Jin He

Mingzhe Li, Jianrui Pei, Jin He et al.

In software engineering, log statement is an important part because programmers can't access to users' program and they can only rely on log message to find the root of bugs. The mechanism of "log level" allows developers and users to specify the appropriate amount of logs to print during the execution of the software. And 26\% of the log statement modification is to modify the level. We tried to use ML method to predict the suitable level of log statement. The specific model is GGNN(gated graph neural network) and we have drawn lessons from Microsoft's research. In this work, we apply Graph Neural Networks to predict the usage of log statement level of some open source java projects from github. Given the good performance of GGNN in this task, we are confident that GGNN is an excellent choice for processing source code. We envision this model can play an important role in applying AI/ML technique for Software Development Life Cycle more broadly.

Wenhe Zhang, Jin He, Kevin Song

The recent progress of artificial intelligence(AI) has shown great potentials for alleviating human burden in various complex tasks. From the view of software engineering, AI techniques can be seen in many fundamental aspects of development, such as source code comprehension, in which state-of-the-art models are implemented to extract and express the meaning of code snippets automatically. However, such technologies are still struggling to tackle and comprehend the complex structures within industrial code, thus far from real-world applications. In the present work, we built an innovative and systematical framework, emphasizing the problem of complexity in code comprehension and further software engineering. Upon automatic data collection from the latest Linux kernel source code, we modeled code structures as complex networks through token extraction and relation parsing. Comprehensive analysis of complexity further revealed the density and scale of network-based code representations. Our work constructed the first large-scale dataset from industrial-strength software code for downstream software engineering tasks including code comprehension, and incorporated complex network theory into code-level investigations of software development for the first time. In the longer term, the proposed methodology could play significant roles in the entire software engineering process, powering software design, coding, debugging, testing, and sustaining by redefining and embracing complexity.