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This paper proposes an examination of effective methods and tools for ensuring software quality. The scope of this topic includes current issues related to software quality assurance within the context of analyzing methods and tools used in practice to develop high-quality software. During the model...
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The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
2025
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System research and information technologies| _version_ | 1866302953420226560 |
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| author | Shantyr, Anton |
| author_facet | Shantyr, Anton |
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| description | This paper proposes an examination of effective methods and tools for ensuring software quality. The scope of this topic includes current issues related to software quality assurance within the context of analyzing methods and tools used in practice to develop high-quality software. During the modeling process, a new comprehensive model for software quality assurance has been developed, combining modular testing, integration testing, and continuous integration methods. The advantage of this development is its enhanced adaptability to addressing key challenges in software quality assurance. Based on the developed model, strategies and approaches are proposed to improve configuration management processes and identify vulnerabilities in software systems. |
| doi_str_mv | 10.20535/SRIT.2308-8893.2025.3.05 |
| first_indexed | 2025-11-09T02:11:02Z |
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A.S. Shantyr, 2025
60 ISSN 1681–6048 System Research & Information Technologies, 2025, № 3
UDC 004.41
DOI: 10.20535/SRIT.2308-8893.2025.3.05
USE OF METHODS AND TOOLS
FOR ENSURING SOFTWARE QUALITY
A.S. SHANTYR
Abstract. This paper proposes an examination of effective methods and tools for
ensuring software quality. The scope of this topic includes current issues related to
software quality assurance within the context of analyzing methods and tools used in
practice to develop high-quality software. During the modeling process, a new com-
prehensive model for software quality assurance has been developed, combining
modular testing, integration testing, and continuous integration methods. The advan-
tage of this development is its enhanced adaptability to addressing key challenges in
software quality assurance. Based on the developed model, strategies and ap-
proaches are proposed to improve configuration management processes and identify
vulnerabilities in software systems.
Keywords: comprehensive model, quality standards, integration testing, unit testing,
technological challenges, integration testing, continuous integration.
INTRODUCTION
In the modern digital world, often referred to as the era of software, the concept of
software quality has become not only relevant but critically important. In all areas
of activity, from business and science to everyday needs, software has become an
integral component. However, the mere existence of a software product does not
guarantee its success [1]. In this regard, it is quite appropriate to agree with the
views of the authors of the work [2], which conclude that quality becomes the
decisive factor influencing decision-making and meeting user needs [2].
According to the authors of the study [3], achieving high-quality software
requires not only innovative technologies but also effective methods and tools for
ensuring quality. These methods and tools encompass a wide range of activities,
from testing and code analysis to quality management and the implementation of
best practices in the software development process. Scientific research in the field
of software quality assurance has been conducted for several decades, and many
aspects have already been addressed, including:
test automation: numerous tools, frameworks, and Continuous Integration
/ Continuous Deployment practices have been developed for automatic code qual-
ity verification [13];
quality metrics: a set of metrics has been defined and implemented, such
as code coverage, system response time, the number of defects per unit of code,
and others [4; 5];
software security: immunity to many types of attacks has been devel-
oped, popular vulnerabilities such as SQL injection, cross-site scripting (XSS)
and others have been identified and fixed;
Use of methods and tools for ensuring software quality
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development methodologies: there are many recognized methodologies,
such as Scrum, Kanban, DevOps, which regulate development and quality assur-
ance processes.
However, there are also unsolved problems and directions for further re-
search, namely:
intelligent testing: unsolved questions regarding the evaluation of the de-
velopment of machine learning methods for automated testing that are able to
adapt to changes in the code and respond to new functions. The essence of the
problem of intelligent testing within the framework of quality assessment of soft-
ware systems boils down to the fact that testing requires an understanding of the
functionality of the program and the possibilities of automation. Ensuring the
adaptability of tests to changes in the code requires intelligent systems. The com-
plexity of solving the above-mentioned problem lies in the fact that at the current
level of technological development of qualitative evaluation of software systems,
there are technological difficulties in the development of optimized machine
learning algorithms that can adapt to changes in the software code and detect new
functionalities automatically;
security of microservice architecture: issues related to ensuring security
and integrity in the context of the rapid development of microservice systems,
given the large number of interacting components, have not been fully resolved.
According to [5], the significant number of interacting microservices creates a
vast attack surface, complicating the detection and resolution of potential vulner-
abilities. As per [6], the challenge in addressing this problem at the methodologi-
cal level lies in the incomplete implementation of mechanisms that address the
development of systems for detecting and monitoring potential attacks, as well as
the development of secure microservice development practices;
automation of vulnerability detection: the issues surrounding the devel-
opment of effective tools for automated detection of new vulnerabilities and their
subsequent remediation have not been fully addressed. According to [7], the es-
sence of this problem is that vulnerabilities evolve, and tools need to detect new
security threats. The challenge here is the constant need to develop more effec-
tive, cutting-edge tools and technologies capable of identifying new and unknown
vulnerabilities through contextual understanding and advanced detection methods;
combination of functional and learning-based testing: approaches that
combine functional testing with machine learning to more effectively detect de-
fects have not been fully developed;
security guarantees at large scales: the development of methods and ar-
chitectures to ensure the security of software systems in large, distributed, and
complex ecosystems has not been fully addressed. The core of the problem is that
in large ecosystems, such as cloud services, ensuring security at all levels is chal-
lenging [8]. Additionally, there are difficulties in using distributed architecture,
encryption, and security standards to protect vast amounts of data and systems;
ethical aspects in testing: the development of standards and ethical norms
for automated testing systems and their impact on people has not been fully ad-
dressed.
