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haystack
LICENSE
README.md
find_intermittent_issues.py
get_data.py
needle_intermittent_issues.json
parse_known_issues.py
requirements.txt
update.sh

README.md

Linux Kernel Functional Testing - Machine Learning

This repository is a proof-of-concept machine learning project for LFKT.

Contents

First, the needle(s) and the haystack:

  • haystack/: Cache of historical test result data retrieved from qa-reports. Due to size (about 1GB on disk per branch), this contains branches 4.4 and 4.9 only.
  • needle_intermittent_issues.json: A file of needles. In this case, tests that have manually been identified as intermittently failing (i.e. flaky) tests.

The other supporting scripts:

  • get_data.py: Populate haystack. Also serves as a library for retrieving data from haystack or qa-reports.
  • find_intermittent_issues.py: Example naive approach to finding inconsistently failing tests (i.e. flaky tests).
  • parse_known_issues.py: Used to populate needle_intermittent_issues.json
  • update.sh: Generates needle_intermittent_issues.json, using parse_known_issues.py.

Design

The concept behind this project is to provide the historical LKFT data in a way that could be used to answer questions about the data, possibly using machine learning. All of LKFT's data is available via qa-report's API, but it is costly to retrieve all of the data over the API. In this repository, get_data.py retrieves the useful data from the API, and stores it in accessible json files in haystack/.

Note that the implementation is a tad sloppy (this is a first attempt): When using get_data.py, some urls will be retrieved from qa-reports but most should be retrieved from the haystack/ cache. The implementation isn't very robust, but it is hopefully straight forward (optimized for simplicity). A more sophisticated approach would be to use a database dump directly, or use the API more directly along with a more correct caching implementation.

Problem

The Linaro team behind LKFT spends a lot of time sifting through noisy test data trying to determine when regressions actually happen. However, because tests are run on real, embedded hardware, in real labs run by actual humans, the data is never good enough to rely on without heavy curation.

The goal of this project is to provide meaningful signal from the data in order to be able to provide more automated reporting, find information that mere humans are not able to deduce, and make the data more accessible more people.

Three initial problems have been identified.

Bad Tests

A bad test is one whose results cannot be trusted. Such tests, which may fail intermittently, cause noise in the data that may detract from actual test regressions.

Note that sometimes an intermittently failing test could be an actual regression, or a hardware problem on a particular board.

Bad Hardware

What if there are 10 identical boards deployed, and a particular test fails on a particular board. What if it only fails 50% of the time on that board? This could indicate a hardware problem, which is very difficult for humans to detect.

Regressions

A regression is a test begins failing due to an actual problem in the linux kernel. Often these can be seen first in next, followed by mainline, and then often backported to a stable tree. While many regressions have been discovered, reported, and fixed - the data most certainly contains additional regressions that nobody has noticed.

Examples

Intermittent Issues

The needle_intermittent_issues.json file is a json representation of actual intermittent known issues identified and maintained by the LKFT team. These should all be considered actual intermittent issues, but there are other intermittent issues that are not listed in the file. Often a test has to be quite noisy before a human bothers to add it as a known intermittent issue.

This needle file is generated using parse_known_issues.py, which retrieves the lists of known intermittent issues from qa-reports-known-issues. It could also retrieve them from the qa-reports API, but this author did not think of that at the time.

Finally, find_intermittent_issues.py is a very naive example of traversing through the haystack to try to discover intermittent failures using hard coded thresholds. For example, it looks at the 10 most recent builds and if any test has changed state (from good to bad or bad to good) more than 2 times in a given environment, it will be printed as an intermittent issue.

Ideas

This could be called 'TODO', except that not all of these things should probably be done. They're just ideas.

  • Implement a better qa-reports client class that can properly model projects, builds, and test data.
  • Re-structure the haystack/ so that there is a logical hierarchy of project/build/testruns/. This would break with the layout in the qa-reports API, but make more sense for a filesystem.
  • Retrieve unstructured log files. Currently the only files in haystack/ are json structured data, but raw logs are also available in qa-reports.
  • Implement support in SQUAD to be able to get the data out more efficiently. Perhaps exporting build or project tarballs.
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