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On-board embedded software developed for spaceflight systems (space software) must adhere to stringent software quality assurance procedures. For example, verification and validation activities are typically performed and assessed by third party organizations. To further minimize the risk of human mistakes, space agencies, such as the European Space Agency (ESA), are looking for automated solutions for the assessment of software testing activities, which play a crucial role in this context. Over the years, mutation analysis has shown to be a promising solution for the automated assessment of test suites; it consists of measuring the quality of a test suite in terms of the percentage of injected faults leading to a test failure. A number of optimization techniques, addressing scalability and accuracy problems, have been proposed to facilitate the industrial adoption of mutation analysis. However, to date, two major problems prevent space agencies from enforcing mutation analysis in space software development. In this paper, we enhance mutation analysis optimization techniques to enable their applicability to embedded software and propose a pipeline that successfully integrates them to address scalability and accuracy issues in this context, as described above. Further, we report on the largest study involving embedded software systems in the mutation analysis literature. Our research is part of a research project funded by ESA ESTEC involving private companies (GomSpace Luxembourg and LuxSpace) in the space sector. These industry partners provided the case studies reported in this paper; they include an on-board software system managing a microsatellite currently on-orbit, a set of libraries used in deployed cubesats, and a mathematical library certified by ESA., Accepted for publication on IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

In real-time systems, priorities assigned to real-time tasks determine the order of task executions, by relying on an underlying task scheduling policy. Assigning optimal priority values to tasks is critical to allow the tasks to complete their executions while maximizing safety margins from their specified deadlines. This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines. In practice, priority assignments result from an interactive process between the development and testing teams. In this article, we propose an automated method that aims to identify the best possible priority assignments in real-time systems, accounting for multiple objectives regarding safety margins and engineering constraints. Our approach is based on a multi-objective, competitive coevolutionary algorithm mimicking the interactive priority assignment process between the development and testing teams. We evaluate our approach by applying it to six industrial systems from different domains and several synthetic systems. The results indicate that our approach significantly outperforms both our baselines, i.e., random search and sequential search, and solutions defined by practitioners. Our approach scales to complex industrial systems as an offline analysis method that attempts to find near-optimal solutions within acceptable time, i.e., less than 16 hours.

Estimating worst-case execution times (WCET) is an important activity at early design stages of real-time systems. Based on WCET estimates, engineers make design and implementation decisions to ensure that task execution always complete before their specified deadlines. However, in practice, engineers often cannot provide precise point WCET estimates and prefer to provide plausible WCET ranges. Given a set of real-time tasks with such ranges, we provide an automated technique to determine for what WCET values the system is likely to meet its deadlines, and hence operate safely with a probabilistic guarantee. Our approach combines a search algorithm for generating worst-case scheduling scenarios with polynomial logistic regression for inferring probabilistic safe WCET ranges. We evaluated our approach by applying it to three industrial systems from different domains and several synthetic systems. Our approach efficiently and accurately estimates probabilistic safe WCET ranges within which deadlines are likely to be satisfied with a high degree of confidence.

Patients with Parkinson’s disease (PD) exhibit differences in their gut microbiomes compared to healthy individuals. Although differences have most commonly been described in the abundances of bacterial taxa, changes to viral and archaeal populations have also been observed. Mechanistic links between gut microbes and PD pathogenesis remain elusive but could involve molecules that promote α-synuclein aggregation. Here, we show that 2-hydroxypyridine (2-HP) represents a key molecule for the pathogenesis of PD. We observe significantly elevated 2-HP levels in faecal samples from patients with PD or its prodrome, idiopathic REM sleep behaviour disorder (iRBD), compared to healthy controls. 2-HP is correlated with the archaeal species Methanobrevibacter smithii and with genes involved in methane metabolism, and it is detectable in isolate cultures of M. smithii. We demonstrate that 2-HP is selectively toxic to transgenic α-synuclein overexpressing yeast and increases α-synuclein aggregation in a yeast model as well as in human induced pluripotent stem cell derived enteric neurons. It also exacerbates PD-related motor symptoms, α-synuclein aggregation, and striatal degeneration when injected intrastriatally in transgenic mice overexpressing human α-synuclein. Our results highlight the effect of an archaeal molecule in relation to the gut-brain axis, which is critical for the diagnosis, prognosis, and treatment of PD.

Acceptance criteria (AC) are implementation agnostic conditions that a system must meet to be consistent with its requirements and be accepted by its stakeholders. Each acceptance criterion is typically expressed as a natural-language statement with a clear pass or fail outcome. Writing AC is a tedious and error-prone activity, especially when the requirements specifications evolve and there are different analysts and testing teams involved. Analysts and testers must iterate multiple times to ensure that AC are understandable and feasible, and accurately address the most important requirements and workflows of the system being developed. In many cases, analysts express requirements through models, along with natural language, typically in some variant of the UML. AC must then be derived by developers and testers from such models. In this paper, we bridge the gap between requirements models and AC by providing a UML-based modeling methodology and an automated solution to generate AC. We target AC in the form of Behavioral Specifications in the context of Behavioral-Driven Development (BDD), a widely used agile practice in many application domains. More specially we target the well-known Gherkin language to express AC, which then can be used to generate executable test cases. We evaluate our modeling methodology and AC generation solution through an industrial case study in the financial domain. Our results suggest that (1) our methodology is feasible to apply in practice, and (2) the additional modeling effort required by our methodology is outweighed by the benefits the methodology brings in terms of automated and systematic AC generation and improved model precision.

