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.

WebAssembly (Wasm) is a binary instruction format designed for secure and efficient execution within sandboxed environments - predominantly web apps and browsers - to facilitate performance, security, and flexibility of web programming languages. In recent years, Wasm has gained significant attention from the academic research community and industrial development projects to engineer high-performance web applications. Despite the offered benefits, developers encounter a multitude of issues rooted in Wasm (e.g., faults, errors, failures) and are often unaware of their root causes that impact the development of web applications. To this end, we conducted an empirical study that mines and documents practitioners’ knowledge expressed as 385 issues from 12 open-source Wasm projects deployed on GitHub and 354 question-answer posts via Stack Overflow. Overall, we identified 120 types of issues, which were categorized into 19 subcategories and 9 categories to create a taxonomical classification of issues encountered in Wasm-based applications. Furthermore, root cause analysis of the issues helped us identify 278 types of causes, which have been categorized into 29 subcategories and 10 categories as a taxonomy of causes. Our study led to first-of-its-kind taxonomies of the issues faced by developers and their underlying causes in Wasm-based applications. The issue-cause taxonomies - identified from GitHub and SO, offering empirically derived guidelines - can guide researchers and practitioners to design, develop, and refactor Wasm-based applications.

In this article, we provide the first systematic literature review of the intersection of two research areas: Multi-Label Learning (MLL) and Software Engineering (SE). We refer to this intersection as MLL4SE. In recent years, MLL problems have increased in many applications and research areas because real-world datasets often have a multi-label nature. For multi-label data, simplifying the assumption of traditional classification approaches that an instance can only be associated with one class only leads to worse accuracy. Thus, a better match of methods and assumptions about the data is required. We identified 50 primary studies in our systematic literature review in the MLL4SE domain. Based on this review, we identified six main SE application domains where MLL has been applied. These domains include Software Requirement Engineering, Issue Tracking and Management, Community and Knowledge Management, API Usage and Management, Code Quality and Maintenance, and Mobile Application Development . We summarized the methods used and the data nature of the MLL4SE applications. Moreover, we separately provide taxonomies of future work directions from machine learning and SE perspectives. In general, we highlight current trends, research gaps, and shortcomings.