The core of the problem is the need to solve issues related to testing that may
affect user privacy and security. The complexity of this issue lies in the definition
of standards and the implementation of practices that ensure ethical standards in
testing.
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ANALYSIS OF RECENT PUBLICATIONS
Foidl H., Felderer M. [1] considered the principles of integration of software qual-
ity models based on risk-oriented testing. The authors noted above solve the prob-
lem of defining software by using quality models. Within the scope of the raised
topic, the authors propose to integrate software quality models based on risk-oriented
testing, according to their position, this approach will help significantly improve the
effectiveness of testing, allowing to focus on areas with high risk for software.
The advantages of this approach are more efficient use of resources: focus-
ing testing on high-risk areas allows for more efficient use of limited resources,
directing them to the most critical aspects of the software. In addition, risk-based
testing, based on quality models, allows you to better consider business needs and
focus on aspects of the program that have the greatest impact on business proc-
esses. The authors also note that the integration of quality models helps to identify
potential problems and risks in advance, which allows developers to pay more
attention to their solution in the early stages of development. In addition, the au-
thors proved that focusing on important areas of risk improves software quality by
identifying and solving problems important to users. However, the integration of
software quality models into risk-based testing proposed by Foidl and Felderer
may have certain drawbacks, namely:
complexity of integration: transferring quality models into the context of
risk-based testing can be challenging, especially if there are different methods and
approaches to defining quality and risks;
data heterogeneity: quality models may be based on different data sets,
and risks may arise from various sources. Integrating this data can be problematic
due to its heterogeneity and incompatibility;
ambiguity in defining risks and quality: the concepts of risk and quality
may have different interpretations for different stakeholders, making the integra-
tion of models difficult due to this ambiguity;
need for large amounts of data: effective operation of integrated models
requires a significant amount of data, both on software quality and risks. This can
be a problem where access to such data is limited;
system complexity: integrating models may require significant effort and
resources to develop, implement, and maintain complex systems.
Thus, while integrating software quality models into risk-based testing can
bring substantial benefits in identifying high-risk areas for software, it is essential
to carefully study and consider the aforementioned challenges for successful ap-
plication of this approach. K. Sahu and R. K. Srivastava [2] examined the founda-
tions of software error prediction using neuro-fuzzy logic methods. Their research
emphasizes software reliability and the need to find effective error prediction
methods. However, the researchers did not explore the possibility of integrating
error prediction methods with other quality assurance methods, such as statistical
models, testing, or defect analysis. In general, recent research in the field of effec-
tive methods and tools for software quality assurance significantly improves the
efficiency of quality assurance processes. According to [9], at the technological-
methodological level, the issues related to the aforementioned analysis require a
deep understanding of the functional and technical aspects of software systems, as
well as innovative approaches to solving them. The complexity lies in combining
Use of methods and tools for ensuring software quality
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technical expertise with creativity and flexibility in addressing diverse problems.
According to [10], research on software quality assessment methods is ongoing to
identify effective tools and techniques. However, according to [10], research in
the field of software quality assessment requires continuous improvement of
methods and tools, as well as consideration of changes in the technological land-
scape and user requirements. According to [11], research on effective methods
and tools for software quality assurance is a crucial field in information technol-
ogy. According to [12], issues related to software quality assurance can arise for
various reasons, and solving them requires a systematic approach. In the frame-
work of research on quality assurance methods and tools, the researchers from
works [115] encountered numerous issues requiring additional comprehensive
analysis. Specifically, in the area of automated testing, it is advisable to conduct
research to develop the most appropriate automated tests covering various aspects
of the software and its functionality to prevent malfunction in real-world condi-
tions [14]. In the realm of improving quality metrics, it is important to conduct
research on developing and implementing effective metrics that take into account
various aspects of quality, such as reliability, performance, and security [3]. In
terms of addressing software security issues, it is necessary to analyze and im-
prove methods for identifying and eliminating potential vulnerabilities and to
develop tools that enhance compliance with security requirements in a growing
environment of cyber threats [46]. Thus, recent studies require further investiga-
tion to continue comprehensive work on resolving the identified challenges.
Setting the task. Aim of the work is to study effective methods and tools for
ensuring the quality of software systems and to develop a new comprehensive
model for ensuring the quality of software systems, which combines the methods
of module testing, integration testing and continuous integration, which contrib-
utes to the improvement of the quality of the software product at various stages of
development.
Achieving the set goal comes down to solving the following problems:
conducting a generalized analysis of current issues related to the study of
effective methods and means to ensure the quality of software systems;
conducting a comprehensive contextual base-review generalization of the
main modern qualitative and quantitative methods that are used to assess the qual-
ity of software systems;
analysis of the main mathematical models involved in integration testing
and quality assessment of software systems;
development and research of a new complex model of quality assurance
of software systems, which is based on a combination of methods of module test-
ing, integration testing and continuous integration.
