2,847 papers · page 32 of 143
Code completion has become a common practice for programmers during their daily programming activities. It automatically predicts the next tokens or statements that the programmers may use. Code completion aims to substantially save keystrokes and improve the programming efficien…
Modern software is bloated, especially for libraries. The unnecessary code not only brings severe vulnerabilities, but also assists attackers to construct exploits. To mitigate the damage of bloated libraries, researchers have proposed several debloating techniques to remove or r…
Resource leaks, which are caused by acquired resources not being released, often result in performance degradation and system crashes. Resource leak detection relies on two essential components: identifying potential Resource Acquisition and Release (RAR) API pairs, and subsequen…
Mobile games play an increasingly important role in our daily life. The quality of mobile games can substantially affect the user experience and game revenue. Different from traditional mobile games, the mini-games provided by our partner, Tencent, are embedded in the mobile app …
Automated code translation between programming languages can greatly reduce the human effort needed in learning new languages or in migrating code. Recent neural machine translation models, such as Codex, have been shown to be effective on many code generation tasks including tra…
Code review is a crucial step in ensuring the quality and maintainability of software systems. However, this process can be time-consuming and resource-intensive, especially in large-scale projects where a significant number of code changes are submitted every day. Fortunately, n…
A Machine Learning (ML) pipeline configures the workflow of a learning task using the APIs provided by ML libraries. However, a pipeline’s performance can vary significantly across different configurations of ML library versions. Misconfigured pipelines can result in inferior per…
Deep learning (DL) models are trained on sampled data, where the distribution of training data differs from that of real-world data (i.e., the distribution shift), which reduces the model's robustness. Various testing techniques have been proposed, including distribution-unaware …
Deep learning (DL) systems are complex component-based systems, which consist of core program (code implementation and data), Python (language and interpreter), third-party libraries, low-level libraries, development tools, OS, and hardware environments. Incompatible interaction …
There is a growing interest in the computer architecture community to incorporate heterogeneity and specialization to improve performance. Developers can create heterogeneous applications that consist of both host code and kernel code, where compute-intensive kernels can be offlo…
A risk in adopting third-party dependencies into an application is their potential to serve as a doorway for malicious code to be injected (most often unknowingly). While many initiatives from both industry and research communities focus on the most critical dependencies (i.e., t…
ML-powered code generation aims to assist developers to write code in a more productive manner by intelligently generating code blocks based on natural language prompts. Recently, large pretrained deep learning models have pushed the boundary of code generation and achieved impre…
Parallelization is a promising way to improve the performance of Python programs. Unfortunately, developers may miss parallelization possibilities, because they usually do not concentrate on parallelization. Many approaches have been proposed to parallelize Python programs automa…
Pre-trained language models (PLMs) have become a prevalent technique in deep learning for code, utilizing a two-stage pre-training and fine-tuning procedure to acquire general knowledge about code and specialize in a variety of downstream tasks. However, the dynamic nature of sof…
Given the rapid growth of Open-Source Software (OSS) projects, ethical considerations are becoming more important. Past studies focused on specific ethical issues (e.g., gender bias and fairness in OSS). There is little to no study on the different types of unethical behavior in …
For coverage-guided fuzzers, many of their adopted seeds are usually underused by exploring limited program states since essentially all their executions have to abide by rigorous program dependencies while only limited seeds are capable of accessing dependencies. Moreover, even …
Learning-based approaches that learn code representations for software vulnerability detection have been proven to produce inspiring results. However, they still fail to capture complete and precise vulnerability semantics for code representations. To address the limitations, in …
While the Java Virtual Machine (JVM) plays a vital role in ensuring correct executions of Java applications, testing JVMs via generating and running class files on them can be rather challenging. The existing techniques, e.g., ClassFuzz and Classming, attempt to leverage the powe…
Although the open source model bears many advantages in software development, open source projects are always hard to sustain. Previous research on open source sustainability mainly focuses on projects that have already reached a certain level of maturity (e.g., with communities,…
As Internet applications continue to scale up, microservice architecture has become increasingly popular due to its flexibility and logical structure. Anomaly detection in traces that record inter-microservice invocations is essential for diagnosing system failures. Deep learning…