=Paper=
{{Paper
|id=Vol-3188/paper1
|storemode=property
|title=Modern Approaches to Software Optimization Methods
|pdfUrl=https://ceur-ws.org/Vol-3188/paper1.pdf
|volume=Vol-3188
|authors=Igor Zhukov,Oleksii Synelnikov,Olena Chaikovska,Serhiy Dorozhynskyi
|dblpUrl=https://dblp.org/rec/conf/cpits/ZhukovSCD21
}}
==Modern Approaches to Software Optimization Methods==
https://ceur-ws.org/Vol-3188/paper1.pdf
Modern Approaches to Software Optimization Methods
Igor Zhukov1, Oleksii Synelnikov1, Olena Chaikovska2, and Serhiy Dorozhynskyi1
1
National Aviation University, 1 Liubomyra Huzara ave., 03058, Kyiv, Ukraine
2
Kyiv National University of Culture and Arts, 36 Y. Konovaltsya str, 01601, Kyiv, Ukraine
Abstract
Lately in the world some optimization methods have been developed surpassing traditional
mathematical programming methods. These methods belong to the category of modern or
unconventional optimization methods. Most of these methods are based on specific
characteristics and behavior of the systems to which they are applied. This article provides a
description of some of the common techniques used in many modern industries, not only in the
traditional computing but also in everyday life areas. Most of these methods have been
developed only in recent years and are becoming popular for solving complex engineering
problems studying test object functions.
Keywords1
Optimization approach, optimization methods, software optimization problem overview,
architecture optimization.
1. Introduction
Dependence of software and equipment complexes using is steadily growing worldwide, which
makes it more useful in all spheres of everyday life. Ukraine also observed persistent annual increase
in the number of used software-hardware systems. In the conditions of the digital transformation for all
spheres in Ukraine, this is the first from the key goals of the Ministry Education and Science, as it is the
actual direction, not only because of the pandemic but also through global present day trends and
national policies regarding the vector of digital transformation in whole country. Cost increasing and
implementing systems complexity of the complexes themselves does not always allow to solve the tasks,
since even the slightest specification inaccuracies or shortcomings allowed during the development
process can lead to a decrease in the software performance. Thus, software optimization requires the
most optimal approaches at all development stages even on testing and product implementation.
Resources using is important criteria for the software complexes effectiveness [1].
A lot of researchers mentioned about impossibility to apply single formulation procedure for all
engineering design problems, since the objective in a design modeling problem and associated therefore,
design parameters vary product to product different techniques are used in different problems. In the
meantime purpose of formulation is to create a mathematical model of the optimal design problem,
which then can be solved using an optimization algorithm. Figure 1 shows an outline of the steps usually
involved in an optimal design formulation.
2. Optimization Model
Different point of view describes constructing optimization model process determining using steps
for it.
CPITS-II-2021: Cybersecurity Providing in Information and Telecommunication Systems, October 26, 2021, Kyiv, Ukraine
EMAIL: zhukov.ihor@sfk.nau.edu.ua (I. Zhukov); oleksii.synelnikov@npp.nau.edu.ua (O. Synelnikov); dorozhun1706@gmail.com
(S. Dorozhynskyi); lena@knukim.edu.ua (O. Chaikovska)
ORCID: 0000-0002-9785-0233 (I. Zhukov); 0000-0002-4073-2594 (O. Synelnikov); 0000-0002-5395-6423 (S. Dorozhynskyi); 0000-0001-
7769-1004 (O. Chaikovska)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
1
The first step in the optimization process is constructing an appropriate model. Modeling is the
process of identifying and expressing in mathematical terms the objective, the variables, and the
constraints of the problem.
1. An objective is a quantitative measure of the performance of the system that we want to
minimize or maximize. In manufacturing, we may want to maximize the profits or minimize the cost of
production, whereas in fitting experimental data to a model, we may want to minimize the total deviation
of the observed data from the predicted data.
2. The variables or the unknowns are the components of the system for which we want to find
values. In manufacturing, the variables may be the amount of each resource consumed or the time spent
on each activity, whereas in data fitting, the variables would be the parameters of the model.
3. The constraints are the functions that describe the relationships among the variables and that
define the allowable values for the variables. In manufacturing, the amount of a resource consumed
cannot exceed the available amount.