THE MAIN PART
Conducting a generalized analysis of current issues related to the study of ef-
fective methods and tools for ensuring the quality of software systems, it is
appropriate to highlight a number of issues in this area, which are presented in
Table 1.
Table 1 is made by the author of this article on the basis of works
analysis [115].
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T a b l e 1 . Generalized analysis of current issues related to the study of effective
methods and tools to ensure the quality of software systems
The name of the
generalized area
of topical issues
Generalized description
of the problem
Research that is needed within
the framework of solving this issue
Automated
testing
Lack of full test coverage,
which can lead to incorrect
operation of the program
in real conditions
Research within the development
of automated tests that cover various aspects
of the program and its functionality
Quality
metrics
Lack of clear metrics
for measuring
software quality
Research within the development and
implementation of effective metrics that
take into account various aspects of quality
such as reliability, performance and safety
Software
security
Ensuring the security
of software systems in the
growing environment
of cyber threats
Research that involves the analysis and
improvement of methods for identifying and
eliminating potential vulnerabilities, as well
as the development of means to improve
compliance with security requirements
Development
methodologies
The choice of the most
appropriate methodology
for the development of soft-
ware systems, taking into
account the trends
in ensuring the quality
of the software product.
Research that involves a comparative
analysis of various methodologies, identify-
ing their advantages and disadvantages,
as well as developing recommendations
for choosing
the appropriate approach to development
Quality
standards
Lack of uniform quality
standards for different
fields of development.
Research on the development
and implementation of quality standards
that take into account the specifics
of various types of software systems
Version control of
software
systems and
configuration man-
agement
Effective implementation
and management
of changes
in software code
Research on the development of tools
for automated version control
and configuration management
to ensure code stability and quality
Shown in Table 1 the results of a generalized visual analysis show that the
quality of software systems has several key aspects that need attention and re-
search. In particular, in the field of resolving issues on automated testing, it is ad-
visable to conduct research on improving software testing, which is used to pre-
vent improper operation of programs and accordingly covers the analysis of their
functionality in real conditions [14].
With the limits of improving quality metrics, it is important to conduct re-
search on the development and implementation of effective metrics that take into
account various aspects of quality, such as reliability, productivity and security of
software [3]. In the framework of adaptive development, quality metrics in our
opinion are advisable to apply the definition of key quality indicators (KPIs) to
measure different aspects of software quality.
Use of methods and tools for ensuring software quality
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Fig. 1 shows a scheme of complex contextual basis-view generalization of
basic modern qualitative methods that are used to assess the quality of software
systems. Analyzing in Fig. 1. Methods should be noted that today, in the field of
quality assessment of software systems, specific standards do not determine the
Delphi method. In the context of the quality
assessment of the software systems,
the Delphi method can be used to obtain objective
expert opinion about various aspects
of quality of the software product
Complex contextual basis-view generalization of basic modern
qualitative methods that are used to evaluate the quality of software
Quality methods of evaluation of quality software systems
Generalized purpose: at a complex level within the framework of delineating the basic
spectrum of problem solving on qualitative evaluation of software systems “Qualitative
methods” used to solve questions related to the assessment of the principles of formalization
of problems, creation of options and qualitative evaluation of the possibilities of operation
of the system. These methods are quite rational to apply them in cases where there is no
description of the patterns of the system in the form of analytical dependencies
The “script” method in the context of quality assurance of software systems is
an approach to testing, which involves the development and implementation
of specific sequences (scenarios) of actions to check the functionality
and properties of the software product
Methods for assessing the quality of software systems, which are based
on the basic aspects of the methodological direction
of the “brainstorming” method (methods of “brainstorming” type)
Expert evaluation method in the context of implementing the
mechanisms and aspects of scientific versatile review
and practical assessment of quality assurance of software systems
The Tree Goal method in the context of the quality assessment
of software systems can be used to systematize and define the goals related
to the quality of the software product
Morphological methods create a systematic structure for determining,
analyzing and improving the quality of software systems, which allows the
team of developers and management to better understand and effectively
manage aspects of product quality
The system of system analysis based on qualitative methods of quality
assessment of software systems involves the consideration of various aspects
and interaction of the system components to assess their efficiency and quality
Fig. 1. Scheme of complex contextual basis-view generalization of basic modern
qualitative methods that are used to assess the quality of software systems
A.S. Shantyr
ISSN 1681–6048 System Research & Information Technologies, 2025, № 3 66
use of specific brainstorming methods. However, brainstorming methods should
be used in quality management processes and software testing, as well as in col-
lecting requirements and design [9].
Brainstorming methods can be successfully integrated into these
standardized processes to achieve better results in solving software quality
problems [114]. Analyzing the script method in the context of quality assurance
of software systems, we note that this is an approach to testing, which involves
the development and implementation of specific sequences (scenarios) of actions
to check the functionality and properties of the software [6]. Brainstorming
methods can be used in the development of test scenarios, identification of
potential defects and collecting requirements for testing.
According to ISO/IEC 9126 (Software Engineering Product Quality):
ISO/IEC 9126 is related to the quality of the software. Brainstorming methods can be
used in determining the requirements for functionality, reliability, ease of use, etc.
