Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning
Table 1. Code Retrieval Results of CO3 and Baselines. We appiyed T-test on the results between Our Model and baselines, and all p-values are smaller than 0.05, which means that the improvement is stable and statistically significant.
['Model', 'SQL MRR', 'SQL NDCG', 'Python MRR', 'Python NDCG']
is a state-of-the-art model proposed recently. Since CoaCor uses ranking metrics for retrieval as reward to producing retrieval-friendly code summary and further assembles two diverse retrieval models, CoaCor outperforms DCS by a large margin. Despite the simplicity of CO3, it outperforms CoaCor by nearly 0.01 for SQL in terms of both MRR and NDGG. The performance margin between CoaCor and CO3 is even larger for Python, which speaks to the superiority of CO3.