Papers software testing
“software testing” 태그가 달린 논문 135편 · 필터 해제
An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning
Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breakin…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)software testing+1The State and Future of Genetic Improvement
We report the discussion session at the sixth international Genetic Improvement workshop, GI-2019 @ ICSE, which was held as part of the 41st ACM/IEEE International Conference on Software Engineering on Tuesday 28th May 2…
software testingBoosting Operational DNN Testing Efficiency through Conditioning
With the increasing adoption of Deep Neural Network (DNN) models as integral parts of software systems, efficient operational testing of DNNs is much in demand to ensure these models' actual performance in field conditio…
DNN Testingsoftware testingTest Selection for Deep Learning Systems
Testing of deep learning models is challenging due to the excessive number and complexity of computations involved. As a result, test data selection is performed manually and in an ad hoc way. This raises the question of…
Deep LearningGeneral Classificationimage-classificationImage Classification+1Ontology Based Test Case Generation for Black Box Testing
Software systems are not considered complete unless properly tested and verified. In existing literature, a growing interest on establishment of automated testing techniques has been observed. However, tedious manual pro…
software testingTowards Improved Testing For Deep Learning
The growing use of deep neural networks in safety-critical applications makes it necessary to carry out adequate testing to detect and correct any incorrect behavior for corner case inputs before they can be actually use…
Deep Learningsoftware testingRecurrent Neural Networks for Fuzz Testing Web Browsers
Generation-based fuzzing is a software testing approach which is able to discover different types of bugs and vulnerabilities in software. It is, however, known to be very time consuming to design and fine tune classical…
Model Selectionsoftware testingTensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Machine learning models are notoriously difficult to interpret and debug. This is particularly true of neural networks. In this work, we introduce automated software testing techniques for neural networks that are well-s…
software testingNLP-assisted software testing: A systematic mapping of the literature
Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of app…
software testingSingle Stage Prediction with Embedded Topic Modeling of Online Reviews for Mobile App Management
Mobile apps are one of the building blocks of the mobile digital economy. A differentiating feature of mobile apps to traditional enterprise software is online reviews, which are available on app marketplaces and represe…
BenchmarkingManagementsoftware testingColumn Generation for Interaction Coverage in Combinatorial Software Testing
This paper proposes a novel column generation framework for combinatorial software testing. In particular, it combines Mathematical Programming and Constraint Programming in a hybrid decomposition to generate covering ar…
software testingSECBENCH: A Database of Real Security Vulnerabilities
Currently, to satisfy the high number of system requirements, complex software is created which turns its development cost-intensive and more susceptible to security vulnerabilities. In software security testing, empiric…
software testingOptimization of Test Case Generation using Genetic Algorithm (GA)
Testing provides means pertaining to assuring software performance. The total aim of software industry is actually to make a certain start associated with high quality software for the end user. However, associated with …
Evolutionary Algorithmssoftware testingGPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation …
Gaussian ProcessesGPUsoftware testingVariational InferenceA new selection strategy for selective cluster ensemble based on Diversity and Independency
This research introduces a new strategy in cluster ensemble selection by using Independency and Diversity metrics. In recent years, Diversity and Quality, which are two metrics in evaluation procedure, have been used for…
ClusteringDiversitysoftware testing