Below are examples of how brainstorming can be used in the context of
quality assessment of software systems:
ISO/IEC 25010 (Square): ISO/IEC 25010 Standard “Systems and Soft-
ware. Model of Quality and Assessment” determines the software quality model.
Brainstorming methods can be used in determining the requirements, as well as in
the analysis and planning of testing to ensure the completeness and variety of test
scenarios;
IEEE 730 (Standard for Software Quality Assurance Processes): The
IEEE 730 Standard defines the quality assurance processes. IstQB (International
Software Testing Qualifications Board): ISTQB initiative defines standards for
certification of software testing professionals.
It is worth noting that the standards do not fully specify the fulfillment of the
completeness of the use of brainstorming methods, they provide context and
principles for the introduction of creative and collective approaches to solving
problems in the quality of software systems.
In Table 2 the generalized characteristics of methodological directions of
qualitative methods in the spectrum of quality of software systems are given.
Brainstorming methods can be successfully integrated into these standardized
processes to achieve better results in solving software quality problems [114].
Analyzing the script method in the context of quality assurance of software
systems, we note that this is an approach to testing, which involves the develop-
ment and implementation of specific sequences (scenarios) of actions to check the
functionality and properties of the software [6]. Brainstorming methods can be
used in the development of test scenarios, identification of potential defects and
collecting requirements for testing.
According to ISO/IEC 9126 (Software Engineering —Product Quality):
ISO/IEC 9126 is related to the quality of the software. Brainstorming methods can be
used in determining the requirements for functionality, reliability, ease of use, etc.
Below are examples of how brainstorming can be used in the context of
quality assessment of software systems:
ISO/IEC 25010 (Square): ISO/IEC 25010 Standard “Systems and Software.
Model of Quality and Assessment” determines the software quality model. Brainstorm-
ing methods can be used in determining the requirements, as well as in the analysis
and planning of testing to ensure the completeness and variety of test scenarios;
IEEE 730 (Standard for Software Quality Assurance Processes): the IEEE
730 Standard defines the quality assurance processes. IstQB (International Soft-
Use of methods and tools for ensuring software quality
Системні дослідження та інформаційні технології, 2025, № 3 67
ware Testing Qualifications Board): ISTQB initiative defines standards for certi-
fication of software testing professionals.
It is worth noting that the standards do not fully specify the fulfillment of the
completeness of the use of brainstorming methods, they provide context and
principles for the introduction of creative and collective approaches to solving
problems in the quality of software systems.
In Table 2 the generalized characteristics of methodological directions of
qualitative methods in the spectrum of quality of software systems are given.
Quantitative methods used to evaluate the quality of software systems have
the following basic generalized characteristics:
quantitative formalization of problems: quantitative methods allow to
structure and formalize problems using quantitative indicators and parameters.
Provides objectivity and quantitative evaluation of system parameters;
T a b l e 2 . Generalized characteristics of methodological directions of qualitative
methods in the spectrum of quality assessment of software systems
The name of the
methodological
direction
Description Advantages and disadvantages
Testing
It includes the execution
of programs in order
to detect errors
or deficiencies
Effectively detects some types of errors, helps
to confirm compliance with requirements.
Cannot guarantee the absence of all errors,
testing costs may be high
Code analysis
Expert analysis of soft-
ware code to identify po-
tential problems or ineffi-
ciencies
Can reveal code complexity,
potential vulnerabilities.
Limited to the quality of the code, it is
difficult to detect certain types of errors
Automated
quality analysis
tools
Using tools for automated
code quality analysis and
error detection
Reduces dependence on the human factor,
quickly detects common problems. May under-
estimate context and complex aspects of code
Code check
Code review by the
development team to iden-
tify errors and improve
quality
Attracts experts, promotes knowledge exchange,
is effective in identifying shortcomings.
Requires time and effort
from the development team
Quality
modeling
Use of mathematical
models
Allows to carry out quality analysis
before product release
Specified in Table 2 methodological directions can be used separately, or in
combination to achieve a more accurate and complete assessment of the quality of
software systems.
In Fig. 2 shows a scheme of complex contextual basis-view generalization of
basic modern quantitative methods that are used to assess the quality of software
systems. Accordingly [3–4] quantitative methods within the framework of the
implementation of the quality of software systems allow quantitative analysis and
make sound solutions based on numerical data and system parameters:
quantitative formation of variants: quantitative methods help to create differ-
ent variants of system implementation, taking into account quantitative aspects. Pro-
vides objectivity and quantitative evaluation of the possibilities of different options;
quantitative evaluation of operation options: used to quantify the effi-
ciency and suitability of different options for the system. Allows objective analy-
sis and comparison of different scenarios of system use;
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application in the presence of analytical dependencies: quantitative meth-
ods are effectively used when the possibility of expression of the system analyti-
cally allows quantitative characteristics. Provides accuracy and objectivity in the
presence of quantitative data.
The modular testing method is closely linked to the concept of software
testing and arose in the context of software testing. However, the specific term
“modular testing” and its methodology received significant flowering through a
movement known as “Extreme Programming” (XP), which began in 19902000
(developers: Kent Beck and his colleagues). XP is a software development
methodology that defines certain practices and principles to improve the quality
and speed of development. It should be noted that modular testing involves
writing automated tests for each individual “module” of the code before its
integration into the system. The essence of this method is to create the maximum
number of automated tests for the individual components of the program and run
them during development to detect errors in the early stages.
