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Library Testing

  • A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs, (ICSE2025)

    • Abstract: As modern web services increasingly rely on REST APIs, their thorough testing has become crucial. Furthermore, the advent of REST API documentation languages, such as the OpenAPI Specification, has led to the emergence of many black-box REST API testing tools. However, these tools often focus on individual test elements in isolation (e.g., APIs, parameters, values), resulting in lower coverage and less effectiveness in fault detection. To address these limitations, we present AutoRestTest, the f...
    • Labels: program testing, library testing

  • CrossProbe: LLM-Empowered Cross-Project Bug Detection for Deep Learning Frameworks, (ISSTA2025)

    • Abstract: Deep Learning (DL) models may introduce reliability challenges in the underlying DL frameworks. These frameworks may be prone to bugs that can lead to crash or wrong results, particularly when involving complex model architectures and substantial computational demands. Such framework bugs can disrupt DL applications, impacting customer experience and potentially causing financial losses. Traditional approaches to testing DL frameworks face limitations in adapting to the vast search space of mode...
    • Labels: program testing, library testing

  • LLM Based Input Space Partitioning Testing for Library APIs, (ICSE2025)

    • Abstract: Automated library APIs testing is difficult as it requires exploring a vast space of parameter inputs that may involve objects with complex data types. Existing search based approaches, with limited knowledge of relations between object states and program branches, often suffer from the low efficiency issue, i.e., tending to generate invalid inputs. Symbolic execution based approaches can effectively identify such relations, but fail to scale to large programs. In this work, we present an LLM-ba...
    • Labels: program testing, library testing

  • Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models, (ISSTA2023)

    • Abstract: Deep Learning (DL) systems have received exponential growth in popularity and have become ubiquitous in our everyday life. Such systems are built on top of popular DL libraries, e.g., TensorFlow and PyTorch which provide APIs as building blocks for DL systems. Detecting bugs in these DL libraries is critical for almost all downstream DL systems in ensuring effectiveness/safety for end users. Meanwhile, traditional fuzzing techniques can be hardly effective for such a challenging domain since the...
    • Labels: program testing, fuzzing, library testing

  • LlamaRestTest: Effective REST API Testing with Small Language Models, (FSE2025)

    • Abstract: Modern web services rely heavily on REST APIs, typically documented using the OpenAPI specification. The widespread adoption of this standard has resulted in the development of many black-box testing tools that generate tests based on OpenAPI specifications. Although Large Language Models (LLMs) have shown promising test-generation abilities, their application to REST API testing remains mostly unexplored. We present LlamaRestTest, a novel approach that employs two custom LLMs-created by fine-tu...
    • Labels: program testing, library testing