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51 results for “crashes”
Replication package of "Search-based Crash Reproduction using Behavioral Model Seeding"
<p>Search-based crash reproduction approaches assist developers during debugging by generating a test case which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the search process. With seeding, the existing usages of classes can be used in the<br> search process to produce realistic sequences of method calls which create the required objects. In this study, we introduce behavioral model seeding: a new seeding method which learns class usages from both<br> the system under test and existing test cases. Learned usages are then synthesized in a behavioral model (state machine). Then, this model serves to guide the evolutionary process. To assess behavioral model-seeding, we evaluate it against test-seeding (the state-of-the-art technique for seeding realistic objects) and no-seeding (without seeding any class usage). For this evaluation, we use a benchmark of 122 hard-to-reproduce crashes stemming from six open-source projects. Our results indicate that behavioral model-seeding outperforms both test seeding and no-seeding by a minimum of 6% without any notable negative impact on efficiency.</p>
Artifacts for ASE 2022 Paper -- FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures
<p><strong>Artifacts for FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures</strong></p> <p>Fuzzing has been an important approach for finding bugs and vulnerabilities in programs. Many fuzzers deployed in industry run daily and can generate an overwhelming number of crashes. Diagnosing such crashes can be very challenging and time consuming. Existing fuzzers typically employ heuristics such as code coverage or call stack hashes to weed out duplicate reporting of bugs. While these heuristics are cheap, they are often imprecise and end up still reporting many "unique" crashes corresponding to the same bug. In this paper, we present <em>FuzzerAid</em> that uses <em>fault signatures</em> to group crashes reported by the fuzzers. Fault signature is a small executable program and consists of a selection of necessary statements from the original program that can reproduce a bug. In our approach, we first generate a fault signature using a given crash. We then execute the fault signature with other crash inducing inputs. If the failure is reproduced, we classify the crashes into the group labeled with the fault signature; if not, we generate a new fault signature. After all the crash inducing inputs are classified, we further merge the fault signatures of the same root cause into a group. We implemented our approach in a tool called <em>FuzzerAid</em> and evaluated it on 3020 crashes generated from 15 real-world bugs and 4 large open source projects. Our evaluation shows that we are able to correctly group 99.1% of the crashes and reported only 17 (+2) "unique" bugs, outperforming the state-of-the-art fuzzers.</p> <p> </p> <p><strong>Change log for v1.0.1:</strong></p> <p>Fix wrong Bug ID for <em>sqlite</em> and add README clarification.</p> <p><strong>Change log for v1.0.2:</strong></p> <p>Added an example linking data in the repository to the table.</p>
Mobile Service Robots Crash Testing with Pedestrians: Safety Assessment with Child and Adult Dummies
<p>Data published with the manuscript: “<em>Estimating risks posed by personal mobility devices and service robots to pedestrians: comparative crash testing of adult versus child dummies</em>”. 2021 (Paez-Granados & Billard, 2021)<br> <strong>Summary:</strong></p> <p>This dataset contains injury measures during collisions between a mobile service robot - Qolo - (Paez-Granados, et al, 2018) and pedestrian dummies: male adult Hybrid-III (H3) and child model 3-years-old (Q3). We present multiple collision scenarios for the assessment of pedestrian safety, considering possible impacts at the legs for adult pedestrians, and legs, chest and head for children. In these tests, we followed known methods of safety analysis used in car crash testing and used a standing wheelchair robot "Qolo" as a representative system of mobile service robots, such as delivery bots (robot without occupant), person carrier robots, autonomous wheelchairs, standing mobility vehicles, and other transport robots expected to operate in pedestrian and public areas.</p> <p>The robot was equipped with an experimental front structure allowing different bumper heights and measurement of reaction forces. On the other hand, the human dummies were equipped with standard instrumentation calibrated in accordance with SAE J211-1 for impact tests, thus, the child dummy, Q3 provided head accelerations, neck forces and moments, chest deflections, and accelerations; and pelvis accelerations. The dummy H3 provided forces and moments at the tibia and femur, and accelerations at the pelvis, chest, and head. You will find scripts to read and plot the data, as well as, analysis of the injury risk based on standard crash testing metrics: Head Injury Criteria (HIC-15), head acceleration (a_3ms), Neck Injury (Nij), Chest deflection (CD), and tibia injury (TI).