Related papers: Generating Project-Specific Test Cases with Requir…
One of the key strengths of Large Language Models (LLMs) is their ability to interact with humans by generating appropriate responses to given instructions. This ability, known as instruction-following capability, has established a…
Mutation testing is the state-of-the-art technique for assessing the fault-detection capacity of a test suite. Unfortunately, mutation testing consumes enormous computing resources because it runs the whole test suite for each and every…
Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual efforts in writing unit tests, some techniques have been…
Translating configurations between different network devices is a common yet challenging task in modern network operations. This challenge arises in typical scenarios such as replacing obsolete hardware and adapting configurations to…
Generating tests for games is challenging due to the high degree of randomisation inherent to games and hard-to-reach program states that require sophisticated gameplay. The test generator NEATEST tackles these challenges by combining…
Currently, generating high-level test cases described in natural language from requirement documents is performed manually. In the industry, including companies specializing in software testing, there is a significant demand for the…
Inference making is an essential but complex skill in reading comprehension (RC). Some inferences require resolving references across sentences, and some rely on using prior knowledge to fill in the detail that is not explicitly written in…
Unit testing is an essential yet frequently arduous task. Various automated unit test generation tools have been introduced to mitigate this challenge. Notably, methods based on large language models (LLMs) have garnered considerable…
Large-scale pre-training has fundamentally changed how machine learning research is done today: large foundation models are trained once, and then can be used by anyone in the community (including those without data or compute resources to…
Acceptance testing is crucial to determine whether a system fulfills end-user requirements. However, the creation of acceptance tests is a laborious task entailing two major challenges: (1) practitioners need to determine the right set of…
Log data can reveal valuable information about how users interact with Web search services, what they want, and how satisfied they are. However, analyzing user intents in log data is not easy, especially for emerging forms of Web search…
Existing automatic code comment generators mainly focus on producing a general description of functionality for a given code snippet without considering developer intentions. However, in real-world practice, comments are complicated, which…
Generative AI is gaining increasing attention in software engineering, where testing remains an indispensable reliability mechanism. According to the widely adopted testing pyramid, unit tests constitute the majority of test cases and are…
The ability to generate test data is often a necessary prerequisite for automated software testing. For the generated data to be fit for its intended purpose, the data usually has to satisfy various logical constraints. When testing is…
Large Language Models (LLMs) have shown impressive abilities in code generation, but they may generate erroneous programs. Reading a program takes ten times longer than writing it. Showing these erroneous programs to developers will waste…
Artificial Intelligence-Generated Content (AIGC) is increasingly used by students to support learning tasks, yet its outputs may contain inaccuracies, fabricated references, bias, and unsupported claims. This study examined students'…
Automated test case generation is important. However, the automatically generated test input does not always make sense, and the automated assertion is difficult to validate against the program under test. In this paper, we propose…
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…
Despite the growing importance of multilingual aspect of web search, no appropriate offline metrics to evaluate its quality are proposed so far. At the same time, personal language preferences can be regarded as intents of a query. This…
Code generation with Large Language Models (LLMs) has been extensively studied and achieved remarkable progress. As a complementary aspect to code generation, test case generation is of crucial importance in ensuring the quality and…