Papers software testing
“software testing” 태그가 달린 논문 135편 · 필터 해제
A Systematic Approach for Assessing Large Language Models' Test Case Generation Capability
Software testing ensures the quality and reliability of software products, but manual test case creation is labor-intensive. With the rise of large language models (LLMs), there is growing interest in unit test creation …
software testingTest Case CreationAssessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models
Data augmentation has become a standard practice in software engineering to address limited or imbalanced data sets, particularly in specialized domains like test classification and bug detection where data can be scarce…
Data Augmentationsoftware testingToward Neurosymbolic Program Comprehension
Recent advancements in Large Language Models (LLMs) have paved the way for Large Code Models (LCMs), enabling automation in complex software engineering tasks, such as code generation, software testing, and program compr…
Code Generationsoftware testingMany-Objective Neuroevolution for Testing Games
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 challenge…
software testingAn efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering
This paper presents a novel approach to represent enterprise web application structures using Large Language Models (LLMs) to enable intelligent quality engineering at scale. We introduce a hierarchical representation me…
Few-Shot LearningIn-Context LearningLanguage ModelingLanguage Modelling+2The Potential of LLMs in Automating Software Testing: From Generation to Reporting
Having a high quality software is essential in software engineering, which requires robust validation and verification processes during testing activities. Manual testing, while effective, can be time consuming and costl…
software testingDesign choices made by LLM-based test generators prevent them from finding bugs
There is an increasing amount of research and commercial tools for automated test case generation using Large Language Models (LLMs). This paper critically examines whether recent LLM-based test generation tools, such as…
software testingReinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation
Software testing is a crucial but time-consuming aspect of software development, and recently, Large Language Models (LLMs) have gained popularity for automated test case generation. However, because LLMs are trained on …
software testingCPP-UT-Bench: Can LLMs Write Complex Unit Tests in C++?
We introduce CPP-UT-Bench, a benchmark dataset to measure C++ unit test generation capability of a large language model (LLM). CPP-UT-Bench aims to reflect a broad and diverse set of C++ codebases found in the real world…
In-Context LearningLanguage ModelingLanguage ModellingLarge Language Model+2Software testing for project report.
Methods of Software Testing There are two basic methods of performing software testing: 1. Manual testing 2. Automated testing Manual Software Testing As the name would imply, manual software testing is the process of an…
software testingVALTEST: Automated Validation of Language Model Generated Test Cases
Large Language Models (LLMs) have demonstrated significant potential in automating software testing, specifically in generating unit test cases. However, the validation of LLM-generated test cases remains a challenge, pa…
HumanEvalLanguage ModelingLanguage Modellingmbpp+3Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?
Search-based software testing (SBST) is a widely adopted technique for testing complex systems with large input spaces, such as Deep Learning-enabled (DL-enabled) systems. Many SBST techniques focus on Pareto-based optim…
software testingTAEGAN: Generating Synthetic Tabular Data For Data Augmentation
Synthetic tabular data generation has gained significant attention for its potential in data augmentation, software testing and privacy-preserving data sharing. However, most research has primarily focused on larger data…
Data AugmentationGenerative Adversarial NetworkPrivacy Preservingsoftware testing+1On the Effectiveness of LLMs for Manual Test Verifications
Background: Manual testing is vital for detecting issues missed by automated tests, but specifying accurate verifications is challenging. Aims: This study aims to explore the use of Large Language Models (LLMs) to produc…
4ksoftware testingComputer Vision Intelligence Test Modeling and Generation: A Case Study on Smart OCR
AI-based systems possess distinctive characteristics and introduce challenges in quality evaluation at the same time. Consequently, ensuring and validating AI software quality is of critical importance. In this paper, we…
3D ClassificationOptical Character RecognitionOptical Character Recognition (OCR)software testingExploring the Integration of Large Language Models in Industrial Test Maintenance Processes
Much of the cost and effort required during the software testing process is invested in performing test maintenance - the addition, removal, or modification of test cases to keep the test suite in sync with the system-un…
software testingThe Future of Software Testing: AI-Powered Test Case Generation and Validation
Software testing is a crucial phase in the software development lifecycle (SDLC), ensuring that products meet necessary functional, performance, and quality benchmarks before release. Despite advancements in automation, …
Overall - Testsoftware testingThe Role of Artificial Intelligence and Machine Learning in Software Testing
Artificial Intelligence (AI) and Machine Learning (ML) have significantly impacted various industries, including software development. Software testing, a crucial part of the software development lifecycle (SDLC), ensure…
Defect Detectionsoftware testingTesting and Evaluation of Large Language Models: Correctness, Non-Toxicity, and Fairness
Large language models (LLMs), such as ChatGPT, have rapidly penetrated into people's work and daily lives over the past few years, due to their extraordinary conversational skills and intelligence. ChatGPT has become the…
FairnessLanguage ModelingLanguage ModellingLogical Reasoning+2Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests
Automated unit test generators, particularly search-based software testing tools like EvoSuite, are capable of generating tests with high coverage. Although these generators alleviate the burden of writing unit tests, th…
Bug fixingDescriptivesoftware testing