A comparison and evaluation of variants in the coupling between objects metric

Child, Mike; Rosner, Peter; Counsell, Steve

doi:10.1016/j.jss.2019.02.020

Cited by 4 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the Coupling Between Objects metric (CBO) metric is a long-established and widely used metric for code smell detection in object-oriented languages (Azeem et al, 2019) that measures the extent of coupling between two classes. However, the coupling can be measured and interpreted in many ways (Briand et al, 1999;Child et al, 2019). Consequently, such ambiguities in metric definitions led to different metrics tools producing widely inconsistent results even for wellknown metrics (Lincke et al, 2008;Sharma & Spinellis, 2018).…”

Section: Heuristic-based Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic detection of Long Method and God Class code smells through neural source code embeddings

Kovačević¹,

Slivka²,

Vidaković³

et al. 2021

Preprint

View full text Add to dashboard Cite

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. 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). 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. 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.

show abstract

Section: Heuristic-based Detectionmentioning

confidence: 99%

“…Furthermore, the metric tools' documentations often quote a standard definition of the calculated metrics, with scant detail on how they handle ambiguities in their implementations (Child et al, 2019). Similarly, some studies proposing a metric-based smell detection approach lack precise definitions of the metrics they use.…”

Section: Heuristic-based Detectionmentioning

confidence: 99%