Context To reduce manual effort of extracting test cases from natural-language
requirements, many approaches based on Natural Language Processing (NLP) have been …

Testing is widely recognized as an important stage of the software development lifecycle.
Effective software testing can provide benefits such as bug finding, preventing regressions …

Writing tests is a time-consuming yet essential task during software development. We
propose to leverage recent advances in deep learning for text and code generation to assist …

Few-shot learning with large-scale, pre-trained language models is a powerful way to
answer questions about code, eg, how to complete a given code example, or even generate …

Deep Learning (DL) has gained wide attention in recent years. Meanwhile, bugs in DL
systems can lead to serious consequences, and may even threaten human lives. As a result …

Input constraints are useful for many software development tasks. For example, input
constraints of a function enable the generation of valid inputs, ie, inputs that follow these …

Deep learning (DL) has attracted wide attention and has been widely deployed in recent
years. As a result, more and more research efforts have been dedicated to testing DL …

Software specifications are essential for ensuring the reliability of software systems. Existing
specification extraction approaches, however, suffer from limited generalizability and require …

Property-based testing (PBT), while an established technique in the software testing
research community, is still relatively underused in real-world software. Pain points in writing …

Conventional automated test generation tools struggle to generate test oracles and tricky
bug-revealing test inputs. Large Language Models (LLMs) can be prompted to produce test …