Having all parameters considered from different sides we tend to draw some best-practice steps in
the optimization process classified for optimization model, since algorithms for solving optimization
problems are tailored to a particular type of problem. An example with basic information for the various
optimization problem types list is as following [2,3].
Figure 1: Solution diagram [4]
● Continuous Optimization versus Discrete Optimization. Some models only make sense if the
variables take on values from a discrete set, often a subset of integers, whereas other models contain
variables that can take on any real value. Continuous optimization problems tend to be easier to solve
than discrete optimization problems; the smoothness of the functions means that the objective function
and constraint function values at a point x can be used to deduce information about points in a
neighborhood of x. However, improvements in algorithms coupled with advancements in computing
technology have dramatically increased the size and complexity of discrete optimization problems that
can be solved efficiently. Continuous optimization algorithms are important in discrete optimization
because many discrete optimization algorithms generate a sequence of continuous subproblems.
● Unconstrained Optimization versus Constrained Optimization. Another important distinction is
between problems in which there are no constraints on the variables and problems in which there are
2
constraints on the variables. Unconstrained optimization problems arise directly in many practical
applications; they also arise in the reformulation of constrained optimization problems in which the
constraints are replaced by a penalty term in the objective function. Constrained optimization problems
arise from applications in which there are explicit constraints on the variables. The constraints on the
variables can vary widely from simple bounds to systems of equalities and inequalities that model
complex relationships among the variables. Constrained optimization problems can be furthered
classified according to the nature of the constraints (e.g., linear, nonlinear, convex) and the smoothness
of the functions (e.g., differentiable or non-differentiable).
● None, One or Many Objectives. Most optimization problems have a single objective function,
however, there are interesting cases when optimization problems have no objective function or multiple
objective functions. Feasibility problems are problems in which the goal is to find values for the
variables that satisfy the constraints of a model with no particular objective to optimize.
Complementarity problems are pervasive in engineering and economics. The goal is to find a solution
that satisfies the complementarity conditions. Multi-objective optimization problems arise in many
fields, such as engineering, economics, and logistics, when optimal decisions need to be taken in the
presence of trade-offs between two or more conflicting objectives. For example, developing a new
component might involve minimizing weight while maximizing strength or choosing a portfolio might
involve maximizing the expected return while minimizing the risk. In practice, problems with multiple
objectives often are reformulated as single objective problems by either forming a weighted
combination of the different objectives or by replacing some of the objectives by constraints.
● Deterministic Optimization versus Stochastic Optimization. In deterministic optimization, it is
assumed that the data for the given problem are known accurately. However, for many actual problems,
the data cannot be known accurately for a variety of reasons. The first reason is due to simple
measurement error. The second and more fundamental reason is that some data represent information
about the future (e. g., product demand or price for a future time period) and simply cannot be known
with certainty. In optimization under uncertainty, or stochastic optimization, the uncertainty is
incorporated into the model. Robust optimization techniques can be used when the parameters are
known only within certain bounds; the goal is to find a solution that is feasible for all data and optimal
in some sense. Stochastic optimization models take advantage of the fact that probability distributions
governing the data are known or can be estimated; the goal is to find some policy that is feasible for all
(or almost all) the possible data instances and optimizes the expected performance of the model [5,6].
There are some approaches improving problems solving quality with the allocation of relevant
computing systems resources. Algorithm verification for solving the most computational task reduces
its solution time, which also has a positive effect on performance as a whole. For selecting one or another
approach, scientific research is proposed to make a decision on the basis of a possible type of variable
model values, thus correlating optimization problems as discrete or continuous.
Continuous optimization problems concern the case when models variables can take any value
allowed by some specified limitations. At the same time, the method of discrete optimization
(combinatorial) uses combinational variables related to a discrete set (from integers subset) of values.
Discrete optimization is aimed at problems associated with the optimal choice solution from the final
possibilities number. In the discrete optimization, rely on the correct solution is possible only when in
the selected solutions there is a deliberately correct result [7].
Heuristic approaches have an advantage in the form of a solution rate at the expense of
approximation, but only in cases where the exact solution exists. These approaches show almost optimal
solutions on a certain time, which gives an increase in the solution rate. Although purely heuristic
optimization is designed to solve a specific task, metabolic can be applied to a wider range of tasks.
They can be used to find an almost optimal solution for optimization problem based on the possible
knowledge of calculation area.
