<p>Code smells are structures in code that often have a
negative impact on its quality. Manually detecting code smells is challenging
and researchers proposed many automatic code smell detectors. Most of the
studies propose detectors based on code metrics and heuristics. However, these
studies have several limitations, including evaluating the detectors using
small-scale case studies and an inconsistent experimental setting. Furthermore,
heuristic-based detectors suffer from limitations that hinder their adoption in
practice. Thus, researchers have recently started experimenting with machine
learning (ML) based code smell detection. </p><p>This paper compares the performance of multiple ML-based
code smell detection models against multiple traditionally employed
metric-based heuristics for detection of God Class and Long Method code smells.
We evaluate the effectiveness of different source code representations for
machine learning: traditionally used code metrics and code embeddings
(code2vec, code2seq, and CuBERT).<br></p><p>We perform our experiments on the large-scale, manually
labeled MLCQ dataset. We consider the binary classification problem – we
classify the code samples as smelly or non-smelly and use the F1-measure of the
minority (smell) class as a measure of performance. In our experiments, the ML
classifier trained using CuBERT source code embeddings achieved the best
performance for both God Class (F-measure of 0.53) and Long Method detection
(F-measure of 0.75). With the help of a domain expert, we perform the error
analysis to discuss the advantages of the CuBERT approach.<br></p><p>This study is the first to evaluate the effectiveness of
pre-trained neural source code embeddings for code smell detection to the best
of our knowledge. A secondary contribution of our study is the systematic
evaluation of the effectiveness of multiple heuristic-based approaches on the same
large-scale, manually labeled MLCQ dataset.<br></p>
Audio Mining: The Role of Vocal Tone in Persuasion
