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Artifact-FocalMethodStudy

Artifact of "An Empirical Study on Focal Methods in Deep-Learning-Based Approaches for Assertion Generation".

The datasets used in our experiments can be found at: https://zenodo.org/records/11312096

Datasets

  • TCTracer: Used to evaluate adapted test-to-code traceability techniques (RQ1)
  • ATLAS+: Used to evaluate DL-based assertion generation approaches (RQ2 & RQ3)

TCTracer

Original TCTracer is available at https://zenodo.org/record/4608587#.ZClJly-9FAc. We split test methods in TCTracer using Scripts/split_tctracer.py and check the synatx of splitted test methods manually. Splited TCTracer is shown in Datasets/TCTracer.

There are four subjects in TCTracer: commons-io, commons-lang, gson, and jfreechart, and their source code can be found in Datasets/TCTracer/subjects. In each directory (named after the project name, e.g. Datasets/TCTracer/subjects/commons-io), splitted test methods are put in the directories, and unsplit test methods are put in the txt files.

ATLAS+

ATLAS+ can be found at https://zenodo.org/records/11312096.

Generated by Scripts/dataset_variation.py and Scripts/dataset_variation_new.py based on the ATLAS dataset(https://sites.google.com/view/atlas-nmt/home). All entries in ATLAS with syntax errors are removed.

4 Training sets:

  • TS-atlas: focal methods without implementation identified by atlas
  • TS-null: without focal methods
  • TS-lcba: focal methods without implementation identified by LCBA
  • TS-combined: focal methods without implementation identified by combined score

11 Testing sets:

  • benchmark-0-atlas: focal methods identified by ATLAS
  • benchmark-0-atlas-with-impl: focal methods identified by ATLAS (with implementation)
  • benchmark-1-lcba: focal methods identified by LCBA
  • benchmark-2-nc: focal methods identified by NC
  • benchmark-3-ncc: focal methods identified by NCC
  • benchmark-4-lcsb: focal methods identified by LCS_B
  • benchmark-5-lcsu: focal methods identified by LCS_U
  • benchmark-6-ed: focal methods identified by ED
  • benchmark-7-combined: focal methods identified by CS
  • benchmark-8-nc-432: successful cases with focal methods identified by NC
  • benchmark-9-atlas-432: control group of benchmark-8

Besides, variant datasets for TOGA (ATLAS star) are generated by Scripts/atlas_star_datagen.py.

Evaluation

Test-to-code traceability techniques

Run Scripts/eval_traceability_tech.py.

ATLAS

Run a docker container provided by nvidia for tensorflow. More details in https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow .

docker pull nvcr.io/nvidia/tensorflow:xx.xx-tf1-py3
docker run -i -d -t --gpus all nvcr.io/nvidia/tensorflow:xx.xx-tf1-py3

Training and inference: https://sites.google.com/view/atlas-nmt/home

T5

Use our scriptScripts/generate_results_T5.py.

Datasets: Variant_ATLAS_for_T5

Models and other detials: https://github.com/antonio-mastropaolo/T5-learning-ICSE_2021

IR

Use script Retrieval/IR.py in https://github.com/yh1105/Artifact-of-Assertion-ICSE22

TOGA

Datasets: Variant_ATLAS_for_TOGA

We use the docker comtainer: docker pull edinella/toga-artifact. More detials at https://github.com/microsoft/toga

Fine-tuning script: toga/model/assertions/run_train.sh

Results

Get correct number: Scripts/inference_accuracy.py, e.g.:

python3 inference_accuracy.py -atlas xxxx/assertLines.txt xxxx/prediction.txt
python3 inference_accuracy.py -t5 xxxx/test.tsv xxxx/prediction.txt
python3 inference_accuracy.py -toga xxxx/test.csv xxxx/prediction.csv

Get BLEU: multi-bleu.perl, e.g.:

./multi-bleu.perl xxxx/assertLines.txt < xxxx/prediction.txt

Note for T5, after finetuning a new model, the script fine_tuning_t5.ipynb on google colab can return the accuracy on the corresponding test set.