With rapid development of unmanned aerial vehicle (UAV) technology, application of the UAVs for task offloading has received increasing interest in the academia. However, real-time interaction between one UAV and the mobile edge computing (MEC) node is required for processing the tasks of mobile end users, which significantly increases the system overhead and is unable to meet the demands of large-scale artificial intelligence (AI) based applications. To tackle this problem, in this article, we propose a new architecture for UAV clustering to enable efficient multi-modal multi-task task offloading. By the proposed architecture, the computing, caching and communication resources are collaboratively optimized using AI based decision-making. This not only increases the efficiency of UAV clusters, but also provides insight into the fusion of computation and communication.

By photopolymerizing liquid crystal shells, their rich variety of self-assembled structures can be rendered permanent and the lifetime extended from days to months, without removing the characteristic responsiveness. If polymerization is carried out close to either boundary of the nematic phase, the process triggers the transition into the adjacent phase, to higher or to lower degree of order.

In ultra-dense heterogeneous networks, caching popular contents at small base stations is considered as an effective way to reduce latency and redundant data transmission. Optimization of caching placement/replacement and content delivering can be computationally heavy, especially for large-scale networks. The provision of both time-efficient and high-quality solutions is challenging. Conventional iterative optimization methods, either optimal or heuristic, typically require a large number of iterations to achieve satisfactory performance, and result in considerable computational delay. This may limit their applications in practical network operations where online decisions have to be made. In this paper, we provide a viable alternative to the conventional methods for caching optimization, from a deep learning perspective. The idea is to train the optimization algorithms through a deep neural network (DNN) in advance, instead of directly applying them in real-time caching or scheduling. This allows significant complexity reduction in the delay-sensitive operation phase since the computational burden is shifted to the DDN training phase. Numerical results demonstrate that the DNN is of high computational efficiency. By training the designed DNN over a massive number of instances, the solution quality of the energy-efficient content delivering can be progressively approximated to around 90% of the optimum.

Starting from the most general formulation of stochastic thermodynamics---i.e. a thermodynamically consistent nonautonomous stochastic dynamics describing systems in contact with several reservoirs---, we define a procedure to identify the conservative and the minimal set of nonconservative contributions in the entropy production. The former is expressed as the difference between changes caused by time-dependent drivings and a generalized potential difference. The latter is a sum over the minimal set of flux--force contributions controlling the dissipative flows across the system. When the system is initially prepared at equilibrium (e.g. by turning off drivings and forces), a finite-time detailed fluctuation theorem holds for the different contributions. Our approach relies on identifying the complete set of conserved quantities and can be viewed as the extension of the theory of generalized Gibbs ensembles to nonequilibrium situations., Comment: 24 pages, 11 figures, 3 tables. Published version in double column format

Non-woven mats comprised of liquid crystal-functionalised fibres are coaxially electrospun to create soft gas sensors that function non-electronically, thus requiring no power supply, detect- ing organic vapours at room temperature. The fibres consist of a poly(vinylpyrrolidone) (PVP) sheath surrounding a core of nematic 4-cyano-4ʹpentylbiphenyl (5CB) liquid crystal. Several types of mats, containing uniformly cylindrical or irregular beaded fibres, in uniform or random orientations, are exposed to toluene vapour as a representative volatile organic compound. Between crossed polarisers all mats respond with a fast (response time on the order of a second or faster) reduction in brightness during gas exposure, and they return to the original state upon removal of the gas almost as quickly. With beaded fibres, the response of the mats is visible even without polarisers. We discuss how variations in fibre spinning conditions such as humidity level and the ratio of core-sheath fluid flow rates can be used to tune fibre morphology and thereby the response. Considering future development perspectives, we argue that fibres turned respon- sive through the incorporation of a liquid crystal core show promise as a new generation of sensors with textile form factor, ideal for wearable technology applications.

This paper evaluates the performance of downlink information transmission in three-dimensional (3D) unmanned aerial vehicle (UAV) networks, where multi-tier UAVs of different types and flying altitudes employ directional antennas for communication with ground user equipments (UEs). We introduce a novel tractable antenna gain model, which is a nonlinear function of the elevation angle and the directivity factor, for directional antenna-based UAV communication. Since the transmission range of a UAV is limited by its antenna gain and the receiving threshold of the UEs, only UAVs located in a finite region in each tier can successfully communicate with the UEs. The communication connectivity, association probability as well as coverage probability of the considered multi-tier UAV networks are derived for both line-of-sight (LoS) and non-line-of-sight (NLoS) propagation scenarios. Our analytical results unveil that, for UAV networks employing directional antennas, a necessary tradeoff between connectivity and coverage probability exists. Consequently, UAVs flying at low altitudes require a large elevation angle in order to successfully serve the ground UEs. Moreover, by employing directional antennas an optimal directivity factor exists for maximizing the coverage probability of the multi-tier UAV networks. Simulation results validate the analytical derivations and suggest the application of high-gain directional antennas to improve downlink transmission in the multi-tier UAV networks.