Complex contextual basis-view generalization of basic modern quantitative
methods that are used to assess the quality of software systems
Quantitative methods for software systems quality assessment
Generalized purpose: at the complex level within the framework of the outline of the
basic spectrum of problem solving from the qualitative evaluation of the software systems
“quantitative methods” used for formalization of problems, creation of options and
quantitative evaluation of the possibilities of operation of the system. It is rational to apply
them in cases where quantitative characteristics or evaluation of system parameters are
possible. Accordingly, these methods provide a quantitative analysis of variational
assessments of the quality of software systems and the calculation of the characteristics of
data of systems in the process of evaluating their real operational operation
Low abstraction methods within the implementation of the quality assessment
process are based on the analysis of resource efficiency, errors resistance,
performance optimization, code safety, scalability, networking optimization
and input, resource control and hardware
High -level abstraction methods within the implementation of the quality assessment
process are based on the analysis of f Methods of abstraction are usually based
on the interaction between different software components.unctionality, reliability,
performance, safety, compatibility, ease of maintenance, portability and user
satisfaction
Combinations of methods of 3 levels: a system of system analysis based on
quantitative methods of quality assessment of software systems involves quantitative
consideration of various aspects and interaction of the components of the system to
evaluate their efficiency and quality
Methods of abstraction are usually based on the interaction
between different software components
Fig. 2. Scheme of complex contextual basis-view generalization of the main modern
quantitative methods that are used to evaluate the quality of software systems
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Integration testing is an important component of the quality assessment of
software systems and is usually performed at the development stage when the
different components of the software system are grouped into a single system.
The main purpose of integration testing is to check the interaction between these
components, as well as integrated system functions.
Different approaches and techniques are used in the integration testing
process, such as:
testing of modules on isolation: before integration of modules into the
system, each module is tested separately to check its functionality and compliance
with requirements;
interface testing: an important step in integration testing is to check the
interaction between components through their interfaces. This may include data
testing between modules and verifying the data of data integrity;
testing of the interaction of components: after the integration of the mod-
ules test the interaction between them, convincing that they work properly to-
gether and perform the expected functions;
exception and error testing: integration testing also includes checking the
system response to exceptional situations and errors, such as incorrect data or in-
accessible resources;
productivity testing: during integration testing, performance tests are also
performed to ensure that the system works efficiently and is capable of processing
the load caused.
The purpose of integration testing is to ensure the high quality of the soft-
ware system by identifying and solving problems related to the integration of
components. This allows you to ensure the correct and reliable operation of the
system as a whole.
In Table 3 the results of the analysis of the main mathematical models
involved in integration testing and quality assessment of software systems are
presented. From Table 3 it is clear that each model has its own unique essence
and purpose, which affects the process of evaluating the quality of software
systems. The interaction matrix and interaction graphs allow the visualization of
the structure and interaction between the components of the software system,
which contributes to the identification of weaknesses and the analysis of the
complexity of interaction. Models of interaction, in turn, enable a mathematical
description of this interaction, which allows to carry out a deeper analysis and
forecasting of the behavior of the system.
It should also be noted that when choosing models to evaluate the quality of
software systems, it is important to consider their advantages and disadvantages,
as well as the context of the project and the specifics of the system. The combina-
tion of different models can be useful to obtain a more complete view of the sys-
tem’s state and test efficiency.
The constant integration method (CI) is a key practice in the field of soft-
ware development aimed at improving the quality of software systems. The main
idea of the CI is to regularly and automatically combine code changes in a com-
mon repository and perform automatic tests to check the correctness of these
changes. Below is a description of the constant integration method and its impact
on the quality assessment of software systems:
code changes automated: developers regularly make changes to the pro-
ject code. Thanks to the CI, these changes automatically merge (integrate) into the
main branch of the repository. This allows you to identify conflicts and other
problems before they get into the production environment;
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automatic tests: after each change of change, the CI system automatically
starts tests that check the functionality, performance, safety and other aspects of
the software system. This allows you to identify possible problems during the ear-
ly stages of development;
T a b l e 3 . Analysis of basic mathematical models involved in integration testing
and quality assessment of software systems
Mathematical
model Essence Appointment Advantages Disadvantages
Interaction
matrix
Representation of
interaction
between
components in the
form of a matrix
Visualization and
analysis of the
interaction structure,
identification
of “weak places”
Ease of use, quick
identification
of defects
Does not take
into account the
dynamics of
interaction, limited
in use for complex
systems
Interaction
graph
Representation
of interaction
between compo-
nents in the form
of a graph
Visualization and
analysis of complex
interaction, detection
of cycles and ways of
interaction
Detailed overview
of the interaction
structure, help with
architecture
analysis
Difficult to use
for large systems
Interaction
models
Mathematical
models describing
the interaction
between
components
Analysis and predic-
tion of interaction
between components,
identification and
elimination
of shortcomings
Deep interaction
analysis, possibility
of predicting sys-
tem behavior
Requires a large
amount of data and
computing
resources, difficult
to implement
Probability
models
Mathematical
models that
determine the
probability
of various events
Assessment of risks
and effectiveness of
testing, detection
and elimination of
shortcomings
Objective assess-
ment of efficiency
and risks, the
possibility of mak-
ing decisions based
on probability
It is difficult to
model all possible
scenarios and
factors affecting
the probability
Testing
models
Models describing
the process
of testing
a software
system
Organization of test-
ing, determination of
the most effective
approaches
and strategies
Systematization and
structuring of the
testing process, the
possibility of
choosing the best
approaches
Requires precise
definition of test-
ing parameters and
criteria, may not be
flexible enough in
some situations
constant feedback: if problems are identified during the performance of
tests, the developers receive notifications about it, which allows them to quickly
correct mistakes and improve the quality of the code;
automated deployment: some CI systems may automatically deploy
changes to the test or production environment after successful completion of the
tests. This allows you to quickly and effectively introduce new functionality and
correction of errors;
code quality metrics: some CI systems may include code analyzers and
quality metrics that automatically estimate the level of readability, efficiency and
other aspects of code quality.
