Related papers: Testing and Debugging Techniques for Answer Set So…

Software debugging is a time-consuming endeavor involving a series of steps, such as fault localization and patch generation, each requiring thorough analysis and a deep understanding of the underlying logic. While large language models…

Programs expecting structured inputs often consist of both a syntactic analysis stage, which parses raw input, and a semantic analysis stage, which conducts checks on the parsed input and executes the core logic of the program.…

Fault diagnosis is the problem of determining a set of faulty system components that explain discrepancies between observed and expected behavior. Due to the intrinsic relation between observations and sensors placed on a system, sensors'…

Fuzzing -- whether generating or mutating inputs -- has found many bugs and security vulnerabilities in a wide range of domains. Stateful and highly structured web APIs present significant challenges to traditional fuzzing techniques, as…

Due to increasingly complex software design and rapid iterative development, code defects and security vulnerabilities are prevalent in modern software. In response, programmers rely on static analysis tools to regularly scan their…

Real-world programs expecting structured inputs often has a format-parsing stage gating the deeper program space. Neither a mutation-based approach nor a generative approach can provide a solution that is effective and scalable. Large…

The increasing usage of machine learning models raises the question of the reliability of these models. The current practice of testing with limited data is often insufficient. In this paper, we provide a framework for automated test data…

As one of the most successful and effective software testing techniques in recent years, fuzz testing has uncovered numerous bugs and vulnerabilities in modern software, including network protocol software. In contrast to other fuzzing…

Debugging ML software (i.e., the detection, localization and fixing of faults) poses unique challenges compared to traditional software largely due to the probabilistic nature and heterogeneity of its development process. Various methods…

Recently efficient model-checking tools have been developed to find flaws in security protocols specifications. These flaws can be interpreted as potential attacks scenarios but the feasability of these scenarios need to be confirmed at the…

Fuzzing technologies have evolved at a fast pace in recent years, revealing bugs in programs with ever increasing depth and speed. Applications working with complex formats are however more difficult to take on, as inputs need to meet…

Monolithic Firmware is widespread. Unsurprisingly, fuzz testing firmware is an active research field with new advances addressing the unique challenges in the domain. However, understanding and evaluating improvements by deriving metrics…

Contemporary fuzz testing techniques focus on identifying memory corruption vulnerabilities that allow adversaries to achieve either remote code execution or information disclosure. Meanwhile, Algorithmic Complexity (AC)vulnerabilities,…

Deep Learning (DL) libraries such as PyTorch provide the core components to build major AI-enabled applications. Finding bugs in these libraries is important and challenging. Prior approaches have tackled this by performing either API-level…

Hardware Fuzzing emerged as one of the crucial techniques for finding security flaws in modern hardware designs by testing a wide range of input scenarios. One of the main challenges is creating high-quality input seeds that maximize…

Mutation testing can help minimize the delivery of faulty software. Therefore, it is a recommended practice for developing embedded software in safety-critical cyber-physical systems (CPS). However, state-of-the-art mutation testing…

Mutation testing consists of generating test cases that detect faults injected into software (generating mutants) which its original test suite could not. By running such an augmented set of test cases, it may discover actual faults that…

Fully test-time adaptation aims at adapting a pre-trained model to the test stream during real-time inference, which is urgently required when the test distribution differs from the training distribution. Several efforts have been devoted…

We propose Answer Set Programming (ASP) as an approach for modeling and solving problems from the area of Declarative Process Mining (DPM). We consider here three classical problems, namely, Log Generation, Conformance Checking, and Query…

Deep Learning (DL) library bugs affect downstream DL applications, emphasizing the need for reliable systems. Generating valid input programs for fuzzing DL libraries is challenging due to the need for satisfying both language…