相关论文: Enhanced Differential Testing in Emerging Database…

Database Management System (DBMS) fuzzing is an automated testing technique aimed at detecting errors and vulnerabilities in DBMSs by generating, mutating, and executing test cases. It not only reduces the time and cost of manual testing…

Static bug finders have been widely-adopted by developers to find bugs in real world software projects. They leverage predefined heuristic static analysis rules to scan source code or binary code of a software project, and report violations…

Deep learning (DL) frameworks serve as the backbone for a wide range of artificial intelligence applications. However, bugs within DL frameworks can cascade into critical issues in higher-level applications, jeopardizing reliability and…

Because database systems are the critical component of modern data-intensive applications, it is important to ensure that they operate correctly. To this end, developers extensively test these systems to eliminate bugs that negatively…

With the application of deep learning technology, tools of DL framework testing are in high demand. Existing DL framework testing tools have limited coverage of bug types. For example, they lack the capability of effectively finding…

Spatial Database Management Systems (SDBMSs) aim to store, manipulate, and retrieve spatial data. SDBMSs are employed in various modern applications, such as geographic information systems, computer-aided design tools, and location-based…

Extensible Markup Language (XML) is a widely used file format for data storage and transmission. Many XML processors support XPath, a query language that enables the extraction of elements from XML documents. These systems can be affected…

Distributed databases are fundamental infrastructures of today's large-scale software systems such as cloud systems. Detecting anomalies in distributed databases is essential for maintaining software availability. Existing approaches,…

About 40% of software bug reports are duplicates of one another, which pose a major overhead during software maintenance. Traditional techniques often focus on detecting duplicate bug reports that are textually similar. However, in bug…

By July 2025, smart contracts collectively manage roughly $120 billion in assets. With Solidity remaining the dominant language for smart contract development, the correctness of Solidity compilers has become critically important. However,…

Checker bugs in Deep Learning (DL) libraries are critical yet not well-explored. These bugs are often concealed in the input validation and error-checking code of DL libraries and can lead to silent failures, incorrect results, or…

In database systems, a query plan is a series of concrete internal steps to execute a query. Multiple testing approaches utilize query plans for finding bugs. However, query plans are represented in a database-specific manner, so…

Differential testing can be an effective way to find bugs in software systems with multiple implementations that conform to the same specification, like compilers, network protocol parsers, or language runtimes. Specifications for such…

Ensuring that safety-critical applications behave as intended is an important yet challenging task. Modeling languages like differential dynamic logic (dL) have proof calculi capable of proving guarantees for such applications. However, dL…

This paper considers the problem of reasoning on massive amounts of (possibly distributed) data. Presently, existing proposals show some limitations: {\em (i)} the quantity of data that can be handled contemporarily is limited, due to the…

The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a growth of techniques and tools aiming at improving the quality of ML components and…

Deep Learning (DL) frameworks are a fundamental component of DL development. Therefore, the detection of DL framework defects is important and challenging. As one of the most widely adopted DL testing techniques, model mutation has recently…

Deep-learning (DL) compilers such as TVM and TensorRT are increasingly being used to optimize deep neural network (DNN) models to meet performance, resource utilization and other requirements. Bugs in these compilers can result in models…

Deep learning (DL) techniques are proven effective in many challenging tasks, and become widely-adopted in practice. However, previous work has shown that DL libraries, the basis of building and executing DL models, contain bugs and can…

With the rapid increasing of data scale, in-database analytics and learning has become one of the most studied topics in data science community, because of its significance on reducing the gap between the management and the analytics of…