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1. Flaky Test Dataset to Accompany "FlakeFlagger: Predicting Flakiness Without Rerunning Tests"

   https://doi.org/10.5281/zenodo.4450722  

   Alshammari, Abdulrahman; Morris, Christopher; Hilton, Michael; Bell, Jonathan (January 2021, Zenodo)

   When developers make changes to their code, they typically run regression tests to detect if their recent changes (re)introduce any bugs. However, many tests are flaky, and their outcomes can change non-deterministically, failing without apparent cause. Flaky tests are a significant nuisance in the development process, since they make it more difficult for developers to trust the outcome of their tests. The traditional approach to identify flaky tests is to rerun them multiple times: if a test is observed both passing and failing on the same code, it is definitely flaky. We conducted a very large empirical study looking for flaky tests by rerunning the test suites of 24 projects 10,000 times each, and found that even with this many reruns, some flaky tests were still not detected. We propose FlakeFlagger, a novel approach that collects a set of features describing the behavior of each test, and then predicts tests that are likely to be flaky based on similar behavioral features. We found that FlakeFlagger correctly labeled at least as many tests as flaky as a state-of-the-art flaky test classifier, but that FlakeFlagger reported far fewer false positives (an increase in precision from just 11% to 60%). This lower false positive rate translates directly to saved time for researchers and developers who use the classification result to guide more expensive flaky test detection processes. By investigating the information gain of each feature, we conclude that test execution time, overall test coverage, coverage of recently changed lines and usage of third party libraries are effective predictors of test flakiness. We did not find any keywords or tokens in the source code of tests that were effective in predicting test flakiness, and did not find the presence of test smells to be effective in predicting test flakiness. This archive contains the dataset that we collected of flaky tests, along with the features that we collected from each test. Contents: Project_Info.csv: List of projects and their revisions studied build-logs-<project-slug>.tgz: An archive of all of the maven build logs from each of the 10,000 runs of that project's test suite.  failing-test-reports-<project-slug>.tgz An archive of all of the surefire XML reports for each failing test of each build of each project. test_results.csv: Summary of the number of passing and failing runs for each test in each project.  "Run ID" is a key into the <project-slug>.tgz archive also in this artifact, which refers to the run that we observed the test fail on. test_features.csv: Summary of the features that each test had, as per our feature detectors described in the paper flakeflagger-code.zip: All scripts used to generate and process these results. These scripts are also located at https://github.com/AlshammariA/FlakeFlagger 

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2. Inline Tests

   Yu Liu; Pengyu Nie; Owolabi Legunsen; Milos Gligoric (October 2022, IEEE/ACM International Conference on Automated Software Engineering)

   Unit tests are widely used to check source code quality, but they can be too coarse-grained or ill-suited for testing individual program statements. We introduce inline tests to make it easier to check for faults in statements. We motivate inline tests through several language features and a common testing scenario in which inline tests could be beneficial. For example, inline tests can allow a developer to test a regular expression in place. We also define language-agnostic requirements for inline testing frameworks. Lastly, we implement I-Test, the first inline testing framework. I-Test works for Python and Java, and it satisfies most of the requirements. We evaluate I-Test on open-source projects by using it to test 144 statements in 31 Python programs and 37 Java programs. We also perform a user study. All nine user study participants say that inline tests are easy to write and that inline testing is beneficial. The cost of running inline tests is negligible, at 0.007x -- 0.014x, and our inline tests helped find two faults that have been fixed by the developers. 

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3. Automatically Reproducing Timing-Dependent Flaky-Test Failures

   https://doi.org/10.5281/zenodo.7041599  

   Shanto Rahman, Aaron Massey (January 2023, Zenodo)

