Dissection of a Bug Dataset: Anatomy of 395 Patches from Defects4J

Well-designed and publicly available datasets of bugs are an invaluable asset to advance research fields such as fault localization and program repair as they allow directly and fairly comparison between competing techniques and also the replication of experiments. These datasets need to be deeply understood by researchers: the answer for questions like "which bugs can my technique handle?" and "for which bugs is my technique effective?" depends on the comprehension of properties related to bugs and their patches. However, such properties are usually not included in the datasets, and there is still no widely adopted methodology for characterizing bugs and patches. In this work, we deeply study 395 patches of the Defects4J dataset. Quantitative properties (patch size and spreading) were automatically extracted, whereas qualitative ones (repair actions and patterns) were manually extracted using a thematic analysis-based approach. We found that 1) the median size of Defects4J patches is four lines, and almost 30% of the patches contain only addition of lines; 2) 92% of the patches change only one file, and 38% has no spreading at all; 3) the top-3 most applied repair actions are addition of method calls, conditionals, and assignments, occurring in 77% of the patches; and 4) nine repair patterns were found for 95% of the patches, where the most prevalent, appearing in 43% of the patches, is on conditional blocks. These results are useful for researchers to perform advanced analysis on their techniques' results based on Defects4J. Moreover, our set of properties can be used to characterize and compare different bug datasets.Comment: Accepted for SANER'18 (25th edition of IEEE International Conference on Software Analysis, Evolution and Reengineering), Campobasso, Ital

Synthèse bibliographique : l'écologie chimique des coccinelles

Review: chemical ecology of ladybird beetles. This paper reviews the chemical ecology of ladybird beetles (Coleoptera: Coccinellidae) to present the role of semiochemicals involved in plant-ladybird, prey-ladybird and predator-ladybird interactions. Ladybird beetles use these compounds to locate their prey, mate, protect themselves from predation or cannibalism, find a shelter to overwinter or ensure a better survival for their offspring. Thorough studies on ladybird behaviors towards these compounds could lead to their practical implementation in integrated strategies using ladybirds to control pests, like aphids or mealybugs

Etude d’une pyodermite particulièrement rebelle chez le chien isolement d’Actinobacillus equuli associé à un staphylocoque

Vallée A., Durieux Jean, Durieux M., Virat Bernard. Etude d'une pyodermite particulièrement rebelle chez le chien isolement d'Actinobacillus equuli associé à un staphylocoque . In: Bulletin de l'Académie Vétérinaire de France tome 113 n°3, 1960. pp. 153-156

Seasonal patterns of oral antihistamine and intranasal corticosteroid purchases from Australian community pharmacies : a retrospective observational study

Acknowledgments The abstract of this paper was presented at the Respiratory Effectiveness Group 2016 Annual Summit as a poster presentation with interim findings. The poster’s abstract was published in “Poster Abstracts” in The Journal of Thoracic Disease (Vol. 8, Supplement 5, 5 July 2016). http://jtd.amegroups.com/article/view/8504.Peer reviewedPublisher PD

Clusterwise Independent Component Analysis (C-ICA):An R package for clustering subjects based on ICA patterns underlying three-way (brain) data

In many areas of science, like neuroscience, genomics and text mining, several important and challenging research questions imply the study of (subject) heterogeneity present in three-way data. In clinical neuroscience, for example, disclosing differences or heterogeneity between subjects in resting state networks (RSNs) underlying multi-subject fMRI data (i.e., time by voxel by subject three-way data) may advance the subtyping of psychiatric and mental diseases. Recently, the Clusterwise Independent Component Analysis (C-ICA) method was proposed that enables the disclosure of heterogeneity between subjects in RSNs that is present in multi-subject rs-fMRI data [1]. Up to now, however, no publicly available software exists that allows to fit C-ICA to empirical data at hand. The goal of this paper, therefore, is to present the CICA R package, which contains the necessary functions to estimate the C-ICA parameters and to interpret and visualize the analysis output. Further, the package also includes functions to select suitable initial values for the C-ICA model parameters and to determine the optimal number of clusters and components for a given empirical data set (i.e., model selection). The use of the main functions of the package is discussed and demonstrated with simulated data. Herewith, the necessary analytical choices that have to be made by the user (e.g., starting values) are explained and showed step by step. The rich functionality of the package is further illustrated by applying C-ICA to empirical rs-fMRI data from a group of Alzheimer patients and elderly control subjects and to multi-country stock market data. Finally, extensions regarding the C-ICA algorithm and procedures for model selection that could be implemented in future releases of the package are discussed

Links between Climate, Malaria, and Wetlands in the Amazon Basin

Climate changes are altering patterns of temperature and precipitation, potentially affecting regions of malaria transmission. We show that areas of the Amazon Basin with few wetlands show a variable relationship between precipitation and malaria, while areas with extensive wetlands show a negative relationship with malaria incidence