</p> <p><strong>Instructions: </strong></p> <p><em>This dataset contains the following main files:</em></p> <ol> <li><strong><em>Data Description.pdf</em>: </strong>Highly recommended to read through this file for understanding the setup of the collected dataset, as well as, the submitted manuscript.</li> <li><em><strong>collision_test_rawdata.zip</strong>: </em>This file contains all the raw data for each sensor as mentioned in table 3, organized in independent subfolders as described in table 2.<em> ‘test_name’/01_values/’testName’_CFC1000.xlsx</em></li> <li><em><strong>collision_test_analysis.zip</strong>: </em>This file contains all the processed data for each sensor in order to apply known injury metrics (Nij, HIC15, acc_3ms, TI, CC, VCI), organized in independent subfolders as described in table 2.<em>‘test_name’/01_values/’testName’_Analysis_v2.xlsx --> </em>Dataset with filtered sensor data accordingly to SAEJ21.</li> <li><em><strong>collision_data_matlab_structure.zip</strong>:</em><em> Matlab containers with all data - also available as .mat files for easy reading from Code Ocean capsule.</em></li> <li><em><em><strong>scripts-crash-test-service-robots.zip</strong>:</em> processing of the dataset is provided in this file with structure of data in Matlab containers and scripts for visualizing the data (see section III), further analysis scripts in the linked GitHub: <a href="https://github.com/epfl-lasa/crash-tests-service-robots">https://github.com/epfl-lasa/crash-tests-service-robots</a></em></li> </ol>
Replication package of "Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper Objectives"
<p>The replication package for the study about using new helper objectives (MOHO) for crash reproduction. This study has been accepted at ASE 2020.</p> <p> </p> <p>Abstract:</p> <p>Evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called <em>Crash Distance</em> to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called <em>MO-HO</em>, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-Objectives for crash reproduction, namely <em>test length</em> (to minimize) and <em>method sequence diversity</em> (to maximize), in addition to <em>Crash Distance</em>.</p> <p>We assessed <em>MO-HO</em> using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 hard-to-reproduce crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for <em>MO-HO</em>.</p> <p>We evaluated this best-performing algorithm for <em>MO-HO</em> against the state-of-the-art: single-objective approach (Single-Objective Search) and decomposition-based multi-objectivization approach (<em>De-MO</em>). Our results show that <em>MO-HO</em> reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, <em>MO-HO</em> improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-Objective Search and <em>De-MO</em>, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time. </p>
Supporting Movies from: Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California
<div> <div> <div> <p>This repository includes supplementary movies from the manuscript titled, "Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California," submitted to the Journal of Geophysical Research: Solid Earth.</p> <p> </p> <p>Movies S1 and S2. These two movies taken during array deployment 4 on October 20, 2023 show the NW tip of Coal Oil Point at the left of the field of view and Sands Beach northwest of that toward the right. Frames have the same figure layout as Figure 4 of the main text.</p> <p>Movie S3. Same as Movies S1 and S2 but with the NW tip of Coal Oil Point at the right of the field of view and Devereux Beach southeast of that toward the left.</p> </div> </div> </div>
Does Unit-Tested Code Crash? A Case Study of Eclipse
<ul> <li>The "jacoco.xml" file contains the code coverage of Eclipse Project generated by JaCoCo</li> <li>The "incidents (stack traces).zip" file contains the stacktraces files (in JSON format) with the specific popular release and product that we used for our research.</li> <li>All The Eclipse stacktraces can be downloaded <a href="http://software-data.org/datasets/aeri-stacktraces/downloads/incidents_full.tar.bz2">here.</a></li> <li>The "output.csv" contains the data that we produced after combining JaCoCo Report and stacktraces.</li> </ul>
Replication package of A benchmark-based evaluation of search-based crash reproduction
<p>Release of the reproduction package of Soltani, M., Derakhshanfar, P., Devroey, X. and van Deursen, A. (2020). A benchmark-based evaluation of search-based crash reproduction. In Empirical Software Engineering. 25, 1 (Jan. 2020), pp. 96–138.</p>
Figure 7 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 7. GLMM diagrams illustrating the effect of the main and alternative prey taxa and the two derived indices on the number of fledglings (A: Common vole, B: Apodemus genus, C: Microtinae/Murinae ratio, D: Trophic level index).
Figure 5 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 5. GLMM diagrams illustrating the effect of the main and alternative prey taxa and the two derived indices on clutch size (A: Common vole, B–C: Apodemus genus, D–E: Microtinae/Murinae ratio, F: Trophic level index).