3. Optimization Technique
Generally speaking, the optimization techniques in used approaches with specific context described
in Table 1. However, this is a technically complex and almost resource-proof procedure, and those
obtained in this way may not often be true and requiring additional time for a real solution. One of the
3
machine learning founder gives to world significant contribution to the development and theoretical
analysis of concepts and technique such as multi-class classification, semi-supervised learning, latent
factor models, and sequential models. Machine training is a technique that allows you to calculate
systems for learning (ie, improved) from experience (available data). Programs for machine learning
method are based on creating a prediction model from a set of preparatory data, which are subsequently
used to create predicted data, and not after preliminary static instructions. You can select the most used
machine learning algorithms for that: regression, solutions tree and artificial networks. Software
optimization is possible only during the life cycle of the software itself. Optimization techniques can be
classified as follows:
1. Message headers (System Memory Savings);
2. Variables initiation (assigning variable variables at the same time with their ad);
3. Reducing variables number (cleaning temporary variables after their use);
4. Select data types (streamlining the simultaneous use of different data types);
5. Removing unnecessary assignment operators (integration of assignment operators);
6. identifying variables (replacement of variables by another variable or numerical variable value);
7. Removing identical operators (may appear in the optimization process);
8. Elimination of non-fulfilled operators (which are not performed for any sets of initial data);
9. I/O using (excluding this type of operations due to their duration);
10. Selecting procedures (functions) (transformation sequence of identical operators in the procedure
or function);
11. Reducing the number of procedures (functions) (allocation to a separate procedure of operators
that run many times);
12. Alternatives (streamlining conditions-in order to descend their probability to be true);
13. Arithmetic operations (replacement of operations from different speeds);
14. Transformation of expressions (preliminary simplification of expressions);
15. Optimization of expressions (replacement of complex semantically equivalent subsections per
simple);
16. Pre-calculation of arithmetic subsection (reducing the number of operations performed);
17. Elimination of unnecessary brackets (eliminate pairs of brackets in arithmetic or logical
expressions);
18. Elimination of extra labels (removing labels in front of operators to which there is no control
transmission);
19. Elimination of unnecessary transition operators (removal of unconditional transition operators
with a non-existent label);
20. Implicit operations (simplifying operators with frequently used variables);
21. Cleaning cycles (reduces the cycle time by removing arithmetic expressions from its body,
independent of the cycle control variable);
22. Using cycles (replacement not repeatedly repeated sequences of operators on successive);
23. Combining cycles (connecting multiple cycles with the same headlines);
24. Cycle loop branches removal (reduce validation checks number);
25. Deleting empty cycles (eliminating empty cycles saves the program execution time);
26. Cycles compression (body cycle logical transition operators removal);
27. Management selection (the location commands group in selection operator, descending the
frequency using probability) [8,9].
However, in practice few are used.
4
Table 1
Optimization techniques [10]
Method Features Disadvantages
Heuristic The practical ability to find new No guarantee for better
unexplored solutions solution
Incorrect decision in some cases
No guarantee for solution even
in case his existing
Metaheuristic Applicable in cases with complete High computational using costs
absence studied object properties
Allow find solutions without correctness
proof
Machine Workaround solution, learning due Select optimal starting point
Learning applying a plurality on a set of similar
problems
Another equally important part of software optimization is the right choice of architecture. The
quality of the architectural solution evaluated by a set of criteria, and his choice is always a compromise
because for given set of functional requirements and quality requirements there is no obvious one the
best solution and improvement of some indicators leads to the deterioration of others and vice versa.
