相关论文: Leveraging Structural Properties of Source Code Gr…

Fixing software faults contributes significantly to the cost of software maintenance and evolution. Techniques for reducing these costs require datasets of software faults, as well as an understanding of the faults, for optimal testing and…

Performance analysis has always been an afterthought during the application development process, focusing on application correctness first. The learning curve of the existing static and dynamic analysis tools are steep, which requires…

The increasing complexity of modern processor and IP designs presents significant challenges in identifying and mitigating hardware flaws early in the IC design cycle. Traditional hardware fuzzing techniques, inspired by software testing,…

Refactoring is an essential activity during software evolution. Frequently, practitioners rely on such transformations to improve source code maintainability and quality. As a consequence, this process may produce new source code entities…

Logging -- used for system events and security breaches to describe more informational yet essential aspects of software features -- is pervasive. Given the high transactionality of today's software, logging effectiveness can be reduced by…

When dealing with time series data, causal inference methods often employ structural vector autoregressive (SVAR) processes to model time-evolving random systems. In this work, we rephrase recursive SVAR processes with possible latent…

The application of large language models (LLMs) to graph data has attracted a lot of attention recently. LLMs allow us to use deep contextual embeddings from pretrained models in text-attributed graphs, where shallow embeddings are often…

Understanding the reasons behind past code changes is critical for many software engineering tasks, including refactoring and reviewing code, diagnosing bugs, and implementing new features. Unfortunately, locating and reconstructing this…

The increasing complexity and volume of software systems have heightened the importance of identifying and mitigating security vulnerabilities. The existing software vulnerability datasets frequently fall short in providing comprehensive,…

Researchers, policy makers, and engineers need to make sense of data on spreading processes as diverse as viral infections, water contamination, and misinformation in social networks. Classical questions include predicting infection…

Scene graph generation (SGG) endeavors to predict visual relationships between pairs of objects within an image. Prevailing SGG methods traditionally assume a one-off learning process for SGG. This conventional paradigm may necessitate…

Open source intelligence is a powerful tool for cybersecurity analysts to gather information both for analysis of discovered vulnerabilities and for detecting novel cybersecurity threats and exploits. However the scale of information that…

Agile teams juggle multiple tasks so professionals are often assigned to multiple projects, especially in service organizations that monitor and maintain a large suite of software for a large user base. If we could predict changes in…

The software development process is characterized by an iterative cycle of continuous functionality implementation and debugging, essential for the enhancement of software quality and adaptability to changing requirements. This process…

For predicting software defects at an early stage, researchers have proposed just-in-time defect prediction (JIT-DP) to identify potential defects in code commits. The prevailing approaches train models to represent code changes in history…

Connection graphs (CGs) extend traditional graph models by coupling network topology with orthogonal transformations, enabling the representation of global geometric consistency. They play a key role in applications such as synchronization,…

Dataflow computing was shown to bring significant benefits to multiple niches of systems engineering and has the potential to become a general-purpose paradigm of choice for data-driven application development. One of the characteristic…

Program source code contains complex structure information, which can be represented in structured data forms like trees or graphs. To acquire the structural information in source code, most existing researches use abstract syntax trees…

Illicit transaction detection is often driven by transaction level attributes however, fraudulent behavior may also manifest through network structure such as central hubs, high flow intermediaries, and coordinated neighborhoods. This paper…

Malformed data-structures can lead to runtime errors such as arbitrary memory access or corruption. Despite this, reasoning over data-structure properties for low-level heap manipulating programs remains challenging. In this paper we…