The constant integration method allows you to create higher quality software
systems, reducing the time of detection and correcting errors, facilitating the joint
work of developers and increasing the reliability of software as a whole.
MODELING AND TESTING
In the range of modeling of a new complex model of quality assurance of soft-
ware systems, based on the combination of modular testing, integration testing
and constant integration methods, it is important to keep in mind a number of ini-
Use of methods and tools for ensuring software quality
Системні дослідження та інформаційні технології, 2025, № 3 71
tial assumptions. In particular, given the automation of processes in our case, it is
suggested that many stages of testing and integration can be automated. This
means that the creation and performance of tests can be carried out without sig-
nificant intervention.
Given the specifics of the modular testing, it is suggested that the individual
components of the program (modules) are tested in isolation from other compo-
nents to ensure that they work properly isolated from other parts of the program.
Given the specifics of integration testing, it is suggested that after successful
modular testing, the components of the program are gradually combined and test-
ed as a holistic system to believe that they are cooperating correctly.
Continuous delivery involves the assumption that the processes of correction
of errors, testing and release of software are automatically and continuously to
ensure rapid delivery of changes and updates.
Considering the needs of assembling the assembly and deployment at the
complex level, it is suggested that the processes of collecting the software code
and its automatic deployment into the test environment or the productive server
must also be automated.
Considering the need for automation tools, there is an assumption that there
are appropriate tools and technologies for the implementation of automated test-
ing, integration and constant integration processes. Such tools can include media
for testing, version control systems, automated testing tools and more. With the
above initial assumptions, you can start modeling and implementation of a new
complex model of quality assurance of software systems.
Mathematical apparatus for the developed new model of complex quality as-
surance of software systems, based on the combination of modular testing meth-
ods, integration testing and constant integration can be presented as follows.
1. Tasks of the modular testing procedure of the software system:
,uuu PNS
where uS — the overall success of modular testing; uN — the total number of
wise tests; uP — predicted probability of successful passage of one modular test.
2. Tasks of the procedure testing procedure:
,sii PNS
where iS — the overall success of integration tests; iN — the total number of
modular tests; iP — The probability of successful passage of one integration test.
3. Tasks of the continuous integration procedure:
CC TFC ,
where C — total number of committees (variable code); CF — frequency of
committees; CT — total time during which committees were carried out.
4. Tasks of the procedure of continuous integration and constant delivery:
CTCR /
where C — total number of committees (variable code); R — the speed of
releases, which is determined by the number of committees and time; СТ — the
total time during which committees were carried out.
5. The task of determining the quality of the software system:
RSSQ iu ,
where Q — The total quality of the software system.
A.S. Shantyr
ISSN 1681–6048 System Research & Information Technologies, 2025, № 3 72
Subsequently optimizing the mathematical apparatus for the model of com-
plex quality assurance of software systems, which is based on the combination of
modular testing methods, integration testing and constant integration, may include
the following characteristics and methods:
Code quality metrics: used to quantify the quality of the software code.
These metrics may include code coating tests, defects, test time, and more.
Statistical methods: used to analyze the results of errors, test efficiency and
system stability.
Probability and statistics theory: used to calculate the likelihood of defects
during testing, risk assessment and statistical significance of test results.
Optimization methods: used to increase the efficiency of testing and select
the optimal test strategies taking into account resource restrictions.
Machine learning and artificial intelligence algorithms: used to automate the
test analysis processes, identify anomalies and forecast possible problems.
These methods and tools allow you to create a complete and optimized com-
plex mathematical model to ensure the quality of software systems, which takes
into account the interaction of different test methods and their impact on the qual-
ity and reliability of the software.
Description of the algorithm of practical implementation of the
developed model. In practice, the implementation of the developed complex
model of quality assurance of software systems will include several steps and stages:
1. Planning and setting up the environment:
Setting up the version control system (GIT example) for constant integration.
Installation and adjustment of tools for automated testing (for the example
of JUnit for modular testing, Selenium for automated integration testing).
2. Creating tests:
Developing a set of modular tests for each component of the program.
Writing integration tests to check the interaction between components.
3. Setting up constant integration (PI):
Create configuration files for automatic assembly and testing after each
committee.
Setting up integration with the task management system or notification of
the developers about the results of the testing.
4. Performing tests and analysis of results:
Automatic performance of modular tests and integration tests after each
committee.
