The oracle problem in software testing: A survey
Testing involves examining the behaviour of a system in order to discover potential faults.
Given an input for a system, the challenge of distinguishing the corresponding desired …
Given an input for a system, the challenge of distinguishing the corresponding desired …
[PDF][PDF] A comprehensive survey of trends in oracles for software testing
Testing involves examining the behaviour of a system in order to discover potential faults.
Determining the desired correct behaviour for a given input is called the “oracle problem” …
Determining the desired correct behaviour for a given input is called the “oracle problem” …
Using machine learning to generate test oracles: A systematic literature review
Machine learning may enable the automated generation of test oracles. We have
characterized emerging research in this area through a systematic literature review …
characterized emerging research in this area through a systematic literature review …
Artificial 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 …
military, industrial, domestic, law, arts as AI is capable to perform several roles such as …
Cirfix: automatically repairing defects in hardware design code
This paper presents CirFix, a framework for automatically repairing defects in hardware
designs implemented in languages like Verilog. We propose a novel fault localization …
designs implemented in languages like Verilog. We propose a novel fault localization …
Assessing evaluation metrics for neural test oracle generation
Recently, deep learning models have shown promising results in test oracle generation.
Neural Oracle Generation (NOG) models are commonly evaluated using static (automatic) …
Neural Oracle Generation (NOG) models are commonly evaluated using static (automatic) …
Automated software test data generation with generative adversarial networks
With the rapid increase of software scale and complexity, the cost of traditional software
testing methods will increase faster than the scale of software. In order to improve test …
testing methods will increase faster than the scale of software. In order to improve test …
Application of quantum extreme learning machines for qos prediction of elevators' software in an industrial context
Quantum Extreme Learning Machine (QELM) is an emerging technique that utilizes
quantum dynamics and an easy-training strategy to solve problems such as classification …
quantum dynamics and an easy-training strategy to solve problems such as classification …
Artificial neural networks as speech recognisers for dysarthric speech: Identifying the best-performing set of MFCC parameters and studying a speaker-independent …
SR Shahamiri, SSB Salim - Advanced Engineering Informatics, 2014 - Elsevier
Dysarthria is a neurological impairment of controlling the motor speech articulators that
compromises the speech signal. Automatic Speech Recognition (ASR) can be very helpful …
compromises the speech signal. Automatic Speech Recognition (ASR) can be very helpful …
Evospex: An evolutionary algorithm for learning postconditions
Software reliability is a primary concern in the construction of software, and thus a
fundamental component in the definition of software quality. Analyzing software reliability …
fundamental component in the definition of software quality. Analyzing software reliability …