And because here you need to take into account relationships between architecture quality criteria and
PS, the initial task must be formulated as a multi-criteria hierarchical task optimization. The most widely
used are two standard approaches to quality assessment architecture. The first approach is evaluation of
its characteristics by the method simulation. For example, for performance simulation results estimated
bandwidth and scalability of the system. In other cases, connectivity and adhesion characteristics used
to assess ability to changes and convenience of system maintenance. In the methods of the second
approach architecture characteristics are evaluated by the results of a survey of experts. For example, in
the ATAM method (Architecture Trade-off Analysis Method) risks are assessed that the architecture
does not satisfy conceptual requirements. Conceptual requirement of one of the interested parties
experiment in the ATAM technique is described with using scripts. Then alternative architecture options
are analyzed for the subject support for each of the scenarios. The method of analysis of Saati hierarchies
allows get relative estimates of architectures as per to each criterion, and on their set. However, because
MAI is an expert technology in essence, then obtained solutions may be unstable to inconsistencies of
matrices of pairwise comparisons, and also to change the priorities of quality criteria. Changing the
requirements for the aircraft leads to a change prioritize quality criteria as a system in general, and its
architecture. To detect critical decisions and analysis sensitivity of decisions to changes in requirements
is necessary perform analysis of conflicts and possible compromises between criteria. It will give
opportunity to identify potential problems and ensure the adaptation of the architecture to future changes
in requirements. Issues of sensitivity in methods architecture evaluation was the first delivered in. But
they did not provide no solutions to this problem. This is partial due to what they used quantitative
evaluation methods, such as ATAM. The approach proposed in the article is an additional analysis results
of the method of analysis of hierarchies. As example of the study of the sensitivity of solutions to
changing requirements and analyzing trade-offs between quality criteria were held for architectures
taken from the GlasBox project [11,12].
One of the most important methods of optimizing the workflow in software development is the use
of cluster techniques. The movement of flexible Agile techniques is revolutionary. Teams that use this
technology systematically report improvements (sometimes abrupt) in the ability to create better
Software. Those who have successfully implemented Agile create high-quality products and do so faster
than before. Agile has turned from an outsider into a working institute. Currently, there is almost none
left Doubts that agile methodologies are an effective way to create software. In 2008 it was conducted
a study, which showed that more than half of all surveyed teams involved software development, use
agile methodologies, practices or principles. Traditionally, companies have used software development
projects Waterfall approach, according to which the team first determines the requirements for the
5
product, plans a project in in general, develops a software solution and then generates code and tests the
product. Much of the software provision - both grand and very small - for years was created in this way.
However, for decades, different teams in different companies have faced the same problems. And some
of them suspected that the main cause of failure - the Waterfall approach itself. The use of Canban to
date is this the most common method (and effective for many agile practitioners) is the introduction of
frugal thinking organization [13].
When using flexible techniques are guided by 12 principles of flexible software development:
1. Our highest priority is customer satisfaction through frequent and continuous deliveries valuable
software for him.
2. Changes in requirements are accepted even at later stages of project implementation. Agile
processes allow use the changes to increase the competitiveness of the product.
3. The desire to supply fully working software every few weeks, at the very least case - every few
months. The more often, the better.
4. The most effective and efficient way to transfer information is to meet the members of the software
development team.
5. Business representatives and the development team should work on the project together.
6. Projects are built around motivated people. It is necessary to create a suitable environment for
them environment, provide everything you need and entrust to do your job.
7. Working software is the main measure of project progress.
8. Flexible processes contribute to continuous development. Sponsors, developers and users should
be able to maintain a constant pace of work indefinitely.
9. Constant attention to technical excellence and quality architecture promotes flexibility.
10. Simplicity represents for the art team not to do unnecessary work.
11. The best architecture, requirements and design are created in self-organized teams.
12. The team is constantly looking for ways to become more effective by adjusting and correcting
their actions [14].
The first principle includes three important conditions: early release of the software product,
continuous realization of value and customer satisfaction. To understand the essence of this principle,
you need to know how these three ideas work together. Project teams exist in the real world, where
nothing perfect happens. Even a brilliantly working team will be missing something when collecting
and recording project requirements, because it is impossible to fully and accurately display the
requirements for each system. That doesn't mean teams shouldn't try, but agile-methodologies are based
on unprecedented methods of communication and fixation of requirements. But, until customers get
their hands on working software, it's hard for them to imagine exactly how it will work [15,16].
In this case, if you want to receive from the client a real, reasonable return communication after
viewing the working software product, it is best to do so through early delivery: "Ship" to the customer
the first working version of the software as soon as possible. Even if implemented only one work
function that the client can use - is to achieve some success for the team. Because now the consumer is
able to provide feedback that will help move the project in the right direction. It is also a success for
customers because by having this work program, they can realize that previously only planned. We can
say that the team has created real value, because the software works and customers can use it. Let this
be only a small part of what the consumer needs, but still it is better than waiting without software,
especially if the alternative is to wait a long time before receiving software product. The disadvantage
of early delivery is that the software is far from perfect. It is a challenge for some customers and
stakeholders: if some users are used to thinking that see the software early, it is harder for others to get
used to the idea. Many employees very much experience when their software is not perfect. In
companies (especially large ones, where years are spent working with teams that develop software)
these teams must agree very carefully on terms of software delivery to interested parties. In the absence
of a partnership between the customer. The incoming software product supplied by the development
team can be evaluated strictly and even terrify the user if it really doesn't live up to expectations. Basic
agile values solve similar ones question: cooperation with the customer is more important than agreeing
on the terms of the contract. A team tightly tied to specifications, cannot make changes to the software
during its creation. Working in such conditions, a new process of total change management needs to be
launched, which will require new negotiations under the contract with the customer. But the team that
really works with customers has the opportunity to make all the necessary changes during the work [17].
