Classifying consolidating software component selection methods
After introducing the broader concept, I focus on consolidation of empirical research.
As background for this, I offer a brief introduction to empirical research.
This distinguishes unsupervised learning from supervised learning and reinforcement learning.
Unsupervised learning is closely related to the problem of density estimation in statistics.
Furthermore, one wants to ensure that, if possible, these remaining defects will cause minimal disruption or damage.
Most modern software systems beyond limited personal use have become progressively larger and more complex because of the increased need for automation, functions, features, and services.
In machine learning and statistics, feature selection, also known as variable selection, feature reduction, attribute selection or variable subset selection, is the technique of selecting a subset of relevant features for building robust learning models.
When applied in biology domain, the technique is also called discriminative gene selection, which detects influential genes based on DNA microarray experiments.
Content consolidated, from empirical research reports, could be empirical or not and could be from quantitative research, qualitative research, other kinds of empirical research, or a combination thereof.
Each QA alternative is then compared by its cost, applicability, and effectiveness over different product types and application environments.
Based on these, the author recommends an integrated approach for software quality assurance and improvement.
classification used to prepare revenue and expenditures statistics for the government component of the public sector.
The next few paragraphs provide an overall view of the classification.
A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities.