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Deep learning (DL) techniques are gaining more and more attention in thesoftware engineering community. They have been used to support severalcode-related tasks, such as automatic bug fixing and code comments generation.Recent studies in the Natural Language Processing (NLP) field have shown thatthe Text-To-Text Transfer Transformer (T5) architecture can achievestate-of-the-art performance for a variety of NLP tasks. The basic idea behindT5 is to first pre-train a model on a large and generic dataset using aself-supervised task ( e.g: filling masked words in sentences). Once the modelis pre-trained, it is fine-tuned on smaller and specialized datasets, each onerelated to a specific task ( e.g: language translation, sentenceclassification). In this paper, we empirically investigate how the T5 modelperforms when pre-trained and fine-tuned to support code-related tasks. Wepre-train a T5 model on a dataset composed of natural language English text andsource code. Then, we fine-tune such a model by reusing datasets used in fourprevious works that used DL techniques to: (i) fix bugs, (ii) inject codemutants, (iii) generate assert statements, and (iv) generate code comments. Wecompared the performance of this single model with the results reported in thefour original papers proposing DL-based solutions for those four tasks. We showthat our T5 model, exploiting additional data for the self-supervisedpre-training phase, can achieve performance improvements over the fourbaselines.

Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks