Papers software testing
“software testing” 태그가 달린 논문 135편 · 필터 해제
Object-Oriented Test Case Generation Using Teaching Learning-Based Optimization (TLBO) Algorithm
Researchers are currently seeking effective methods for automated software testing to reduce time, avoid test case redundancy, and create comprehensive test cases to cover (paths, benches, conditions, and statements). …
Allsoftware testingA Comparison of Reinforcement Learning Frameworks for Software Testing Tasks
Software testing activities scrutinize the artifacts and the behavior of a software product to find possible defects and ensure that the product meets its expected requirements. Recently, Deep Reinforcement Learning (DRL…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Differential testing for machine learning: an analysis for classification algorithms beyond deep learning
Context: Differential testing is a useful approach that uses different implementations of the same algorithms and compares the results for software testing. In recent years, this approach was successfully used for test c…
Deep Learningsoftware testingDiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment
Quantization is one of the most applied Deep Neural Network (DNN) compression strategies, when deploying a trained DNN model on an embedded system or a cell phone. This is owing to its simplicity and adaptability to a wi…
AstronomyQuantizationsoftware testingGuiding the retraining of convolutional neural networks against adversarial inputs
Background: When using deep learning models, there are many possible vulnerabilities and some of the most worrying are the adversarial inputs, which can cause wrong decisions with minor perturbations. Therefore, it becom…
image-classificationImage Classificationsoftware testingAn Efficiency Study for SPLADE Models
Latency and efficiency issues are often overlooked when evaluating IR models based on Pretrained Language Models (PLMs) in reason of multiple hardware and software testing scenarios. Nevertheless, efficiency is an import…
Retrievalsoftware testingTest2Vec: An Execution Trace Embedding for Test Case Prioritization
Most automated software testing tasks can benefit from the abstract representation of test cases. Traditionally, this is done by encoding test cases based on their code coverage. Specification-level criteria can replace …
software testingPipelines for Social Bias Testing of Large Language Models
The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks. However, while research has shown how biased and harmful these models are, systematic ways of integrati…
software testingSoftware Testing for Machine Learning
Machine learning has become prevalent across a wide variety of applications. Unfortunately, machine learning has also shown to be susceptible to deception, leading to errors, and even fatal failures. This circumstance ca…
BIG-bench Machine Learningsoftware testingComparative Study of Machine Learning Test Case Prioritization for Continuous Integration Testing
There is a growing body of research indicating the potential of machine learning to tackle complex software testing challenges. One such challenge pertains to continuous integration testing, which is highly time-constrai…
BIG-bench Machine Learningsoftware testingFailed Disruption Propagation in Integer Genetic Programming
We inject a random value into the evaluation of highly evolved deep integer GP trees 9743720 times and find 99.7percent Suggesting crossover and mutation's impact are dissipated and seldom propagate outside the program. …
Diversitysoftware testingSymbolic RegressionNPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks
Deep learning has recently been widely applied to many applications across different domains, e.g., image classification and audio recognition. However, the quality of Deep Neural Networks (DNNs) still raises concerns in…
Defect DetectionDNN Testingimage-classificationImage Classification+1The Definitive Guide to Automated Regression Testing
Software testing is one of the most important things you can do to protect your software from bugs and improve conversion rates. I’ve been using automated regression testing for several years and wanted to share these tr…
regressionsoftware testingTesting Deep Learning Models: A First Comparative Study of Multiple Testing Techniques
Deep Learning (DL) has revolutionized the capabilities of vision-based systems (VBS) in critical applications such as autonomous driving, robotic surgery, critical infrastructure surveillance, air and maritime traffic co…
Autonomous Drivingsoftware testingFairness-aware Configuration of Machine Learning Libraries
This paper investigates the parameter space of machine learning (ML) algorithms in aggravating or mitigating fairness bugs. Data-driven software is increasingly applied in social-critical applications where ensuring fair…
BIG-bench Machine LearningFairnesssoftware testingDeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software
Although machine learning (ML) has been successful in automating various software engineering needs, software testing still remains a highly challenging topic. In this paper, we aim to improve the generative testing of s…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Systematic Training and Testing for Machine Learning Using Combinatorial Interaction Testing
This paper demonstrates the systematic use of combinatorial coverage for selecting and characterizing test and training sets for machine learning models. The presented work adapts combinatorial interaction testing, which…
BIG-bench Machine Learningsoftware testingArtificial Intelligence in Software Testing : Impact, Problems, Challenges and Prospect
Artificial Intelligence (AI) is making a significant impact in multiple areas like medical, military, industrial, domestic, law, arts as AI is capable to perform several roles such as managing smart factories, driving au…
Autonomous Vehiclessoftware testingAn Algorithm for Generating Gap-Fill Multiple Choice Questions of an Expert System
This research is aimed to propose an artificial intelligence algorithm comprising an ontology-based design, text mining, and natural language processing for automatically generating gap-fill multiple choice questions (MC…
Multiple-choicesoftware testingvalidEfficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVL
With the growing capabilities of autonomous vehicles, there is a higher demand for sophisticated and pragmatic quality assurance approaches for machine learning-enabled systems in the automotive AI context. The use of si…
Autonomous DrivingAutonomous VehiclesPedestrian Detectionsoftware testing