Analyzing Code Comments to Boost Program Comprehension

Shinyama, Yusuke; Arahori, Yoshitaka; Gondow, Katsuhiko

doi:10.1109/apsec.2018.00047

Cited by 24 publications

(15 citation statements)

References 17 publications

(20 reference statements)

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“…Zhang et al constructed a Python comment taxonomy based on the work of Pascarella et al (Zhang et al 2018). Shinayam et al identified the information embedded in local comments, as shown in Table 5 (Shinyama et al 2018). Mapping to their work showed that Pharo class comments contain low-level information also in addition to high-level information.…”

Section: Comment Information Categorizationmentioning

confidence: 99%

What do class comments tell us? An investigation of comment evolution and practices in Pharo Smalltalk

et al. 2021

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Context Previous studies have characterized code comments in various programming languages, showing how high quality of code comments is crucial to support program comprehension activities, and to improve the effectiveness of maintenance tasks. However, very few studies have focused on understanding developer practices to write comments. None of them has compared such developer practices to the standard comment guidelines to study the extent to which developers follow the guidelines. Objective Therefore, our goal is to investigate developer commenting practices and compare them to the comment guidelines. Method This paper reports the first empirical study investigating commenting practices in Pharo Smalltalk. First, we analyze class comment evolution over seven Pharo versions. Then, we quantitatively and qualitatively investigate the information types embedded in class comments. Finally, we study the adherence of developer commenting practices to the official class comment template over Pharo versions. Results Our results show that there is a rapid increase in class comments in the initial three Pharo versions, while in subsequent versions developers added comments to both new and old classes, thus maintaining a similar code to comment ratio. We furthermore found three times as many information types in class comments as those suggested by the template. However, the information types suggested by the template tend to be present more often than other types of information. Additionally, we find that a substantial proportion of comments follow the writing style of the template in writing these information types, but they are written and formatted in a non-uniform way. Conclusion The results suggest the need to standardize the commenting guidelines for formatting the text, and to provide headers for the different information types to ensure a consistent style and to identify the information easily. Given the importance of high-quality code comments, we draw numerous implications for developers and researchers to improve the support for comment quality assessment tools.

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Section: Comment Information Categorizationmentioning

confidence: 99%

What do class comments tell us? An investigation of comment evolution and practices in Pharo Smalltalk

et al. 2021

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“…Aman et al 17 Classification Automated NLP, Doc2Vec 2018 Shinyama et al 9 Classification Automated Decision tree (C4.5) 2019…”

Section: Heuristic-based Approachesmentioning

confidence: 99%

“…Another approach based on decision trees is presented by Shinyama et al. 9 Shinyama et al identify local source code comments that help programmers to understand nontrivial parts in source code. To achieve this, they built three different classifiers to identify target, category and extent of a comment.…”

Section: Ml-based Approachesmentioning

confidence: 99%

“…To mitigate these problems, we are interested in whether we can replace heuristics by leveraging machine learning (ML) for comment classification for an industrial use case. ML methods were already applied for comment classification of C++, Java, and Python software systems with promising results in References 7‐9. Current practices for classification of source code comments in deep learning (DL) are rare.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging machine learning for software redocumentation—A comprehensive comparison of methods in practice

et al. 2020

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Source code comments contain key information about the underlying software system. Many redocumentation approaches, however, cannot exploit this valuable source of information. This is mainly due to the fact that not all comments have the same goals and target audience and can therefore only be used selectively for redocumentation. Performing a required classification manually, for example, in the form of heuristics, is usually time‐consuming and error‐prone and strongly dependent on programming languages and guidelines of concrete software systems. By leveraging machine learning (ML), it should be possible to classify comments and thus transfer valuable information from the source code into documentation with less effort but the same quality. We applied classical ML techniques but also deep learning (DL) approaches to legacy systems by transferring source code comments into meaningful representations using, for example, word embeddings but also novel approaches using quick response codes or a special character‐to‐image encoding. The results were compared with industry‐strength heuristic classification. As a result, we found that ML outperforms the heuristics in number of errors and less effort, that is, we finally achieve an accuracy of more than 95% for an image‐based DL network and even over 96% for a traditional approach using a random forest classifier.

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“…Liu et al [10] proposed a machine learningbased approach that utilized 64 different features to detect comments that should be updated when the code changes. Shinyama et al [30] proposed eleven distinct categories of code comments and developed a decision-tree based classifier that could identify the explanatory comments. Yu et al [33] propose a code comment quality assessment approach by using the aggregation of the basic classification algorithms.…”

Section: Related Work a Comment Classificationmentioning

confidence: 99%

Deep Code-Comment Understanding and Assessment

et al. 2019

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Code comments are a key software component for program comprehension and software maintainability. High-quality code and comments are urgently needed by data-driven models widely used in tasks like code summarization. Many existing approaches for assessing the quality of comments are machine learning based classification algorithms or rely on heuristic rules. These approaches are difficult to capture the complicated features of text data and are often limited in accuracy, efficiency, and generalization ability. In this paper, we convert the quality assessment of code comments into a classification problem based on the multi-input neural network. We summarize the input, the code and comments, into vectors using the attention-based Bi-LSTM model and the weighted GloVe model, respectively, and concatenate the code vectors and the comment vectors as the input of the Multiple-Layer Perceptron classifier for the comment quality assessment. Experimental results show that our approach, in general, outperforms the previous technique, on both our labeled dataset and the public dataset, with the F1-score of 96.91% and 91.90%, respectively. Using the training set and the testing set from distinct sources, our approach can still achieve reasonable performance, which demonstrates its generalization ability.INDEX TERMS Code comment, source code, multi-input neural network, text classification.

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Analyzing Code Comments to Boost Program Comprehension

Cited by 24 publications

References 17 publications

What do class comments tell us? An investigation of comment evolution and practices in Pharo Smalltalk

What do class comments tell us? An investigation of comment evolution and practices in Pharo Smalltalk

Leveraging machine learning for software redocumentation—A comprehensive comparison of methods in practice

Deep Code-Comment Understanding and Assessment

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