Preface to the special issue on improving software quality through program analysis

This special issue is dedicated to the presentation of novel results in the scope of program analysis, verification, and testing of software to improve its quality. The papers included in the special issue present approaches that successfully combine model-based test case generation, reasoning about functional equivalence, data mining, classification, and the combination of abstraction with model-checking, to address real software applications in realistic settings.

Towards high-towards high-resolution dust reanalysis for Northern Africa, the Middle East and Europe

<p>DustClim (Dust Storms Assessment for the development of user-oriented Climate Services in Northern Africa, Middle East and Europe) is a project of the European Research Area For Climate Services (ERA4CS). DustClim is aiming to provide reliable information on sand and dust storms for developing dust-related services for selected socio-economic sectors: air quality, aviation and solar energy.</p><p>This contribution will describe the work done within the DustClim project towards the production of a dust reanalysis over the domain of Northern Africa, the Middle East and Europe at an unprecedented high spatial resolution (at 10km x 10km) using the state-of-art Multiscale Online Nonhydrostatic Atmosphere Chemistry model (MONARCH) and its data assimilation capability (Di Tomaso et al., 2017). An ensemble-based Kalman filter (namely the local ensemble transform Kalman filter – LETKF) has been utilized to optimally combine model simulations and satellite retrievals.</p><p>Dust ensemble forecasts are used to estimate flow-dependent forecast uncertainty, which is used by the data assimilation scheme to optimally combine model prior information with satellite retrievals. Satellite observations from MODIS Deep Blue with specific observational constraint for dust (Ginoux et al., 2012; Pu and Ginoux, 2016; Sayer et al., 2014) are considered for assimilation over land surfaces, including source regions. MONARCH ensemble has been generated by applying multi-parameters, multi-physics, multi-meteorological initial and boundary conditions perturbations. Sensitive parameters of the assimilation configuration like the balance between observational and background uncertainty, or the spatial location of errors have been carefully calibrated.</p><p>The dust reanalysis for the period 2011-2016 is being compared against independent dust-filtered observations from AERONET (AErosol RObotic NETwork) show the benefit of the assimilation of dust-related MODIS Deep Blue products over areas not easily covered by other observational datasets. Particularly relevant is the improvement of the model skills over the Sahara.</p><p>References:<br>Di Tomaso, E., Schutgens, N. A. J., Jorba, O., and Pérez García-Pando, C. (2017): Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107-1129, doi:10.5194/gmd-10-1107-2017.<br>Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. and Zhao, M. Global-Scale Attribution of Anthropogenic and Natural Dust Sources and Their Emission Rates Based on Modis Deep Blue Aerosol Products. Rev Geophys 50, doi:10.1029/2012rg000388 (2012).<br>Pu, B., and Ginoux, P. (2016). The impact of the Pacific Decadal Oscillation on springtime dust activity in Syria. Atmospheric Chemistry and Physics, 16(21), 13431-13448.<br>Sayer, A. M., Munchak, L. A., Hsu, N. C., Levy, R. C., Bettenhausen, C., and Jeong, M.-J.: MODIS Collection 6 aerosol products: Comparison between Aqua’s e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations, J. Geophys. Res.-Atmos., 119, 13965–13989, doi:10.1002/2014JD022453, 2014.</p><p>Acknowledgement<br>DustClim project is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). We acknowledge PRACE for awarding access to HPC resources through the eDUST and eFRAGMENT1 projects.</p><p> </p>

Model Check What You Can, Runtime Verify the Rest

Model checking and runtime verification are pillars of formal verification but for the most part are used independently. In this position paper we argue that the formal verification community would be well-served by developing theory, algorithms, implementations, and applications that combine model checking and runtime verification into a single, seamless technology. This technology would allow system developers to carefully choose the appropriate balance between offline verification of expressive properties (model checking) and online verification of important parts of the system's state space (runtime verification). We present several realistic examples where such technology appears necessary and a preliminary formalization of the idea.

Pure-projective modules and positive constructibility

In modules many ‘positive’ versions of model-theoretic concepts turn out to be equivalent to concepts known in classical module theory—by ‘positive’ we mean that instead of allowing arbitrary first-order formulas in the model-theoretic definitions only positive primitive formulas are taken into consideration. (This feature is due to Baur's quantifier elimination for modules, cf. [Pr], however we will not make explicit use of it here.) Often this allows one to combine model-theoretic methods with algebraic ones. One instance of this is the result proved in [Rot1] (see also [Rot2]) that the Mittag-Leffler modules are exactly the positively atomic modules. This paper is parallel to the one just mentioned in that it is proved here, Theorem 3.1, that the pure-projective modules are exactly the positively constructible modules. The following parallel facts from module theory and from model theory led us to this result: every pure-projective module is Mittag-Leffler and the converse is true for countable (in fact even countably generated) modules, cf. [RG]; every constructible model is atomic and the converse is true for countable models, cf. [Pi].