In this study, we present CoDesc -- a large parallel dataset composed of 4. 2 million Java methods and natural language descriptions.
Ranked #1 on Code Search on CoDesc
In this work, we leverage the efficacy of these embedding models using a simple, lightweight 2-layer neural network in the task of semantic code search.
As a bi-product of the standard NLU benchmarks, we introduce a new downstream dataset on natural language inference (NLI) and show that BanglaBERT outperforms previous state-of-the-art results on all tasks by up to 3. 5%.
As the choice of words and syntax vary while preparing a textual description, it is challenging for the system to reliably produce a consistently desirable output from different forms of language input.
In our empirical study on the 146, 612 code changes from the three software projects, we find that (1) The new features like reviewer dimensions that are introduced in PredCR are the most informative.
In this research, we conducted a benchmark study to assess the performance of different applicable machine learning approaches on three different datasets where we accumulated the largest and most diversified one.