System interaction is a fundamental aspect of software development. It involves direct engagement between developers and operating systems, covering tasks such as file management, permission handling, environment dependencies, and parallel development. Accurate system interaction can boost software performance and enhance user experience. On the other hand, improper use often leads to software issues, impacting reliability and stability. Meanwhile, most system interaction bugs typically involve only a minor size of code and follow similar fix patterns. In this paper, we designed a technique to uncover common fix patterns for system interaction bugs. The technique converts bug-fixing behaviors into edit actions, then establishes feature vectors to complete their clustering. Based on this, we present a large-scale study on over 7,800 commits from 37 real Github repositories. We analyzed the results and summarized 19 common fix patterns across 9 categories. Further, we discuss the implications that can support related development, testing, and improvements. These findings will contribute to understanding the essence of system interaction bugs and provide insights for future studies.

Python programming language has gained enor-mous popularity in the past decades. While its flexibility signifi-cantly improves software development productivity, the dynamic typing feature challenges software maintenance and quality assurance. To facilitate programming and type error checking, the Python programming language has provided a type hint mechanism enabling developers to annotate type information for variables. However, this manual annotation process often requires plenty of resources and may introduce errors. In this paper, we propose a deep learning type inference technique, namely DLInfer, to automatically infer the type infor-mation for Python programs. DLInfer collects slice statements for variables through static analysis and then vectorizes them with the Unigram Language Model algorithm. Based on the vectorized slicing features, we designed a bi-directional gated recurrent unit model to learn the type propagation information for inference. To validate the effectiveness of DLInfer, we conduct an extensive empirical study on 700 open-source projects. We evaluate its accuracy in inferring three kinds of fundamental types, including built-in, library, and user-defined types. By training with a large-scale dataset, DLInfer achieves an average of 98.79% Top-1 accuracy for the variables that can get type information through static analysis and manual annotation. Further, DLInfer achieves 83.03% type inference accuracy on average for the variables that can only obtain the type information through dynamic analysis. The results indicate DLInfer is highly effective in inferring types. It is promising to apply it to assist in various software engineering tasks for Python programs.

Python is a popular typical dynamic programming language. In Python, dynamic typing is one of the most critical dynamic features. The lack of type information is likely to hinder the maintenance of Python projects. However, existing work has seldom focused on studying the impact of Python dynamic typing on project maintenance. This paper focuses on the two most common practices of Python dynamic typing, i.e. inconsistent-type assignments (ITA) and inconsistent variable types (IVT). Two approaches are proposed to identify ITA and IVT, i.e. identifying ITA by analyzing Abstract Syntax Trees and comparing identifiers types and identifying IVT by constructing a type dependency graph. In empirical experiments, we first locate the usage of ITA and IVT in 10 open-source Python projects. Then, we investigate the relations between the occurrence of ITA and IVT and the results of maintenance tasks. The study results show that projects are more prone to change as the number of dynamic typing identifiers increases. There is a weak connection between change-proneness and variable dynamic typing. There is a high probability that maintenance time and the acceptance of commits decrease as dynamic typing identifiers increase in projects. These results implicate that dynamic and static variables should be divided while developing new programming languages. Dynamic typing identifiers may not be the direct root causes for most software bugs. The categories of these bugs are worth exploring.