   This artifact contains the source code for FlakeRake, a tool for automatically reproducing timing-dependent flaky-test failures. It also includes raw and processed results produced in the evaluation of FlakeRake   Contents:   Timing-related APIs that FlakeRake considers adding sleeps at: timing-related-apis Anonymized code for FlakeRake (not runnable in its anonymized state, but included for reference; we will publicly release the non-anonymized code under an open source license pending double-blind review): flakerake.tgz Failure messages extracted from the FlakeFlagger dataset: 10k_reruns_failures_by_test.csv.gz  Output from running isolated reruns on each flaky test in the FlakeFlager dataset: 10k_isolated_reruns_all_results.csv.gz (all test results summarized into a CSV), 10k_isolated_reruns_failures_by_test.csv.gz (CSV including just test failures, including failure messages), 10k_isolated_reruns_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven) Output from running the FlakeFlagger replication study (non-isolated 10k reruns):flakeFlaggerReplResults.csv.gz (all test results summarized into a CSV), 10k_reruns_failures_by_test.csv.gz (CSV including just failures, including failure messages), flakeFlaggerRepl_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven - this file is markedly larger than the 10k isolated reruns results because we ran *all* tests in this experiment, whereas the 10k isolated rerun experiment only re-ran the tests that were known to be flaky from the FlakeFlagger dataset). Output from running FlakeRake on each flaky test in the FlakeFlagger dataset: For bisection mode: results-bis.tgz For one-by-one mode: results-obo.tgz Scripts used to execute FlakeRake using an HPC cluster: execution-scripts.tgz Scripts used to execute rerun experiments using an HPC cluster: flakeFlaggerReplScripts.tgz Scripts used to parse the "raw" maven test result XML files in this artifact into the CSV files contained in this artifact: parseSurefireXMLs.tgz  Output from running FlakeRake in “reproduction” mode, attempting to reproduce each of the failures that matched the FlakeFlagger dataset (collected for bisection mode only): results-repro-bis.tgz Analysis of timing-dependent API calls in the failure inducing configurations that matched FlakeFlagger failures: bis-sleepyline.cause-to-matched-fail-configs-found.csv 

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4. UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC) Data Archive and Biodiversity Dataset Graph

   https://doi.org/10.5281/zenodo.5557670  

   Cheadle Center for Biodiversity and Ecological Restoration, University of (January 2021, Zenodo)

   {"Abstract":["A biodiversity dataset graph: UCSB-IZC<\/p>\n\nThe intended use of this archive is to facilitate (meta-)analysis of the UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC). UCSB-IZC is a natural history collection of invertebrate zoology at Cheadle Center of Biodiversity and Ecological Restoration, University of California Santa Barbara.<\/p>\n\nThis dataset provides versioned snapshots of the UCSB-IZC network as tracked by Preston [2,3] on 2021-10-08 using [preston track "https://api.gbif.org/v1/occurrence/search/?datasetKey=d6097f75-f99e-4c2a-b8a5-b0fc213ecbd0"].<\/p>\n\nThis archive contains 14137 images related to 33730 occurrence/specimen records. See included sample-image.jpg and their associated meta-data sample-image.json [4].<\/p>\n\nThe archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance files and data files. Only two index and provenance files are included and have been individually included in this dataset publication. Index files provide a way to links provenance files in time to establish a versioning mechanism.<\/p>\n\nTo retrieve and verify the downloaded UCSB-IZC biodiversity dataset graph, first download preston-*.tar.gz. Then, extract the archives into a "data" folder. Alternatively, you can use the Preston [2,3] command-line tool to "clone" this dataset using:<\/p>\n\n$$ java -jar preston.jar clone --remote https://archive.org/download/preston-ucsb-izc/data.zip/,https://zenodo.org/record/5557670/files<\/p>\n\nAfter that, verify the index of the archive by reproducing the following provenance log history:<\/p>\n\n$$ java -jar preston.jar history\n<urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .<\/p>\n\nTo check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.<\/p>\n\n$ java -jar preston.jar verify\nhash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c    file:/home/jhpoelen/ucsb-izc/data/ce/1d/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c    OK    CONTENT_PRESENT_VALID_HASH    66438    hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c\nhash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844    file:/home/jhpoelen/ucsb-izc/data/f6/8d/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844    OK    CONTENT_PRESENT_VALID_HASH    4093    hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844\nhash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef    file:/home/jhpoelen/ucsb-izc/data/3e/70/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef    OK    CONTENT_PRESENT_VALID_HASH    5746    hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef\nhash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b    file:/home/jhpoelen/ucsb-izc/data/99/58/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b    OK    CONTENT_PRESENT_VALID_HASH    6147    hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b<\/p>\n\nNote that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".<\/p>\n\nFiles in this data publication:<\/p>\n\n--- start of file descriptions ---<\/p>\n\n-- description of archive and its contents (this file) --\nREADME<\/p>\n\n-- executable java jar containing preston [2,3] v0.3.1. --\npreston.jar<\/p>\n\n-- preston archive containing UCSB-IZC (meta-)data/image files, associated provenance logs and a provenance index --\npreston-[00-ff].tar.gz<\/p>\n\n-- individual provenance index files --\n2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a<\/p>\n\n-- example image and meta-data --\nsample-image.jpg (with hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c)\nsample-image.json (with hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844)<\/p>\n\n--- end of file descriptions ---<\/p>\n\n\nReferences<\/p>\n\n[1] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-10-08 as indexed by the Global Biodiversity Informatics Facility (GBIF) with provenance hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36.\n[2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .\n[3] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132\n[4] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-10-08. https://www.gbif.org/occurrence/3323647301 . hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c<\/p>"],"Other":["This work is funded in part by grant NSF OAC 1839201 and NSF DBI 2102006 from the National Science Foundation."]} 

