Furthermore, the extent to which developers follow these guidelines while writing code comments is unknown. However, it is not clear from the research to date which specific aspects of comments the guidelines cover (e.g., syntax, content, structure). A number of coding style guidelines have been created with the aim to encourage writing of informative, readable, and consistent comments. Demo webpage: Īssessing code comment quality is known to be a difficult problem. Our results show that more than one-third of the rules inferred by NEON are relevant for the proposed task.
![python comment python comment](https://i.ytimg.com/vi/RwPHyaEkF7M/maxresdefault.jpg)
Through a small study involving human subjects with NL processing and parsing expertise, we assess the performance of NEON in identifying rules useful to classify app reviews for software maintenance purposes.
PYTHON COMMENT MANUAL
To reduce such a manual effort, we propose an NL parsing-based tool for software artifacts analysis named NEON that can automate the mining of such rules, minimizing the manual effort of developers and researchers. However, such techniques require the manual identification of patterns to be used for classification purposes. More specifically, Natural Language (NL) parsing techniques have been successfully leveraged to automatically identify (or extract) the relevant information embedded in unstructured software artifacts. In this context, semi-structured and unstructured software artifacts have been leveraged by researchers to build recommender systems aimed at supporting developers in different tasks, such as transforming user feedback in maintenance and evolution tasks, suggesting experts, or generating software documentation. Software developers rely on various repositories and communication channels to exchange relevant information about their ongoing tasks and the status of overall project progress.