6
That's what uninterrupted delivery means. That is why flexible methodologies are usually iterative.
Agile commands plan iterations of the project by selecting indicators and requirements that ensure
maximum return. Only the way in which the team can find out which indicators implement this value-
cooperation with client and embedding feedback from a previous iteration. This allows the team to
satisfy requests consumer in the short term, demonstrating the value of the product at an early stage
cooperation, and in the long run to provide him with a finished product that has all possible valuable
qualities [18].
The team uses iterations to break the project into pieces with regular delivery deadlines. During
iterations team supplies the working software. At the end of each iteration, the team conducts a
demonstration, showing the customer the created product as well as previous options to see what lessons
can be learned from this situation. Then begin a planning session to find out what they will create next
iteration. A predictable schedule and constant checkpoints help the team keep track of these changes
early on stages and create an atmosphere in which it is not customary to look for the culprit when
everyone can discuss change and agree on a strategy to include it in the project.
At this point, Agile becomes attractive to the traditional command-and-control manager project.
Such a manager wants to control the term. Setting limits on the duration of iterations gives it this
possibility. In addition, one of the main tasks of the manager is solved - to work with the changes that
occur at the very end of the project.
One of the most difficult moments in the work of a traditional manager is the monitoring of changes.
Daily reports and retrospectives of iterations allow the project manager to gain the support of all team
members, because they are now the eyes and ears of the leader, helping to identify the need for change
before they become the cause more serious problems in the project. The role of the project manager is
to initiate the replacement of the command-and-control system, in which he needs to give tasks to team
members on a daily basis and constantly adjust the work plan to direct them in the right direction. Now
the manager interacts with the team just to make sure: each specialist sees a single picture and works on
common goals. The easiest way to do this is when the team is running in short iteration modes that allow
you to deliver work Software. This sets specific goals for each participant and gives him a
comprehensive idea of what working team, as well as a sense of responsibility not only for their work,
but also for the overall result after end of iteration [19].
Kanban is a method of improving the processes used by flexible teams. Teams that apply it, begin to
understand how they create software, and gradually improve him. Kanban, like Scrum and XP, affects
the way a person thinks. In particular, it requires thrift thinking. In turn, Lean is a way of thinking,
values and principles. Teams using Kanban began with the application of the worldview of Lean. This
provides a solid foundation, which in combination with Kanban gives opportunity to improve processes.
When teams use Kanban to improve, they are focused to eliminate losses from processes. Kanban is a
technological term adapted by David Anderson for software development.
Here is how he describes his relationship with Lean in his book Kanban. An alternative path in Agile.
Kanban method is a complex adaptive system designed to activate lean solutions within organizations.
There are teams that use Lean and Thrifty thinking to develop programs without using them kanban, but
today it is the most common method (and effective for many agile practitioners) introduction of frugal
thinking in the organization. Kanban differs from flexible methodologies such as Scrum and XP. Scrum
is mainly focused on project management: the amount of work to be done, that this work was done, and
the result needed by users and stakeholders. XP is focused on Software development, its values and
practices are built around creating an enabling environment for developing and forming habits that help
the developer write simple and easily modifiable code. Kanban helps the team improve software
development methods. The team that uses this method, has a clear idea of what actions it does when
creating a software product, how it interacts with another the company, how losses are caused by
inefficiency and unevenness, and how over time rectify the situation by eliminating the root cause of
these losses. When a team improves the way they create software, it is traditionally called the process
of improvement. Kanban is a good example of the application of flexible ideas (such as a last resort) to
create a process improvement method that teams can easily adopt [20].
CANBAN practices allow to stabilize and improve the software development system software. An
up-to-date list of basic practices can be found in the Kanban Yahoo! First, it should adhere to the basic
principles:
• Start with what you are doing now, keep existing roles, responsibilities and job descriptions.
