Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors

• URL: http://arxiv.org/abs/2106.08415v1
• Date: Tue, 15 Jun 2021 20:13:14 GMT
• Title: Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors
• Authors: Junayed Mahmud, Fahim Faisal, Raihan Islam Arnob, Antonios Anastasopoulos, Kevin Moran
• Abstract summary: Machine translation models are employed to "translate" code snippets into relevant natural language descriptions. Most evaluations of such models are conducted using automatic reference-based metrics. We compare three recently proposed source code summarization models based on the smoothed BLEU-4, METEOR, and ROUGE-L machine translation metrics. Our investigation reveals new insights into the relationship between metric-based performance and model prediction errors grounded in an empirically derived error taxonomy.
• Score: 19.653423881863834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated source code summarization is a popular software engineering research topic wherein machine translation models are employed to "translate" code snippets into relevant natural language descriptions. Most evaluations of such models are conducted using automatic reference-based metrics. However, given the relatively large semantic gap between programming languages and natural language, we argue that this line of research would benefit from a qualitative investigation into the various error modes of current state-of-the-art models. Therefore, in this work, we perform both a quantitative and qualitative comparison of three recently proposed source code summarization models. In our quantitative evaluation, we compare the models based on the smoothed BLEU-4, METEOR, and ROUGE-L machine translation metrics, and in our qualitative evaluation, we perform a manual open-coding of the most common errors committed by the models when compared to ground truth captions. Our investigation reveals new insights into the relationship between metric-based performance and model prediction errors grounded in an empirically derived error taxonomy that can be used to drive future research efforts

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