  Code comments play a crucial role in software development, as they provide
programmers with practical information, allowing them to understand better the
intent and semantics of the underpinning code. Nevertheless, developers tend to
leave comments unchanged after updating the code, resulting in a discrepancy
between the two artifacts. Such a discrepancy may trigger misunderstanding and
confusion among developers, impeding various activities, including code
comprehension and maintenance. Thus, it is crucial to identify if, given a code
snippet, its corresponding comment is coherent and reflects well the intent
behind the code. Unfortunately, existing approaches to this problem, while
obtaining an encouraging performance, either rely on heavily pre-trained
models, or treat input data as text, neglecting the intrinsic features
contained in comments and code, including word order and synonyms. This work
presents Co3D as a practical approach to the detection of code comment
coherence. We pay attention to internal meaning of words and sequential order
of words in text while predicting coherence in code-comment pairs. We deployed
a combination of Gensim word2vec encoding and a simple recurrent neural
network, a combination of Gensim word2vec encoding and an LSTM model, and
CodeBERT. The experimental results show that Co3D obtains a promising
prediction performance, thus outperforming well-established baselines. We
conclude that depending on the context, using a simple architecture can
introduce a satisfying prediction.
