software testing

Abstract: Large language models and deep learning models designed for code intelligence have revolutionized the software engineering field due to their ability to perform various code-related tasks. These models can process source code and software artifacts with high accuracy in tasks such as code completion, defect detection, and code summarization; therefore, they can potentially become an integral part of modern software engineering practices.

Resolving Python dependency issues remains a tedious and error-prone process, forcing developers to manually trial compatible module …

Testing deep reinforcement learning (DRL) agents in safety-critical domains requires discovering diverse failure scenarios. Existing …

Abstract The rising popularity of the microservice architectural style has led to a growing demand for automated testing approaches tailored to these systems. EvoMaster is a state-of-the-art tool that uses Evolutionary Algorithms (EAs) to automatically generate test cases for microservices’ REST APIs.

Abstract DRVN is a regression testing tool that aims to diversify the test scenarios (road maps) to execute for testing and validating self-driving cars. DRVN harnesses the power of convolutional neural networks to identify possible failing roads in a set of generated examples before applying a greedy algorithm that selects and prioritizes the most diverse roads during regression testing.

Abstract Byzantine fault tolerant algorithms are critical for achieving consistency and reliability in distributed systems, especially in the presence of faults or adversarial behavior. The consensus algorithm used by the XRP Ledger falls into this category.

Search-based test case generation approaches make use of static type information to determine which data types should be used for the …

Abstract: The microservice architecture allows developers to divide the core functionality of their software system into multiple smaller services. However, this architectural style also makes it harder for them to debug and assess whether the system’s deployment conforms to its implementation.

Abstract: Search-based approaches have been used in the literature to automate the process of creating unit test cases. However, related work has shown that generated tests with high code coverage could be ineffective, i.