Classifying code comments in Java mobile applications

Pascarella, Luca

doi:10.1145/3197231.3198444

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…By using a Random Forest classifier and lemmatization, they were able to achieve a classification precision of 90%. In another study, L. Pascarella [36] compared the performance of two machine learning models to automatically classify code comments in five open-source mobile applications. Their aim was to assess code comments produced by professional developers.…”

Section: Type Totalmentioning

confidence: 99%

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

et al. 2023

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Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categorized those comments using a machine learning approach. The work involves the initial manual classification of code comments and then building a machine learning model to classify student code comments automatically. The findings of our study revealed that novice developers/students’ comments are mainly related to Literal (26.66%) and Insufficient (26.66%). Further, we proposed and extended the taxonomy of such source code comments by adding a few more categories, i.e., License (5.18%), Profile (4.80%), Irrelevant (4.80%), Commented Code (4.44%), Autogenerated (1.48%), and Improper (1.10%). Moreover, we assessed our approach with three different machine-learning classifiers. Our implementation of machine learning models found that Decision Tree resulted in the overall highest accuracy, i.e., 85%. This study helps in predicting the type of code comments for a novice developer using a machine learning approach that can be implemented to generate automated feedback for students, thus saving teachers time for manual one-on-one feedback, which is a time-consuming activity.

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Section: Type Totalmentioning

confidence: 99%

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

et al. 2023

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“…A text-mining based approach to SATD detection can be found in [38], [39]; in this work, unfortunately, only a subset of the dataset was used and results are thus not comparable. In a closely related comment classification work, Pascarella [40] focus on comment classification in mobile applications.…”

Section: Related Workmentioning

confidence: 99%

Making the most of small Software Engineering datasets with modern machine learning

Prenner¹,

Robbes²

2021

Preprint

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This paper provides a starting point for Software Engineering (SE) researchers and practitioners faced with the problem of training machine learning models on small datasets. Due to the high costs associated with labeling data, in Software Engineering, there exist many small (< 1 000 samples) and medium-sized (< 100 000 samples) datasets. While deep learning has set the state of the art in many machine learning tasks, it is only recently that it has proven effective on small-sized datasets, primarily thanks to pre-training, a semi-supervised learning technique that leverages abundant unlabelled data alongside scarce labelled data. In this work, we evaluate pre-trained Transformer models on a selection of 13 smaller datasets from the SE literature, covering both, source code and natural language. Our results suggest that pre-trained Transformers are competitive and in some cases superior to previous models, especially for tasks involving natural language; whereas for source code tasks, in particular for very small datasets, traditional machine learning methods often has the edge. In addition, we experiment with several techniques that ought to aid training on small datasets, including active learning, data augmentation, soft labels, self-training and intermediate-task fine-tuning, and issue recommendations on when they are effective. We also release all the data, scripts, and most importantly pre-trained models for the community to reuse on their own datasets.

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“…In the rapidly evolving field of software development, efficient code commenting plays a vital role in improving code readability, maintainability, and collaboration among coders [1]. Conventionally, code commenting was a manual and time-consuming process, which requires coders to carefully mark lines of code with explanations.…”

Section: Introductionmentioning

confidence: 99%

Automatic Code Commenting in Integrated Development Environments Based on Indirect Interaction with Chatbots

Fabiyi,

Ajibuwa

2023

2023 International Scientific Conference on Computer Science (COMSCI)

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Classifying code comments in Java mobile applications

Cited by 8 publications

References 8 publications

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

Making the most of small Software Engineering datasets with modern machine learning

Automatic Code Commenting in Integrated Development Environments Based on Indirect Interaction with Chatbots

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