Analysis of test results and detection of errors or disadvantages in the code.
5. Control of versions and releases:
Management of software versions through the version control system.
Automatic software release based on test results and successful integration.
6. Monitoring and reporting:
Monitoring the work of the continuous integration system and test results.
Generation of code quality reports, testing and testing results for analysis
and improvement of the development process. This process requires the work of
developers, testers and operators, as well as the introduction of a number of tools
for automation of routine testing and integration tasks.
The developed model of quality assurance of software systems, which com-
bines modular testing, integration testing and constant integration, has several
useful advantages:
Improved software quality: this model allows you to identify and correct er-
rors in the early stages of development, providing high quality software.
Use of methods and tools for ensuring software quality
Системні дослідження та інформаційні технології, 2025, № 3 73
Reducing the time for detecting and correcting errors: constant integration
and continuous delivery allow you to identify and correct problems quickly, which
reduces the time of development and improves the speed of product production.
Testing and release automation: through automated software testing and re-
lease processes, the risks of human errors can be reduced and the project stability
can be reduced. Improving development efficiency: developers can focus on writ-
ing code, as many routine tasks (testing, assembly, deployment) are automated.
Reduced testing costs and releases: automated quality assurance processes save
time and money that is usually spent on manual testing and releases.
Increasing user confidence: high product quality and renewal speed helps to
improve users’ reputation and trust.
In general, this model helps to make the software development process more
efficient, faster and reliable, which is critical in the modern fleeting world of
software development.
In Table 4 the results of practical testing of plowing methods and the developed
model in the analy sis of the quality of the application “task management system”.
T a b l e 4 . Analysis of comparison of software testing methods in the analysis
of the quality of the application “task management system”
Features Modular
testing
Integration
testing
Continuous
integration
Combination of methods
(unit testing + Integration + PI)
Execution time 5 hours 8 hours 12 hours 10 hours
Number
of detected errors
10 50 30 70
Level of automation High Average High High
Stability of releases High average High High
Test coverage
(percentage)
90% 70% 95% 97%
Stability (scale 1-10) 9 7 8 8
From Table 4 it is observed that software quality testing by the developed
method takes 10 hours, which is less than continuous integration and close to the
average time required for integration testing. Thus, in terms of execution time, the
combined method is quite efficient. Analyzing the number of errors detected: the
combined method detects 70 errors, which is more than any other testing method.
This indicates its high efficiency in detecting errors in software. Analyzing the
level of automation: the combined method has a high level of automation, which
allows for efficient testing without significant human involvement. Analyzing
release stability and stability: the combined method has a high level of release
stability, which is equal to the release stability obtained with continuous integra-
tion. This is important to ensure software quality. Analyzing test coverage: the
combined method has the highest rate of test coverage — 97%. This indicates that
it tests more parts of the software and detects more possible problems. So, from
the standpoint of comparison with existing testing methods, the combined method
(Unit Testing + Integration + PI) is very effective, as it combines the advantages
of different approaches and ensures high quality and stability of the software. In
turn, it should be noted that in order to obtain the results of practical testing of the
described methods and the developed quality model during the quality analysis of
the “task management system” application, various tools were used, including:
Automated test frameworks: popular test frameworks were used for module test-
ing and integration testing, such as like JUnit, NUnit, or PyTest. The above-
mentioned test frameworks allow you to automate the execution of tests and ana-
lyze their results.
A.S. Shantyr
ISSN 1681–6048 System Research & Information Technologies, 2025, № 3 74
Systems for continuous integration: tools such as Jenkins, Travis CI, or Git-
Lab CI were used for continuous integration. These systems allow you to automate
the process of building, testing, and deploying software with each code change.
Code quality monitoring and analysis: code quality monitoring tools such as
SonarQube or CodeClimate were used. These tools allow you to identify potential
problems in your code, such as code duplication, improper use of variables, and
other anomalies. Resource Usage Analyzers: profilers and performance analyzers
such as VisualVM have been used to analyze the application’s resource usage,
and YourKit is also possible. These tools allow you to identify performance and
memory usage issues in an application. Bug trackers: used bug trackers like Jira,
Bugzilla, GitHub Issues to track the issues found and fix them later. These tools
contributed to obtaining practical results of testing and quality analysis of the
“task management system” application using the described testing methods and
the developed quality assurance model. The application of this strategy and ap-
proaches is quite expedient in terms of improving configuration management pro-
cesses and identifying vulnerabilities in software systems.
CONCLUSIONS
The analysis of the current state of the issue raised in the article showed that an
important task is to further improve the methods and means of quality assurance
of software systems in the context of rapid technology development. Research
confirms that effective methods and quality assurances are a key element of suc-
cessful development of software systems. In the course of modeling, a new com-
plex model of quality assurance of software systems was developed, which is
based on the combination of modular testing methods, integration testing and
constant integration. The advantage of this development is to increase its adapta-
tion to solving the main tasks for the quality of software systems. The results of
practical research have shown that the integrated use of methods, such as modular
testing, integration testing and continuous integration, better facilitates the detec-
tion and correction of errors in the early stages of software development. The pro-
spects for further research are to improve existing techniques, as well as to study the
latest technologies that can help improve the quality of software in the future.
