Does your code need comment?

Huang, Yuan; Jia, Nan; Shu, Junhuai; Hu, Xinyu; Chen, Xiangping; Zhou, Qiang

doi:10.1002/spe.2772

Cited by 13 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From Figure 7, we can see that BW‐Avg‐comments is the only feature that increases code readability. Comments are usually used as a direct way to convey intention 1,42 . People can understand the purpose of the code more easily through comments.…”

Section: Resultsmentioning

confidence: 99%

“…Comments are usually used as a direct way to convey intention. 1,42 People can understand the purpose of the code more easily through comments. Our results confirm that comments could improve code readability, which is consistent with existing studies.…”

Section: F I G U R Ementioning

confidence: 99%

“…49 In fact, overly commented code would make it harder to read and understand. As to the necessity of commenting, Huang et al 42 proposed a commenting suggestion approach to determine whether a method requires a header comment. Accordingly, the caveat is that our findings cannot be regarded as practical rules to ensure code readability.…”

Section: Implications For Practitionersmentioning

confidence: 99%

See 2 more Smart Citations

What makes a readable code? A causal analysis method

Chen

Cai

et al. 2023

Softw Pract Exp

View full text Add to dashboard Cite

Context Code readability is one of the most important quality attributes for software source code. To investigate which features affect code readability, most existing studies rely on correlation‐based methods. However, spurious correlations (a mathematical relationship wherein two variables appear to be causal but are not) involved in correlation‐based methods may affect research conclusions. Objective In order to remove spurious correlations and obtain conclusions from the perspective of causation as to what makes a readable code, we propose a causal theory‐based approach to analyze the relationship between code features and code readability scores. Method First, we adopt the PC algorithm and additive noise models to construct the causal graph on the basis of the selected code features. Then, we use the linear regression algorithm based on the back‐door criterion to obtain the causal effect of different features on code readability. Result We conduct a set of experiments on readability data labeled by human annotators. The experimental results show that the average number of comments positively impacts code readability, with each additional unit increasing the code readability score by 0.799 points. Whereas the average number of assignments, identifiers, and periods have a negative impact, with each additional unit decreasing the code readability score by 0.528, 0.281, and 0.170 points respectively. Conclusion We believe that our findings will provide developers with a better understanding of the patterns behind code readability, and guide developers to optimize their code as the ultimate goal.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: F I G U R Ementioning

confidence: 99%

Section: Implications For Practitionersmentioning

confidence: 99%

See 1 more Smart Citation

What makes a readable code? A causal analysis method

Chen

Cai

et al. 2023

Softw Pract Exp

View full text Add to dashboard Cite

show abstract

“…The remaining 4 articles (VISWANATHAN; KUMAR; SOMAN, 2019) (SHALABY et al, 2017) (GONÇALES et al, 2020) (HUANG et al, 2020 were removed by the exclusion criteria; they corresponded to articles that make use of machine learning to understand the source code.…”

Section: Study Selectionmentioning

confidence: 99%

Investigating the Impact of Solid Design Principles on Machine Learning Code Understanding

OLIVEIRA CABRAL

View full text Add to dashboard Cite

show abstract

Are your comments outdated? Toward automatically detecting code‐comment consistency

Huang,

Chen,

Chen

et al. 2024

J Software Evolu Process

View full text Add to dashboard Cite

In software development and maintenance, code comments can help developers understand source code and improve communication among developers. However, developers sometimes neglect to update the corresponding comment when changing the code, resulting in outdated comments (i.e., inconsistent codes and comments). Outdated comments are dangerous and harmful and may mislead subsequent developers. More seriously, the outdated comments may lead to a fatal flaw sometime in the future. To automatically identify the outdated comments in source code, we proposed a learning‐based method, called CoCC, to detect the consistency between code and comment. To efficiently identify outdated comments, we extract multiple features from both codes and comments before and after they change. Besides, we also consider the relation between code and comment in our model. Experiment results show that CoCC can effectively detect outdated comments with precision over 90%. In addition, we have identified the 15 most important factors that cause outdated comments and verified the applicability of CoCC in different programming languages. We also used CoCC to find outdated comments in the latest commits of open source projects, which further proves the effectiveness of the proposed method.

show abstract

Does your code need comment?

Cited by 13 publications

References 49 publications

What makes a readable code? A causal analysis method

What makes a readable code? A causal analysis method

Investigating the Impact of Solid Design Principles on Machine Learning Code Understanding

Are your comments outdated? Toward automatically detecting code‐comment consistency

Contact Info

Product

Resources

About