Find a way to distinguish regular users from bots

[zurk]

We can take some rule-based approach as a benchmark: email contains bot word or no-reply. However, there are emails like [email protected] that is hard to find. So some ML should be applied to find them. Commit-time-series features can be used.

[zurk]

@warenlg did MVP of this feature (https://src-d.slack.com/archives/C7USX021L/p1563778058004300):

Why don't we remove bot, ci automated stuff etc from the identity matching table with simple regexp ? Right now, I might have 10% of bots in the cloudfroundry identities I'm working with for the demo, e.g.,

["log cache ci", "metric store ci", "loggregator ci",
                  "pivotal publication toolsmiths", "cf-infra-bot",
                  "cloud foundry buildpacks team robot",
                 "garden windows", "final release builder",
                 "pipeline", "flintstone ci", "capi ci",
                  "container networking bot", "cf mega bot",
                 "routing-ci", "cf bpm", "uaa identity bot",
                 "pcf backup & restore ci",
                 "ci bot", "cfcr ci bot", "cfcr"]

I removed 2.5k rows over 15k in total excluding name identities matching [^a-zA-Z]ci[^a-zA-Z]|bot$|pipeline|release|routing

@EgorBu as was discussed I assign this issue to you.

Regardless of an approach you choose, please create a list of filtered bots so we can also review them with eyes and see that we do not filter anything unrelated.

[warenlg]

Thanks K for filling the issue

[vmarkovtsev]

Related to https://github.com/src-d/eee-identity-matching/issues/30

[EgorBu]

Current pattern: r"[^a-zA-Z|]ci\W|[\s-]ci\W|ci[\s-]|[\s-]ci[\s-]|bot$|pipeline|release|routing"

Problems with regexp:

('cici jiayi shen', '[email protected]'), 
('daniel adrian bohbot', '[email protected]'),
('horaci macias', '[email protected]'),
('melvindebot', '[email protected]'),
("daniel obot", "[email protected]")

Some French and Chinese names/surnames may look like bots for regexp

[vmarkovtsev]

@EgorBu Regarding French and Chinese, GitHub profiles often contain the country code. You can take the "users" table from GHTorrent and remove "bots" which have any country assigned.

[EgorBu]

Ideas:

• use regexp to find highly probable bots (19k found from 1300M rows author.date, author.email, author.name, committer.date, committer.email, committer.name)
• calculate authors/committer fraction - it may show that distributions for normal users and bots are different
• contribution activity - time & counts & repositories - it may show that distributions for normal users and bots are different
• entropy of commit messages - idea that bots use heavily some patterns
• intersection of name & repository contributed most
• pretrained (or train on dataset) NN model to extract message embeddings + clustering for messages - if user messages are always from 1-2-3 clusters it could be a signal of bot
• pretrained (or train on dataset) NN model to extract email/name embeddings + classification/clustering - it could be a good approach because we have quite a lot of bot names
• use statistical features, messages, email/names as input for NN to make embeddings (triplet loss to make embeddings of bots closer to each other) + K nearest-neighbors search / classification

Updates:

• launched pipeline for extraction statistics for bots - and it's slow (should be ~50 hours).
• downloaded message dataset, reading about entropy measurements and other possible approaches
• reading and thinking about ideas, coding

Next steps:

• I will rewrite pipeline to use Spark - the task matches the map-reduce paradigm
• Resave datasets as parquet/csv
• launch pipeline for statistics
• launch pipeline for entropy
• intersection of name & repository contributed most

[EgorBu]

There are at least several problems that may affect the quality:

1. Noisy labels -
   • false positives from regexp - like: abbot, julia jenkins and so on
   • false negatives - not detected bots (gardener@tensorflow for example)
2. Model input doesn't contain required info to make a correct prediction
   • false negatives - email doesn't contain bot related info, and the name contains. Ex: [email protected] / Egor's bot for deployment
3. The name doesn't contain the required information to label it as bot
   • false positives - email contains bot related info, and the name doesn't contain. Ex: [email protected] / Egorka -> so it will be labeled as not bot and email tells that it's a bot
4. Metrics. Deduplication:
   • deduplication is done by several fields - and if repository name is included - the quality could be found here - https://gist.github.com/EgorBu/a333409dfc12f89ac5fa1dc71461a3c0
   • it's higher than current - probably it could mean that standard names for bots are much more frequent - and in most of the cases standard names will be detected with high quality
5. Metrics. Usage
   • we still don't have a clear understanding of how it should be applied (for each commit, for each identity, etc) - metrics should be selected on usage
6. Dataset
   • Another possible reason that quality was higher here is some dataset issues

Hypothesis to check

• metrics - clarify how to measure quality
• Dataset
  • select a row in dataset
  • split dataset into 2 parts before some row and after
  • assign labels (0 - before, 1 - after)
  • train classifier - if the quality is better than random - something is fishy with dataset
• false positives - email contains bot related info, and the name doesn't contain and false negatives - email doesn't contain bot related info, and the name contains
  • labels & predictions should be computed
  • extract features separately from names & emails
  • find nearest neighbors by name
  • find nearest neighbors by email
  • several situations are possible:
    • labels & predictions are the same among nearest neighbors for name & emails - perfect
    • labels among nearest neighbors for name are not the same - possible regexp mistakes?
    • predictions are not the same among nearest neighbors for emails - check it
    • labels & predictions are not the same among nearest neighbors for name & emails - possible regexp mistake?
• model overfits to mistakes on regexp
  • hypothesis - number of mistakes is not so big
  • train several models on different chunks of data - it will reduce number of mistakes in each chunk
  • make voting among models when making prediction
  • focus on samples with different predictions and labels
  • focus on samples with different predictions
• features are not good enough
  • BPE could extract features from abot as [a, bot] - and it will make almost impossible for model to differentiate one class from another
    • use token splitter to split [email protected] into [victor, abot, fr]
    • add a feature that will highlight if something is in the exception list
    • don't extract BPE features from exceptions

Papers:

• An Effective Label Noise Model for DNN Text Classification
• Deep Learning is Robust to Massive Label Noise
• Learning with Noisy Labels