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5. UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC) Data Archive and Biodiversity Dataset Graph

   https://doi.org/10.5281/zenodo.5660088  

   Cheadle Center for Biodiversity and Ecological Restoration, University of (January 2021, Zenodo)

   {"Abstract":["A biodiversity dataset graph: UCSB-IZC<\/p>\n\nThe intended use of this archive is to facilitate (meta-)analysis of the UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC). UCSB-IZC is a natural history collection of invertebrate zoology at Cheadle Center of Biodiversity and Ecological Restoration, University of California Santa Barbara.<\/p>\n\nThis dataset provides versioned snapshots of the UCSB-IZC network as tracked by Preston [2,3] between 2021-10-08 and 2021-11-04 using [preston track "https://api.gbif.org/v1/occurrence/search/?datasetKey=d6097f75-f99e-4c2a-b8a5-b0fc213ecbd0"].<\/p>\n\nThis archive contains 14349 images related to 32533 occurrence/specimen records. See included sample-image.jpg and their associated meta-data sample-image.json [4].<\/p>\n\nThe images were counted using:<\/p>\n\n$$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\\\n | grep -o -P ".*depict"\\\n | sort\\\n | uniq\\\n | wc -l<\/p>\n\nAnd the occurrences were counted using:<\/p>\n\n$$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\\\n | grep -o -P "occurrence/([0-9])+"\\\n | sort\\\n | uniq\\\n | wc -l<\/p>\n\nThe archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance files and data files. Only two index and provenance files are included and have been individually included in this dataset publication. Index files provide a way to links provenance files in time to establish a versioning mechanism.<\/p>\n\nTo retrieve and verify the downloaded UCSB-IZC biodiversity dataset graph, first download preston-*.tar.gz. Then, extract the archives into a "data" folder. Alternatively, you can use the Preston [2,3] command-line tool to "clone" this dataset using:<\/p>\n\n$$ java -jar preston.jar clone --remote https://archive.org/download/preston-ucsb-izc/data.zip/,https://zenodo.org/record/5557670/files,https://zenodo.org/record/5557670/files/5660088<\/p>\n\nAfter that, verify the index of the archive by reproducing the following provenance log history:<\/p>\n\n$$ java -jar preston.jar history\n<urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .\n<hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c> <http://purl.org/pav/previousVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .<\/p>\n\nTo check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.<\/p>\n\n$ java -jar preston.jar verify\nhash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c    file:/home/jhpoelen/ucsb-izc/data/ce/1d/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c    OK    CONTENT_PRESENT_VALID_HASH    66438    hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c\nhash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844    file:/home/jhpoelen/ucsb-izc/data/f6/8d/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844    OK    CONTENT_PRESENT_VALID_HASH    4093    hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844\nhash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef    file:/home/jhpoelen/ucsb-izc/data/3e/70/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef    OK    CONTENT_PRESENT_VALID_HASH    5746    hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef\nhash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b    file:/home/jhpoelen/ucsb-izc/data/99/58/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b    OK    CONTENT_PRESENT_VALID_HASH    6147    hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b<\/p>\n\nNote that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".<\/p>\n\nFiles in this data publication:<\/p>\n\n--- start of file descriptions ---<\/p>\n\n-- description of archive and its contents (this file) --\nREADME<\/p>\n\n-- executable java jar containing preston [2,3] v0.3.1. --\npreston.jar<\/p>\n\n-- preston archive containing UCSB-IZC (meta-)data/image files, associated provenance logs and a provenance index --\npreston-[00-ff].tar.gz<\/p>\n\n-- individual provenance index files --\n2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a<\/p>\n\n-- example image and meta-data --\nsample-image.jpg (with hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c)\nsample-image.json (with hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844)<\/p>\n\n--- end of file descriptions ---<\/p>\n\n\nReferences<\/p>\n\n[1] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-11-04 as indexed by the Global Biodiversity Informatics Facility (GBIF) with provenance hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36 hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c.\n[2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .\n[3] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132\n[4] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-10-08. https://www.gbif.org/occurrence/3323647301 . hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c<\/p>"],"Other":["This work is funded in part by grant NSF OAC 1839201 and NSF DBI 2102006 from the National Science Foundation."]} 

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