Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis

Static analysis is used to automatically detect bugs and security breaches, and aids compiler optimization. Whole-program analysis (WPA) can yield high precision, however causes long analysis times and thus does not match common software-development workflows, making it often impractical to use for large, real-world applications. This paper thus presents the design and implementation of ModAlyzer, a novel static-analysis approach that aims at accelerating whole-program analysis by making the analysis modular and compositional. It shows how to compute lossless, persisted summaries for callgraph, points-to and data-flow information, and it reports under which circumstances this function-level compositional analysis outperforms WPA. We implemented ModAlyzer as an extension to LLVM and PhASAR, and applied it to 12 real-world C and C++ applications. At analysis time, ModAlyzer modularly and losslessly summarizes the analysis effect of the library code those applications share, hence avoiding its repeated re-analysis. The experimental results show that the reuse of these summaries can save, on average, 72% of analysis time over WPA. Moreover, because it is lossless, the module-wise analysis fully retains precision and recall. Surprisingly, as our results show, it sometimes even yields precision superior to WPA. The initial summary generation, on average, takes about 3.67 times as long as WPA

Multistage s-t Path: Confronting Similarity with Dissimilarity in Temporal Graphs

Addressing a quest by Gupta et al. [ICALP\u2714], we provide a first, comprehensive study of finding a short s-t path in the multistage graph model, referred to as the Multistage s-t Path problem. Herein, given a sequence of graphs over the same vertex set but changing edge sets, the task is to find short s-t paths in each graph ("snapshot") such that in the found path sequence the consecutive s-t paths are "similar". We measure similarity by the size of the symmetric difference of either the vertex set (vertex-similarity) or the edge set (edge-similarity) of any two consecutive paths. We prove that these two variants of Multistage s-t Path are already NP-hard for an input sequence of only two graphs and maximum vertex degree four. Motivated by this fact and natural applications of this scenario e.g. in traffic route planning, we perform a parameterized complexity analysis. Among other results, for both variants, vertex- and edge-similarity, we prove parameterized hardness (W[1]-hardness) regarding the parameter path length (solution size) for both variants, vertex- and edge-similarity. As a further conceptual study, we then modify the multistage model by asking for dissimilar consecutive paths. One of our main technical results (employing so-called representative sets known from non-temporal settings) is that dissimilarity allows for fixed-parameter tractability for the parameter solution size, contrasting the W[1]-hardness of the corresponding similarity case. We also provide partially positive results concerning efficient and effective data reduction (kernelization)

Colonisation success of introduced oysters is driven by wave-related exposure

The Pacific oyster, Magallana gigas, is an extremely successful invader with established populations in marine and estuarine habitats almost all over the world. Ecological implications of the introduction of this species to indigenous communities are well documented. However, the processes by which this species successfully establishes in a recipient community is still insufficiently understood. The early detection of the oyster at the island of Helgoland (North Sea) provided the ideal opportunity to investigate whether physical mechanisms, such as wave exposure, influence their successful colonisation. We hypothesized that oyster colonisation benefits from wave-protected conditions. For this purpose, we evaluated colonisation success of M. gigas among wave-protected sites and wave-exposed sites along the island’s pier system. The densities of M. gigas were significantly higher at wave-protected sites than at wave-exposed sites, and the frequency distributions of oyster lengths indicated better growth and higher survival rates in the harbours. This higher colonisation success at wave-protected sites may be explained by the relative retention time of water masses in the harbours, probably resulting in both reduced larval drift and lower energy demands for secretion formation (i.e. firmer binding to the substrate). The fact that the density of M. gigas can vary greatly on small spatial scales depending on exposure corroborates a multiple exposure sampling approach to monitor oyster populations in order to avoid potential overestimations of population sizes in given areas

Mapping and modeling eelgrass Zostera marina distribution in the western Baltic Sea

In the northern hemisphere, eelgrass Zostera marina L. is the most important and widespread seagrass species. Despite its ecological importance, baseline data on eelgrass distribution and abundance are mostly absent, particularly in subtidal areas with relatively turbid waters. Here, we report a combined approach of vegetation mapping in the Baltic Sea coupled to a species distribution model (SDM). Eelgrass cover was mapped continuously in the summers of 2010 and 2011 with an underwater towed camera along ~400 km of seafloor. Eelgrass populated 80% of the study region and occurred at water depths between 0.6 and 7.6 m at sheltered to moderately exposed coasts. Mean patch length was 128.6 m but was higher at sheltered locations, with a maximum of >2000 m. The video observations (n = 7824) were used as empiric input to the SDMs. Using generalized additive models, 3 predictor variables (depth, wave exposure, and slope), which were selected based on Akaike’s information criterion, were sufficient to predict eelgrass presence/absence. Along with a very good overall discriminative ability (area under the receiver-operating characteristic curve ROC/AUC = 0.82), depth (as a proxy for light), wave exposure, and slope contributed 66, 29, and 5%, respectively, to the final model. The estimated total areal extent of eelgrass in the study region amounts to 140.5 km2 and comprises about 11.5% of all known Baltic seagrass beds. The present work is, to the best of our knowledge, the largest study undertaken to date on vegetation mapping and the first to assess distribution of eelgrass quantitatively in the western Baltic Sea