and grape and wine microbiota present regionally defined patterns associated with vineyard and climatic conditions, but the degree to which these microbial patterns associate with the chemical composition of wine is unclear. Through a longitudinal survey of over 200 commercial wine fermentations, we demonstrate that both grape microbiota and wine metabolite profiles distinguish viticultural area designations and individual vineyards within Napa and Sonoma Counties, California. Associations among wine microbiota and fermentation characteristics suggest new links between microbiota, fermentation performance, and wine properties. The bacterial and fungal consortia of wine fermentations, composed from vineyard and winery sources, correlate with the chemical composition of the finished wines and predict metabolite abundances in finished wines using machine learning models. The use of postharvest microbiota as an early predictor of wine chemical composition is unprecedented and potentially poses a new paradigm for quality control of agricultural products. These findings add further evidence that microbial activity is associated with wine terroirImportanceWine production is a multi-billion-dollar global industry for which microbial control and wine chemical composition are crucial aspects of quality. Terroir is an important feature of consumer appreciation and wine culture, but the many factors that contribute to terroir are nebulous. We show that grape and wine microbiota exhibit regional patterns that correlate with wine chemical composition, suggesting that the grape microbiome may influence terroir In addition to enriching our understanding of how growing region and wine properties interact, this may provide further economic incentive for agricultural and enological practices that maintain regional microbial biodiversity

Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder

Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme that tightly integrates with deep neural networks and has the ability to capture correlation structures among exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional multivariate distribution as a generating process. As a result, the variational lower bound of the joint likelihood can be optimized via a conditional variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was motivated by two real-world applications in computational sustainability: one studies the spatial correlation among multiple bird species using the eBird data and the other models multi-dimensional landscape composition and human footprint in the Amazon rainforest with satellite images. We show that MEDL_CVAE captures rich dependency structures, scales better than previous methods, and further improves on the joint likelihood taking advantage of very large datasets that are beyond the capacity of previous methods.Comment: The first two authors contribute equall