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Received 04.04.2024
INFORMATION ON THE ARTICLE
A.S. Shantyr, ORCID: 0000-0002-0466-3659, State University of Information and
Communication Technologies, Ukraine, e-mail: shantyr.a@duikt.edu.ua
ВИКОРИСТАННЯ МЕТОДІВ ТА ІНСТРУМЕНТІВ ДЛЯ ЗАБЕЗПЕЧЕННЯ
ЯКОСТІ ПРОГРАМНИХ СИСТЕМ / А.С. Шантир
Анотація. Запропоновано розгляд ефективних методів та засобів для забезпе-
чення якості програмних систем. У спектрі даної тематики розглянуто актуа-
льні питання, пов’язані із забезпеченням якості програмних систем у межах
реалізації аналізу методів та засобів, які застосовуються на практиці для забез-
печення розроблення високоякісного програмного забезпечення. У ході моде-
лювання розроблено нову комплексну модель забезпечення якості програмних
систем, яка ґрунтується на поєднанні методів модульного тестування, інтегра-
ційного тестування та постійного інтегрування. Перевагою цієї розробки є під-
вищення її адаптування до вирішення основних завдань із забезпечення якості
програмних систем. На базі розробленої моделі запропоновано стратегії та
підходи щодо вдосконалення процесів управління конфігурацією та виявлення
вразливостей програмних систем.
Ключові слова: комплексна модель, стандарти якості, інтеграційне тестуван-
ня, модульне тестування, технологічні виклики, інтеграційне тестування, не-
перервне інтегрування.
|
| id | journaliasakpiua-article-301260 |
| institution | System research and information technologies |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2025-11-09T02:11:02Z |
| publishDate | 2025 |
| publisher | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
| record_format | ojs |
| resource_txt_mv | journaliasakpiua/ba/c05b23fe61b662ec3f1174fa98e6d1ba.pdf |
| spelling | journaliasakpiua-article-3012602025-11-09T00:01:30Z Use of methods and tools for ensuring software quality Використання методів та інструментів для забезпечення якості програмних систем Shantyr, Anton comprehensive model quality standards integration testing unit testing technological challenges continuous integration комплексна модель стандарти якості інтеграційне тестування модульне тестування технологічні виклики неперервне інтегрування This paper proposes an examination of effective methods and tools for ensuring software quality. The scope of this topic includes current issues related to software quality assurance within the context of analyzing methods and tools used in practice to develop high-quality software. During the modeling process, a new comprehensive model for software quality assurance has been developed, combining modular testing, integration testing, and continuous integration methods. The advantage of this development is its enhanced adaptability to addressing key challenges in software quality assurance. Based on the developed model, strategies and approaches are proposed to improve configuration management processes and identify vulnerabilities in software systems. Запропоновано розгляд ефективних методів та засобів для забезпечення якості програмних систем. У спектрі даної тематики розглянуто актуальні питання, пов’язані із забезпеченням якості програмних систем у межах реалізації аналізу методів та засобів, які застосовуються на практиці для забезпечення розроблення високоякісного програмного забезпечення. У ході моделювання розроблено нову комплексну модель забезпечення якості програмних систем, яка ґрунтується на поєднанні методів модульного тестування, інтеграційного тестування та постійного інтегрування. Перевагою цієї розробки є підвищення її адаптування до вирішення основних завдань із забезпечення якості програмних систем. На базі розробленої моделі запропоновано стратегії та підходи щодо вдосконалення процесів управління конфігурацією та виявлення вразливостей програмних систем. The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2025-09-29 Article Article application/pdf https://journal.iasa.kpi.ua/article/view/301260 10.20535/SRIT.2308-8893.2025.3.05 System research and information technologies; No. 3 (2025); 60-75 Системные исследования и информационные технологии; № 3 (2025); 60-75 Системні дослідження та інформаційні технології; № 3 (2025); 60-75 2308-8893 1681-6048 en https://journal.iasa.kpi.ua/article/view/301260/330990 |
| spellingShingle | комплексна модель стандарти якості інтеграційне тестування модульне тестування технологічні виклики неперервне інтегрування Shantyr, Anton Використання методів та інструментів для забезпечення якості програмних систем |
| title | Використання методів та інструментів для забезпечення якості програмних систем |
| title_alt | Use of methods and tools for ensuring software quality |
| title_full | Використання методів та інструментів для забезпечення якості програмних систем |
| title_fullStr | Використання методів та інструментів для забезпечення якості програмних систем |
| title_full_unstemmed | Використання методів та інструментів для забезпечення якості програмних систем |
| title_short | Використання методів та інструментів для забезпечення якості програмних систем |
| title_sort | використання методів та інструментів для забезпечення якості програмних систем |
| topic | комплексна модель стандарти якості інтеграційне тестування модульне тестування технологічні виклики неперервне інтегрування |
| topic_facet | comprehensive model quality standards integration testing unit testing technological challenges continuous integration комплексна модель стандарти якості інтеграційне тестування модульне тестування технологічні виклики неперервне інтегрування |
| url | https://journal.iasa.kpi.ua/article/view/301260 |
| work_keys_str_mv | AT shantyranton useofmethodsandtoolsforensuringsoftwarequality AT shantyranton vikoristannâmetodívtaínstrumentívdlâzabezpečennââkostíprogramnihsistem |