Data

Please download from this Google Drive and put it in the ./data/ directory.

Experimental Results

Check the results in ./results directory.

Commands

Generate input for REP

To run REP, we need prepare one file for each different json file: dbrd_test.txt. Go to SABD directory and run the following command:

python classical_approach/generate_input_dbrd.py --database ../data/keywords/spark/spark_gpt_p1_r1.json --test ../data/raw/test_spark.txt --output ../data/rep/dbrd_test_spark_gpt_p1_r1.txt

python classical_approach/generate_input_dbrd.py --database ../data/keywords/spark/spark_kpminer_test_1.json --test ../data/raw/test_spark.txt --output ../data/rep/dbrd_test_spark_kpminer.txt

Run REP

build/bin/fast-dbrd -n 20240508_spark_gpt_p1_r1 -r ranknet-configs/full-textual-no-version.cfg --ts /app/tosem-sampel-data/spark/timestamp_file.txt --time-constraint 365 --training-duplicates 273 --recommend /app/tosem-sampel-data/spark/dbrd_test_spark_gpt_p1_r1.txt --trainfile /app/tosem-sampel-data/spark/sampled_training_spark_triplets_random_1.txt

build/bin/fast-dbrd -n 20240510_hadoop_gpt_length_content_r1 -r ranknet-configs/full-textual-no-version.cfg --ts /app/tosem-sampel-data/hadoop/timestamp_file.txt --time-constraint 365 --training-duplicates 285 --recommend /app/tosem-sampel-data/hadoop/dbrd_test_hadoop_gpt_length_content_r${i}.txt --trainfile /app/tosem-sampel-data/hadoop/sampled_training_hadoop_triplets_random_1.txt

build/bin/fast-dbrd -n 20240510_kibana_gpt_p1_r1 -r ranknet-configs/full-textual-no-version.cfg --ts /app/tosem-sampel-data/kibana/timestamp_file.txt --time-constraint 365 --training-duplicates 286 --recommend /app/tosem-sampel-data/kibana/dbrd_test_kibana_gpt_p1_r${i}.txt --trainfile /app/tosem-sampel-data/kibana/training_kibana_triplets_random_1.txt

RQ1: Comparing with Baselines

For SABD and Siamese Pair, we use the implementation in the replication package of Zhang et al.[1].

Reference

[1] Zhang, T., Han, D., Vinayakarao, V., Irsan, I. C., Xu, B., Thung, F., ... & Jiang, L. (2023). Duplicate bug report detection: How far are we?. ACM Transactions on Software Engineering and Methodology, 32(4), 1-32.

RQ2

RQ2.1: Selection Rules

Preparing the data src/handle_data.ipynb

RQ2.2: ChatGPT Alternatives

Comparing with other keyword extraction methods

• Running LLaMA 3 and Phi-3 path: src/run_llm.ipynb

• Running OpenChat Repo: https://github.com/imoneoi/openchat

CUDA_VISIBLE_DEVICES=2 python -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106'

We have deplpyed the API server and inference the model with the requests. There are 13 cases returning <Response [400]>, thus, we kept their original content.

• Running TFIDF path: src/run_tfidf.ipynb

• Running KP-Miner path: src/run_kpminer.ipynb

• Running YAKE path: src/run_yake.ipynb

RQ2.3: Prompt Alternatives

path: src/run_gpt.ipynb