TRADER

Installation

1. Please use Ubuntu 16.04 or 18.04, 64bit version

2. Install Docker

   apt-get install -y uidmap
   curl -fsSL https://get.docker.com/rootless | sh

   Make sure the following environment variables are set:

   export PATH=/home/$USER/bin:$PATH
   export DOCKER_HOST=unix:///run/user/1002/docker.sock

3. Start docker

   systemctl --user start docker

4. Pull our docker image

   docker pull traderrnn/trader

5. Start the container

   docker run -it --rm traderrnn/trader bash

Usage

The main functions are located in main.py file. For a test drive, please use the following command:

python3 main.py --phase test

The above command will give a prediction result, which corresponds to the first value in Table 5.

The main.py script provides different options:

• --phase: test for testing model performance, trace for trace divergence analysis
• --method: ori for original model, rs for regularization strategies (baseline), trader for proposed appraoch
• --dataset: different datasets including AppReviews, IMDB, JIRA, StackOverflow, Yelp
• --model_name: different model structures including rnn, lstm and gru
• --model_size: different numbers of hidden neurons including 64, 128 and 256
• --embed_name: different embeddings including glove, w2v and adv

Reproduction

Please use the above options to repoduce the results in Table 3-5 in the paper.

Table 3 and Table 4

To reproduce the results presented in Table 3 and Table 4, please use the following command by providing corresponding model_name, model_size and embed_name:

python3 main.py --phase trace --model_name [model_name] --model_size [model_size] --embed_name [embed_name]

Running the above command will give the time and space overhead of trace divergence analysis, and the fitting scores of oracle and buggy machines.

The time overhead was originally measured on the server equipped with GPUs. The artifact provided here runs on CPU for simplicity. Hence, the measurement of time overhead can be slightly different from that in the paper.

Output sample

Fitting score for oracle machine:       0.974
Fitting score for buggy machine:        0.995
Time overhead:  0.857 (s)
Space overhead: 0.399 (M)

Table 5

To reproduce the results presented in Table 5, please use the following command by providing corresponding model_name, model_size, embed_name and method:

python3 main.py --phase test --model_name [model_name] --model_size [model_size] --embed_name [embed_name] --method [method]

Running the above command will give the prediction result of the corresponding model.

Output sample

Testing ../models/AppReviews_rnn_64_glove_original.ckpt
Test accuracy: 67.65

Other tables are not directly related to the artifact.