Figure 4 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 4. Box plots of the relative frequency of the main and alternative prey taxa. The bottom and top limits of each box are the lower and upper quartiles; error bars equal ±1.5 times the interquartile range; the horizontal black band within each box is the median; and the red triangle is the mean.
Figure 6 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 6. GLMM diagrams illustrating the effect of the main and alternative prey taxa and the two derived indices on the number of hatchlings (A: Common vole, B: Apodemus genus, C: Microtinae/Murinae ratio, D: Trophic level index).
Figure 3 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 3. Rank abundance curves of the prey composition of the common barn-owl in different outbreak and crash years, and cumulative results of these two periods.
Figure 2 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 2. Box plots of barn owls' breeding parameters (A: clutch size; B: number of hatchlings; C: number of fledglings). The bottom and top limits of each box are the lower and upper quartiles; error bars equal ±1.5 times the interquartile range; the horizontal black band within each box is the median; and the red triangle is the mean.
Figure 1 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 1. Study area in Baranya County (Hungary), showing the location of sampled nesting pairs (settlements).
Queensland Road Traffic Crashes Fatalities and Hospitalisations 2011-2021
<p>Data sets that have been prepared from the Open Data Portal of the Queensland Government (2022) available from <a href="https://www.data.qld.gov.au/dataset/crash-data-from-queensland-roads">https://www.data.qld.gov.au/dataset/crash-data-from-queensland-roads</a>.</p> <p>Provides information from 2011 - 2021 on all police reported fatalities and hospitalisations that have occurred during this time period, showing road conditions and driver demographics.</p>
JCrashPack: A Java Crash Reproduction Benchmark
<p>A Java Crash reproduction benchmark</p>
B-29 Superfortress Over Exposed Crash Site
This is a photogrammetry scan of the crash site of Over Exposed, a Boeing RB-29A Superfortress. It was used as a photographic plane and took photos of some of the nuclear tests at Bikini Atoll. Sadly none of the crew members survived the crash. More info on the history can be found here: https://en.wikipedia.org/wiki/Bleaklow_Bomber Source: Objaverse 1.0 / Sketchfab
Debrise from the crash plane IL-18 (OK-NAB)
The debris without identification of the location on the crash plane ( July 28, 1976) the aircraft IL-18 (OK-NAB) into the lake Zlate piesky Bratislava Recovery the debrise: January 30, 2021 Crash plane details: https://en.wikipedia.org/wiki/%C4%8CSA_Flight_001 Source: Objaverse 1.0 / Sketchfab
Modeling Crash Severity and Collision Types Using Machine Learning
<p>Traffic safety analysis is the fundamental step for reducing economic, social, and environmental cost incurred due to traffic accidents. The essence of traffic safety is understanding the factors affecting crash occurrence, injury severity and collision type and their underlying relationships and predict-prevent future crash instances. Crash injury severity studies in past have utilized numerous statistical, econometric and Machine Learning (ML) and Artificial Intelligence (AI) tools to extract the underlying relationship between the crash causal factors and the consequent severity or collision type. The study aims to explore the Multi-Label Classification (MLC) tool from the domain of Artificial Intelligence (AI) for classification problems in the setting of traffic safety. MLC finds its application primarily in protein function, semantic scene, and music categorization problems. In the real world, multiple heterogenous subjective factors decide the extent of damage/severity of a particular crash instance. Theoretically, the traffic collision type and crash severity type can be correlated, and thus, it is intuitive to model them simultaneously. The ability of MLC to categorize an entity under analysis to more than one labels, correlated or uncorrelated, provides the approach an edge over the single-class (binary) or multi-class classification approach. The MLC based classification model was calibrated and tested using the historical crash data extracted for the state of Texas. The selection of study area was based on a link-level unsupervised principal component analysis-based clustering approach. Similar clustering approach was also tested at the county-level to understand the spatial behavior and thus transferability of the MLC approach to other key cities in the state. The performance of the proposed approach was tested, compared, and quantified with the conventional binary/multi-class classification tools used in the traffic safety domain. Inferences from the preliminary numerical analysis indicates that the proposed multi-label classification approach has promising performance compared to the traditional classification approaches, specifically found in traffic safety literatures.</p>
Reproduction Package for A Partial Reproduction of A Guided Genetic Algorithm for Crash Reproduction
<p>A reproduction package for "A Partial Reproduction of A Guided Genetic Algorithm for Crash Reproduction"</p> <p>Includes datasets and source code for reproducing our results</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.