7
• Agree on evolutionary development.
• Encourage leadership at all levels.
Then you should apply the basic practices: visualization; workflow limit (WIP); flow management;
make the rules clear; enter feedback loops; develop together and experiment (using modeling or a
scientific approach). At the initial stage, it is not necessary to implement all six practices. Partial
implementation is recognized superficial and involves a gradual deepening, as most practices are
accepted and performed with great potential. Kanban is not a software development methodology or a
project management system. One of the most common mistakes at the beginning of studying CANBAN
is to try to use it as methodology for software development.
Kanban is a way to improve processes. It will help to understand the technique used, and find ways
to improve it. Kanban does not operate with processes, but with working elements. It's called that a
stand-alone unit of work that needs to be tracked throughout the system. Typically, the requirements are
designed for user history or a similar fragment of the general pool of works. There is a difference
between a task board and a kanbandoshka: while tasks move across the task board, work items are not
tasks. Task - this is what people do to move the working elements within the system, ie these are the
“cogs” of the mechanism, which pushes the working elements [21, 22].
That is why you can use systems thinking to understand the workflow when creating software,
without degrading the human dignity of developers by thinking that they are part of some cars. Tasks
are “cogs,” and people are unique individuals with their own character, desires and motivation. The
columns on the kanban board may seem like stages in the value stream. However, many kanban experts
share the concepts of “value systematization” and “kanbandoshka.” They reflect on the board the state
of the working elements in the workflow regardless of the flow values and call it a life cycle map. The
difference is that the value creation stream is a tool careful thinking, helps to understand the operation
of the system, and the life cycle map is like a kanban method identifies the specific stages that each
working element goes through [23,24].
Teams continue to deliver functionality, they identify workflow issues and adjust the WIP limit so
that the feedback loops provide a sufficient amount of information, no causing slips. Flow is the speed
at which working elements move through the system. When the team finds the optimal pace for delivery
combined with the convenient feedback it has maximizes flow. Reduction of uneven work and
overloads, the ability to complete one task and only then move on to the next increases flow. When due
to uneven work accumulate task, this interrupts the workflow and reduces the flow. The Kanban team
uses flow management practices by measuring and doing active steps to improve it. You already know
how inspiring it is when the work progresses. You feel that you have time to do a lot, do not waste time
and do not wait for someone else to finish their part of the work and will give it to you. When you work
in a team with a large flow, you constantly feel that you are doing something valuable. Everyone aspires
to this. You also know how it is when work stalls. It feels like you're booted into mud and can barely
move. It seems like you're always waiting for someone to finish creating what you need, or make a
decision that affects work, agree on a card, or find something else that interferes with work - even if you
know that no one is intentionally interfering. You feel inconsistency and inconsistency and spend a lot
of time explaining why you are waiting. This does not mean that the team is not enough loaded, maybe
people are laid out 100%, and maybe even overwhelmed. But while the plan of the project says that the
work is done by 90%, it seems to you that, on the contrary, you still need to do 90% work. An effective
tool for measuring flow - cumulative flow diagram (cumulative flow diagram, CFD). CFD is similar to
a WIP chart, but has one important difference: instead of disappearing, working elements accumulate in
the bottom bar of the chart. And if on the WIP-diagram strips correspond to states in the value creation
flow chart, the CFD (and WIP chart) in this section has the bands corresponding to the columns on
kanban board [25].
8
4. Conclussions
In general, most of mentioned methods give various improvements in productivity and speed, but
there are a lot of issues that require improvements and improvements. Among the disadvantages can be
allocated: requirement to use additional comments in the code, restriction on a specific programming
language or model or ability to use only one of the optimization techniques.
There is exigency for software optimization that is applicable to a large spectrum of programs and
systemic architecture. Many existing solutions are limited unfortunately to a specific programming
language and model, application type or system architecture. Future efforts should focus on developing
solutions for widely used general-purpose languages.
5. References
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odniyeyu-z-klyuchovih-cilej-mon-na-2021-rik-sergij-shkarlet.
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shchodo-rozvitku-sistemi-e-poslug.
[4] Memorandum Mikhail Fedorov from Ministry and Digital Transformation Committee of
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metodichnikh-rekomendatsiy-shchodo-rozvitku-sistemi-e-poslug.
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