Jove
Visualize
Contact Us

Related Concept Videos

Quality Assurance01:19

Quality Assurance

574
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
574
Response Surface Methodology01:16

Response Surface Methodology

374
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
374
Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

119
The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
119
Data Validation01:15

Data Validation

383
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
383
Data Validation01:03

Data Validation

6.0K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
6.0K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.6K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Enhancing analogy-based software cost estimation using Grey Wolf Optimization algorithm.

PeerJ. Computer science·2025
Same author

LEMABE: a novel framework to improve analogy-based software cost estimation using learnable evolution model.

PeerJ. Computer science·2022
Same author

Landmark-based homologous multi-point warping approach to 3D facial recognition using multiple datasets.

PeerJ. Computer science·2021
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Nov 10, 2025

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

385

Development of a valid and reliable software customization model for SaaS quality through iterative method:

Abdulrazzaq Qasem Ali1, Abu Bakar Md Sultan1, Abdul Azim Abd Ghani1

  • 1Department of Software Engineering and Information System, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary

This study developed a reliable model for software customization in Software as a Service (SaaS) applications. It identifies key customization types and quality attributes to improve SaaS quality analysis.

Keywords:
Content validityCustomization approachesIterative methodModel developmentReliability studySaaS qualitySoftware as a service

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.1K
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.4K

Related Experiment Videos

Last Updated: Nov 10, 2025

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

385
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.1K
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.4K

Area of Science:

  • Software Engineering
  • Information Systems
  • Quality Management

Background:

  • Standardization in Software as a Service (SaaS) is beneficial, but customer-specific requirements necessitate application customization.
  • Balancing standardization with customization is crucial for SaaS application success and user satisfaction.

Purpose of the Study:

  • To develop a valid and reliable model for software customization in SaaS applications.
  • To identify generic software customization types, common practices, and key quality attributes associated with SaaS customization.

Main Methods:

  • The study involved three phases: model conceptualization, content validation with academic expertise, and reliability testing with software engineers.
  • An initial model with six customization approaches, 46 practices, and 13 quality attributes was refined through two validation rounds.
  • Internal consistency reliability was assessed by 34 software engineer researchers.

Main Results:

  • The final model comprises six constructs and 44 items, including Configuration, Composition, Extension, Integration, Modification, and SaaS quality.
  • Content validation led to the removal of one approach and 14 practices, with 20 practices being reformulated.
  • The reliability study confirmed that all constructs of the content-validated model are reliable.

Conclusions:

  • The developed model provides a framework for empirically analyzing the impact of software customization on SaaS quality.
  • The model's constructs and items can enhance the systematic evaluation of customization strategies in multi-tenant SaaS environments.
  • This research contributes to a better understanding of how to manage and measure SaaS application customization effectively.