arXiv subjects

Huan Ji

Publications and source records attributed to Huan Ji.

4 recordsLinked to original sources

arXiv2024

Depends-Kotlin: A Cross-Language Kotlin Dependency Extractor

Since Google introduced Kotlin as an official programming language for developing Android apps in 2017, Kotlin has gained widespread adoption in Android development. However, compared to Java, there is limited support for Kotlin code dependency analysis, which is the foundation to software analysis. To bridge this gap, we develop Depends-Kotlin to extract entities and their dependencies in Kotlin source code. Not only does Depends-Kotlin support extracting entities' dependencies in Kotlin code, but it can also extract dependency relations between Kotlin and Java. Using three open-source Kotlin-Java mixing projects as our subjects, Depends-Kotlin demonstrates high accuracy and performance in resolving Kotlin-Kotlin and Kotlin-Java dependencies relations. The source code of Depends-Kotlin and the dataset used have been made available at https: //github.com/XYZboom/depends-kotlin. We also provide a screen-cast presenting Depends-Kotlin at https://youtu.be/ZPq8SRhgXzM.

cs.SE↗

arXiv2024

An Empirical Study of Kotlin-Java Cross-Dependency Issues and Their Detection

Since Google introduced Kotlin as an official programming language for developing Android apps in 2017, Kotlin has gained widespread adoption in Android development. The interoperability of Java and Kotlin's design nature allows them to coexist and interact with each other smoothly within a project. However, there is limited research on how Java and Kotlin interact with each other in real-world projects and what challenges are faced during these interactions. The answers to these questions are key to understanding these kinds of cross-language software systems. In this paper, we implemented a tool; named DependExtractor, which can extract 11 types and 9 types of Kotlin-Java and Java-Kotlin dependencies, and conducted an empirical study of 40 Kotlin-Java real-world projects with 27,427 Java and 36,141 Kotlin source files. Our findings revealed that Java and Kotlin frequently interact with each other in these cross-language projects, with Access and Call dependency types being the most dominant. Compared to files interacting with other files in the same language, Java/Kotlin source files, which participate in the cross-language interactions, experience more commits and exhibit a higher defect rate. Additionally, among all Kotlin-Java problematic interactions, we identified ten common issues, along with their fixing strategies. Furthermore, we implemented a tool called InteropScan to proactively detect these issues from Java and Kotlin source code. The findings of this study can help developers understand and address the challenges in Kotlin-Java projects.

cs.SE↗

arXiv2024

Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data

In recent years, the rapid development of high-precision map technology combined with artificial intelligence has ushered in a new development opportunity in the field of intelligent vehicles. High-precision map technology is an important guarantee for intelligent vehicles to achieve autonomous driving. However, due to the lack of research on high-precision map technology, it is difficult to rationally use this technology in the field of intelligent vehicles. Therefore, relevant researchers studied a fast and effective algorithm to generate high-precision GPS data from a large number of low-precision GPS trajectory data fusion, and generated several key data points to simplify the description of GPS trajectory, and realized the "crowdsourced update" model based on a large number of social vehicles for map data collection came into being. This kind of algorithm has the important significance to improve the data accuracy, reduce the measurement cost and reduce the data storage space. On this basis, this paper analyzes the implementation form of crowdsourcing map, so as to improve the various information data in the high-precision map according to the actual situation, and promote the high-precision map can be reasonably applied to the intelligent car.

cs.AI↗

arXiv2023

An Empirical Study of Untangling Patterns of Two-Class Dependency Cycles

Dependency cycles pose a significant challenge to software quality and maintainability. However, there is limited understanding of how practitioners resolve dependency cycles in real-world scenarios. This paper presents an empirical study investigating the recurring patterns employed by software developers to resolve dependency cycles between two classes in practice. We analyzed the data from 38 open-source projects across different domains and manually inspected hundreds of cycle untangling cases. Our findings reveal that developers tend to employ five recurring patterns to address dependency cycles. The chosen patterns are not only determined by dependency relations between cyclic classes, but also highly related to their design context, i.e., how cyclic classes depend on or are depended by their neighbor classes. Through this empirical study, we also discovered three common counterintuitive solutions developers usually adopted during cycles' handling. These recurring patterns and common counterintuitive solutions observed in dependency cycles' practice can serve as a taxonomy to improve developers' awareness and also be used as learning materials for students in software engineering and inexperienced developers. Our results also suggest that, in addition to considering the internal structure of dependency cycles, automatic tools need to consider the design context of cycles to provide better support for refactoring dependency cycles.

cs.SE↗