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A systematic review of machine learning methods in software testing
Background The quest for higher software quality remains a paramount concern in software
testing, prompting a shift towards leveraging machine learning techniques for enhanced …
testing, prompting a shift towards leveraging machine learning techniques for enhanced …
Mitigating noise in quantum software testing using machine learning
Quantum Computing (QC) promises computational speedup over classic computing.
However, noise exists in near-term quantum computers. Quantum software testing (for …
However, noise exists in near-term quantum computers. Quantum software testing (for …
How artificial intelligence can revolutionize software testing techniques
Since the end of the 2000s, connected objects, applications and other innovative digital
tools have abounded and continued to grow. However, if the digital evolution makes it …
tools have abounded and continued to grow. However, if the digital evolution makes it …
On the value of parameter tuning in stacking ensemble model for software regression test effort estimation
A type of software testing, regression testing is often costly and labour-intensive. As such,
multiple corporations have intensified efforts to estimate the amount of effort required …
multiple corporations have intensified efforts to estimate the amount of effort required …
Understanding the factors that influence software testing through moments of translation
Purpose Organisations make use of different tools and methods in testing software to ensure
quality and appropriateness for business needs. Despite the efforts, many organisations …
quality and appropriateness for business needs. Despite the efforts, many organisations …
Energy efficient and optimized genetic algorithm for software effort estimator using double hidden layer bi-directional associative memory
In software development, it's important to have an accurate assessment of effort, cost,
energy, and time in order to plan and allocate resources in the best way possible. This …
energy, and time in order to plan and allocate resources in the best way possible. This …
Analysis of tree-family machine learning techniques for risk prediction in software requirements
Risk prediction is the most sensitive and critical activity in the Software Development Life
Cycle (SDLC). It might determine whether the project succeeds or fails. To increase the …
Cycle (SDLC). It might determine whether the project succeeds or fails. To increase the …
An impact-driven approach to predict user stories instability
A common way to describe requirements in Agile software development is through user
stories, which are short descriptions of desired functionality. Nevertheless, there are no …
stories, which are short descriptions of desired functionality. Nevertheless, there are no …
The impact of data quality on software testing effort prediction
Background: This paper investigates the impact of data quality on the performance of
models predicting effort on software testing. Data quality was reflected by training data …
models predicting effort on software testing. Data quality was reflected by training data …
Gradient boosting optimized through differential evolution for predicting the testing effort of software projects
Software testing (ST) is one of the most important software development life cycle (SDLC)
phases and ST effort is often expressed as a percentage of SDLC effort. Unfortunately, in the …
phases and ST effort is often expressed as a percentage of SDLC effort. Unfortunately, in the …