Deep Reinforcement Learning (DRL) is a paradigm of artificial intelligence where an agent uses a neural network to learn which actions to take in a given environment. DRL has recently gained traction from being able to solve complex environments like …
Many software testing papers claim to discover bugs but without providing sufficient evidence. My analysis of 39 papers from top conferences revealed that only 51% provide complete bug identifiers and 28% have inaccessible artifacts. Their …
Relational DBMSs (RDBMSs) are ubiquitous, so any bugs or inconsistencies within RDBMSs are highly consequential. Particularly, logic bugs, which can cause an incorrect result to be returned for a given query evaluation, are critical because they are …
Service-level objectives (SLOs), as network performance requirements for delay and packet loss typically, should be guaranteed for increasing high-performance applications, e.g., telesurgery and cloud gaming. However, SLO violations are common and …
Learned database components, which deeply integrate machine learning into their design, have been extensively studied in recent years. Given the dynamism of databases, where data and workloads continuously drift, it is crucial for learned database …
Rust aims to offer full memory safety for programs, a guarantee that untamed C programs do not enjoy. How difficult is it to translate existing C code to Rust? To get a complementary view from that of automatic C to Rust translators, we report on a …
The PHP interpreter, powering over 70% of websites on the internet, plays a crucial role in web development. Existing approaches to finding bugs in PHP primarily focus on detecting explicit security issues through crashes or sanitizer-based oracles, …
PHP, a dominant scripting language in web development, powers a vast range of websites, from personal blogs to major platforms. While existing research primarily focuses on PHP application-level security issues like code injection, memory errors …
Query engines are the cornerstone of any relational databases, including query optimizers and query executors. It is imperative for database developers to be equipped with a tool to detect the query execution bug and evaluate the query optimizer …
Training Large Language Models (LLMs) is often inefficient due to high communication overhead, resulting in sub-50% Model FLOPS Utilization (MFU). In this talk, I will discuss how to build a cost-efficient and scalable machine learning system, using …