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9 results for “code coverage”
Unveiling the Relationship Between Continuous Integration and Code Coverage
<p>Artifacts from <strong>Unveiling Relationships Between Continuous Integration and Code Coverage</strong></p>
Excluding Code from Test Coverage (dataset)
<p>This is the dataset for the paper "What Code Is Deliberately Excluded from Test Coverage and Why?", IEEE/ACM International Conference on Mining Software Repositories (MSR), 2021.</p> <p>This is the dataset for the paper "Excluding Code from Test Coverage: Practices, Motivations, and Impact", Empirical Software Engineering, 2021.</p>
Exploring the Interaction of Code Coverage and Non-Coverage Objectives in Search-Based Test Generation
<p>Data Package for "Exploring the Interaction of Code Coverage and Non-Coverage Objectives in Search-Based Test Generation"</p> <p>This package contains data generated as part of our experiments on EvoSuite and Defects4J.</p> <p>This paper is currently under submission.</p> <p>This package contains experimental data and the algorithms that generated them in folder "framework\test\". Specifically, in the folder "framework\test\Experiments", the data folders contain the suites generated by each technique for each project and fault ID. </p> <p>If you have questions, please contact Afonso Fontes at afonsohfontes@gmail.com.</p>
Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements (Replication Package)
<p>Replication package for Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements</p>
Code Coverage Dataset Sample
<p>This is a code coverage dataset sample for submission the The Web Conference 2023. It is submitted anonymously to preserve the double-blind review process.</p>
Data for the thesis "Exploring Heuristics for Predicting Microbenchmark Stability and Code Coverage using Static Code Analysis"
Open the record for dataset details and reuse information.
Studying the Impact of Early Test Termination Due to Assertion Failure on Code Coverage and Spectrum-based Fault Localization
<p>A dataset for early test termination</p>
Full-coverage landscape of extracellular RNAs, coding and non-coding, secreted by human glioma stem cells
GEO Series GSE93143. Mus musculus; other sequences; Homo sapiens. 45 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Artifacts for Code Coverage Profiler Validation with Debugging Support
<p><strong>Code Coverage Profiler Validation with Debugging Support</strong></p> <p>Table of Contents</p> <ul> <li>Background</li> <li>Install</li> <li>Usage</li> <li>Experiment</li> <li>Violations</li> <li>Bugs</li> </ul> <p><strong>Background</strong></p> <p><strong>DeCov</strong> is the tool for code coverage profiler validation with debugging support. We totally submit 24 bug reports for gcov and llvm-cov. Of these 24 bug reports, 18 are confirmed/fixed by developers, 2 are marked as duplicate, and the other 4 bug reports are waiting for the developers' confirmation.</p> <p><strong>Install</strong></p> <p>This project uses <a href="https://gcc.gnu.org/">gcc</a>, <a href="http://www.gnu.org/software/gdb/">gdb</a>, <a href="http://llvm.org/">llvm</a> and <a href="http://lldb.llvm.org/">lldb</a>. Go check them out if you don't have them locally installed.</p> <p><code>apt install gcc gdb llvm lldb </code></p> <p><strong>Usage</strong></p> <p>To use DeCov for gcov validation:</p> <p><code>python3 decov-gcov-pexpect.py -s test.c </code></p> <p>or</p> <p><code>python3 decov-gcov-pexpect.py -s testprograms </code></p> <p>To use DeCov for llvm-cov validation:</p> <p><code>python3 decov-llvmcov-pexpect.py -s test.c </code></p> <p>or</p> <p><code>python3 decov-llvmcov-pexpect.py -s testprograms </code></p> <p><strong>Experiment</strong></p> <p>First, download the gcc-10.1.0.tar.xz:</p> <p><code><code>wget http://ftp.gnu.org/gnu/gcc/gcc-10.1.0/gcc-10.1.0.tar.xz </code></code></p> <p>Then, unzip gcc-10.1.0:</p> <p><code>tar -xvf gcc-10.1.0.tar.xz </code></p> <p>Finally, run Decov over the testsuite of gcc-10.1.0:</p> <p><code>python3 decov-gcov-pexpect.py -s gcc-10.1.0/gcc/testsuite/ </code></p> <p>or</p> <p><code>python3 decov-llvmcov-pexpect.py -s gcc-10.1.0/gcc/testsuite/ </code></p> <p>Note that, we recommend to use the parallel option when using our tool if your computer has multi-cores:</p> <p><code>python3 decov-gcov-pexpect.py --parallel -s gcc-10.1.0/gcc/testsuite/ </code></p> <p>or</p> <p><code>python3 decov-llvmcov-pexpect.py --parallel -s gcc-10.1.0/gcc/testsuite/ </code></p> <p><strong>Violations</strong></p> <p>Experimental data are in the Expr folder:</p> <ul> <li>files-DIFFCOV-gcc.txt and files-DIFFCOV-clang.txt list the filenames in the testsuites of GCC-10.1.0 that the coverage statistics from coverage profilers inconsistent with the hit count from debuggers</li> <li>DIFFCOV-Gcov-vs-GDB.txt and DIFFCOV-LLVMcov-vs-LLDB.txt list the filesname and the the respected lines of code that violating <em>HMR</em>, coverage statistics is not consistent with the hit count</li> <li>files-SAMECOV-gcc.txt and files-SAMECOV-clang.txt list the filenames in the testsuites of GCC-10.1.0 that the coverage statistics from coverage profilers consistent with the hit count from debuggers.</li> <li>files-CPLERROR-gcc.txt and files-CPLERROR-clang.txt list the filenames in the testsuites of GCC-10.1.0 that cannot be correctly compiled independently by respected compilers</li> </ul> <p><strong>Bugs</strong></p> <p>Bugs reported by <strong>DeCov</strong> for LLVM-cov:</p> <p>llvm-cov 48818 Status History: "NEW"</p> <p>llvm-cov 48795 Status History: "NEW"</p> <p>llvm-cov 48278 Status History: "NEW" -> "DUPLICATE"</p> <p>llvm-cov 48264 Status History: "NEW" -> "INVALID" -> "REOPENED"</p> <p>llvm-cov 48263 Status History: "NEW"</p> <p>llvm-cov 45850 Status History: "New" -> "CONFIRMED"</p> <p>llvm-cov 45849 Status History: "New" -> "FIXED" -> "REOPENED" -> "FIXED"</p> <p>llvm-cov 45846 Status History: "NEW" -> "CONFIRMED"</p> <p>llvm-cov 45845 Status History: "NEW" -> "CONFIRMED"</p> <p>llvm-cov 45844 Status History: "NEW" -> "DUPLICATE"</p> <p>llvm-cov 45821 Status History: "NEW" -> "CONFIRMED"</p> <p>llvm-cov 45678 Status History: "NEW" -> "CONFIRMED"</p> <p>llvm-cov 45595 Status History: "NEW" -> "CONFIRMED"</p> <p>Bugs reported by <strong>DeCov</strong> for Gcov:</p> <p>gcov 97925 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 97924 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 97923 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 97917 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 97910 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 93757 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 93735 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 93726 Status History: "UNCONFIRMED" -> "WAIT"</p> <p>gcov 93725 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 93706 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 93680 Status History: "UNCONFIRMED" -> "NEW"</p>
ScienceDex guides
Understand access before you commit
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.