相关论文: Automatic Unit Test Generation for Deep Learning F…

Deep Neural Networks (DNNs), with its promising performance, are being increasingly used in safety critical applications such as autonomous driving, cancer detection, and secure authentication. With growing importance in deep learning,…

Testing Deep Learning (DL) based systems inherently requires large and representative test sets to evaluate whether DL systems generalise beyond their training datasets. Diverse Test Input Generators (TIGs) have been proposed to produce…

New ideas in distributed systems (algorithms or protocols) are commonly tested by simulation, because experimenting with a prototype deployed on a realistic platform is cumbersome. However, a prototype not only measures performance but also…

Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query,…

Automated unit test generation is a well-known methodology aiming to reduce the developers' effort of writing tests manually. Prior research focused mainly on statically typed programming languages like Java. In practice, however,…

Modern web services routinely provide REST APIs for clients to access their functionality. These APIs present unique challenges and opportunities for automated testing, driving the recent development of many techniques and tools that…

Unit tests (UTs) play an instrumental role in assessing code correctness as well as providing feedback to large language models (LLMs), motivating automated test generation. However, we uncover a trade-off between generating unit test…

The unit testing of Deep Learning (DL) libraries is challenging due to complex numerical semantics and implicit tensor constraints. Traditional Search-Based Software Testing (SBST) often suffers from semantic blindness, failing to satisfy…

Large language models (LLMs) have shown strong potential for automated test generation, yet most approaches to generating Java unit tests still rely on mocking frameworks to handle dependencies. Mockless test generation could exercise more…

Template-based code generator development as part of model-drivendevelopment (MDD) demands for strong mechanisms and tools that support developers to improve robustness, i.e., the desired code is generated for the specified inputs. Although…

Deep Learning (DL) compilers have been widely utilized to optimize DL models for efficient deployment across various hardware. Due to their vital role in the DL ecosystem, ensuring their reliability and security is critical. However,…

Writing and maintaining UI tests for mobile apps is a time-consuming and tedious task. While decades of research have produced automated approaches for UI test generation, these approaches typically focus on testing for crashes or…

Automated test generation has become a key technique for ensuring software quality, particularly in modern API-based architectures. However, automatically generated test cases are typically assigned non-descriptive names (e.g., test0,…

This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed…

Understanding the correct API usage sequences is one of the most important tasks for programmers when they work with unfamiliar libraries. However, programmers often encounter obstacles to finding the appropriate information due to either…

Unit testing is crucial for detecting bugs in individual program units but consumes time and effort. Recently, large language models (LLMs) have demonstrated remarkable capabilities in generating unit test cases. However, several problems…

We present a new unit test generator for C code, CTGEN. It generates test data for C1 structural coverage and functional coverage based on pre-/post-condition specifications or internal assertions. The generator supports automated stub…

Using API reference documentation like JavaDoc is an integral part of software development. Previous research introduced a grounded taxonomy that organizes API documentation knowledge in 12 types, including knowledge about the…

Ensemble methods are powerful machine learning algorithms that combine multiple models to enhance prediction capabilities and reduce generalization errors. However, their potential to generate effective test cases for fault detection in a…

One of the critical phases in software development is software testing. Testing helps with identifying potential bugs and reducing maintenance costs. The goal of automated test generation tools is to ease the development of tests by…