Participant %4: @tdurieux, INRIA

[tdurieux]

Hi all,

I just did a quick naive solution based on string distance:

Dataset	Perfect Match	In Top 10	Recall	Loss
Bench 1	3791	4322 98%	0.86	0.13615878141899027
Bench 2	9910	10805 97%	0.89	0.10263617900182995

[monperrus]

CodRep is like CandyCrush, there are plenty of easy tasks at the beginning but it is super hard at the end :-)

[tdurieux]

Hey @monperrus,

The ranking does not contain my last score (see the google group), you put the old results with this commit: 4b3af7a5261d669c8de025bdb9e7ae83329c3d2e

Dataset 1: 0.1143165305556653 Dataset 2: 0.08536293667171425

[monperrus]

could you do a pull request?

[monperrus]

Thanks for the pull request to update your score!

[monperrus]

the readme is now up to date with you top scores!

[tdurieux]

@chenzimin @monperrus my result for the new bench:

Dataset	Perfect Match	In Top 10	Loss
Dataset 3	17410/18633 93%	10 18438 98%	0.06407823026301902

[chenzimin]

@tdurieux Good job, I have updated the ranking accordingly.

[tdurieux]

I did a small update on my project, here my results. (I have to keep the best score, haha :smile:)

Dataset	Perfect Match	In Top 10	Loss
Dataset 1	3934	4334 98%	0.10400918447628195
Dataset 2	10123	10850 98%	0.08405196035050681
Dataset 3	17520	18454 99%	0.05824092991175515
Dataset 4	15786	16936 98%	0.07700153400317002

@cesarsotovalero is still better on the dataset 4

Execution time: < 1min per dataset

Interesting fact: when there are several times the same lines in the file, it is better to select the last one. I have no clue why. WDYT?

[monperrus]

Interesting fact: when there are several times the same lines in the file, it is better to select the last one. Indeed fun

I have no clue why. WDYT? One idea: because code tends to be added to the end of the file, and new code tend to be more buggy than old code.

[tdurieux]

My final results:

Dataset	Perfect Match	In Top 10	Loss
Dataset 1	3967	4346 98%	0.09654287550022056
Dataset 2	11069	10908 98%	0.07930336605281313
Dataset 3	17753	18498 99%	0.04631417984476575
Dataset 4	16074	17031 99%	0.06053843584998035

[monperrus]

not bad!

[tdurieux]

I don't think we can do much better with my approach, I achieved more than 90% of perfect prediction on the 4 benches. I think it is better possible to do better with ML with the features I used.

[jjhenkel]

Hi @tdurieux,

Congrats on the smart features and overall technique! After you released your code I went ahead and made some small modifications so that I could test the performance of a hybrid model that uses your features (plus some more simple text-distance features) and our learning-to-rank approach.

I also did a small grid-search over some hyper-parameters of this model to get a better understanding of what parameters work well.

A model with the following hyper-parameters trained for a relatively short time over your features can produce an incremental improvement on the 5th dataset:

# Hyper-parameters / Model
# Leaves       128
# Trees        2000
# Min. Support 8
# Stop At      30 
# Model        LambdaMART

root@442d3980f24a:/> python3 /app/src/guesser.py /tmp/scores.txt /data/Dataset5/Tasks | python3 /app/src/evaluate.py -d /data/Dataset5
Total files: 18366
Average line error: 0.07180536507565463 (the lower, the better)
Recall@1: 0.9273657846019819 (the higher, the better)

I think this could be pushed even further by offering more features (essentially all the features we both used) and perhaps increasing the capacity of the model further. (From my grid-search it seems that, in general, incremental increases in the number of leaves allowed in the tree improve performance on the validation set. To avoid overfitting I've switched from training/validating on datasets 1,2,3, and 4 to training just on datasets 1,2,3 and validating on the 4th set.)

[tdurieux]

Hi @jjhenkel,

That is really nice that you look at my features (sorry it is a little bit messy) and succeed to create a model from it. It is impressive.

Is there a way for you to know which features are more important for the prediction?

For extracting the feature, I extract some high-level AST of each line (basically the type of each code element, VARIABLE, CLASS, STRING, COMMENT, NUMBER, TOKEN, KEYWORD), do you think it can be used in a model?

[jjhenkel]

Hi @tdurieux,

The feature importance is a good question! I'm not entirely sure yet---I think RankLib has a set of tools for looking at the importance of individual features (for certain supported models) but I haven't tried doing anything with that yet.

The approximate parse you do for each line is a nice asset! They could maybe be used to create some set similarity based features. There are some techniques that could take advantage of that information and also avoid any hand-designed features/filters. I haven't investigated any of that seriously yet but there's been some interesting work on embedding edits that could maybe be applied to this challenge.

[monperrus]

Thanks Jordan for the follow-up, as Thomas said, that's indeed really interesting.

there's been some interesting work on embedding edits Which papers do you think of?

[jjhenkel]

This paper is the one I'm thinking of: https://arxiv.org/abs/1810.13337. The embedded edits could be fed directly to the learning to rank algorithms or to some other neural architecture and, in doing so, any hand-made features could be avoided. No idea if this would work---but, it's an interesting approach I've wanted to try.

[monperrus]

This paper is on my radar! We need an implementation of it for Java patches.

[tdurieux]

@monperrus you should update the ranking with the new best score ;)

[chenzimin]

@tdurieux Done!