Papers › Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive...
Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation
Gabriel Orlanski, Alex Gittens
Answering a programming question using only its title is difficult as salient contextual information is omitted. Based on this observation, we present a corpus of over 40,000 StackOverflow question texts to be used in conjunction with their corresponding intents from the CoNaLa dataset (Yin et al., 2018). Using both the intent and question body, we use BART to establish a baseline BLEU score of 34.35 for this new task. We find further improvements of 2.8% by combining the mined CoNaLa data with the labeled data to achieve a 35.32 BLEU score. We evaluate prior state-of-the-art CoNaLa models with this additional data and find that our proposed method of using the body and mined data beats the BLEU score of the prior state-of-the-art by 71.96%. Finally, we perform ablations to demonstrate that BART is an unsupervised multimodal learner and examine its extractive behavior. The code and data can be found https://github.com/gabeorlanski/stackoverflow-encourages-cheating.
Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Code Generation | CoNaLa | BART W/ Mined | BLEU | 30.55 | #9 of 14 | Archive leaderboard | report |
| Code Generation | CoNaLa | BART Base | BLEU | 26.24 | #13 of 14 | Archive leaderboard | report |
| Code Generation | CoNaLa-Ext | BART W/ Mined | BLEU | 35.32 | #1 of 6 | Archive leaderboard | report |
| Code Generation | CoNaLa-Ext | BART Base | BLEU | 34.35 | #2 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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