Научная статья на тему 'OPTIMIZATION OF PERFORMANCE AND RELIABILITY OF FINANCIAL APPLICATIONS THROUGH INNOVATIVE TESTING METHODS'

OPTIMIZATION OF PERFORMANCE AND RELIABILITY OF FINANCIAL APPLICATIONS THROUGH INNOVATIVE TESTING METHODS Текст научной статьи по специальности «Экономика и бизнес»

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financial applications / artificial intelligence / machine learning / optimization algorithms / test automation / innovative testing methods / performance engineering.

Аннотация научной статьи по экономике и бизнесу, автор научной работы — Bobunov A.Yu.

This article explores innovative testing methods (TM) for optimizing the performance and reliability of financial applications. It examines the limitations of traditional testing and places a special emphasis on the application of artificial intelligence and machine learning, as well as test automation using optimization algorithms. Examples of successful implementation of innovative TM in major financial institutions are provided.

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Текст научной работы на тему «OPTIMIZATION OF PERFORMANCE AND RELIABILITY OF FINANCIAL APPLICATIONS THROUGH INNOVATIVE TESTING METHODS»

UDK 004.415.53

Bobunov A.Yu.

bachelor's degree,

Moscow University for Industry and Finance "Synergy",

Moscow, Russia

OPTIMIZATION OF PERFORMANCE AND RELIABILITY OF FINANCIAL APPLICATIONS THROUGH INNOVATIVE TESTING METHODS

Abstract

This article explores innovative testing methods (TM) for optimizing the performance and reliability of financial applications. It examines the limitations of traditional testing and places a special emphasis on the application of artificial intelligence and machine learning, as well as test automation using optimization algorithms. Examples of successful implementation of innovative TM in major financial institutions are provided.

Keywords

financial applications, artificial intelligence, machine learning, optimization algorithms, test automation,

innovative testing methods, performance engineering.

Introduction

Financial applications (FA) handle sensitive data and transactions critical to the economic activities of individuals and organizations alike. However, ensuring their reliability and optimal performance poses significant challenges, particularly as the complexity and scope of financial services continue to expand. The goal of this study is to explore the optimization process of FA. It highlights innovative testing methods (TM) and the integration of technologies such as artificial intelligence (AI) and machine learning (ML).

Main part. Traditional TM

Understanding standard TM is important for evaluating their efficacy and identifying their shortcomings. Unit tests and integration tests are foundational in conventional software testing paradigms:

• Unit tests focus on individual components of an application, ensuring each part functions correctly in isolation. This method allows developers to identify and fix errors at an early stage, which simplifies debugging during later stages of development [1].

• Integration tests verify the interaction between these components, ensuring they work together seamlessly.

These traditional TM encounter several limitations. A primary concern is their inability to predict system behavior under realistic operational conditions. This can lead to unexpected performance issues when the FA is deployed, as tests performed in controlled environments do not always capture the complexities of real-world usage. Maintaining extensive suites of unit and integration tests requires significant manual effort, which is not only time-consuming but also susceptible to human error.

Another drawback is the difficulty in scaling these methods to match the increasing complexity of FA. This can leave critical vulnerabilities undetected, posing significant risks, especially in FA where security and reliability are paramount. Traditional tests often fail to keep pace with rapid development cycles in agile environments, potentially delaying product releases.

These limitations underscore the necessity for more advanced and flexible testing approaches. As the financial technology landscape continues to evolve, the adoption of innovative testing methodologies that can overcome the constraints of traditional practices is becoming increasingly important. This shift aims to enhance testing efficiency, improve coverage, and better align with the dynamic nature of modern FA.

Innovative TM incorporate the latest advancements in technology and process management to address

the unique challenges posed by high-volume, high-security financial environments:

• Behavior-Driven Development focuses on the business behaviors. That facilitating clearer communication between developers, Quality Assurance teams, and non-technical stakeholders. It helps ensure that all development efforts are aligned with business goals and user expectations, thus improving the reliability and functionality of the application [2].

• Performance engineering integrates performance optimization throughout the development process. This proactive approach helps in identifying performance bottlenecks early and can significantly enhance the efficiency and stability of FA.

• Chaos engineering involves intentionally injecting faults into applications to test their resilience. This method is particularly useful for complex, distributed systems commonly found in financial services. It helps organizations ensure that their applications can sustain unexpected disruptions without significant loss of functionality or data.

• Fuzz testing (Fuzzing): this automated testing technique feeds random data, inputs, or conditions into the system to detect security loopholes and operational bugs. It is highly effective in identifying potential vulnerabilities within the software that could be exploited in FA.

• In complex FA where certain components may not be available during initial testing phases, service virtualization simulates these components. This allows testing to proceed without delays, ensuring that the application can be evaluated under conditions that mimic the full production environment.

• With the increasing emphasis on cybersecurity, automating security testing processes through tools that can identify vulnerabilities from the coding phase through deployment is crucial. This includes static application security testing (SAST), dynamic application security testing (DAST), and interactive application security testing (IAST).

As the financial industry continues to harness technology to drive efficiency and security, the most promising advancements in testing methodologies stem from the application of AI and ML, alongside test automation using optimization algorithms.

The application of AI and ML

According to a survey of developers conducted by Stack Overflow in 2023, AI is a sought-after tool for development and testing (fig. 1).

0 10 20 30 40 50 60 70 SO

Figure 1 - Most popular uses of AI in the development workflow among developers worldwide as of 2023, % [3] Source: developed by the author

The incorporation of AI and ML into software testing represents a transformative shift in methodologies used to ensure the quality and reliability of FA. These technologies offer the potential to automate complex processes and uncover insights that traditional TM cannot achieve (table 1).

Table 1

Overview of innovative TM using AI and ML [4,5]

Aspect Description Benefits

Automated test generation AI algorithms analyze historical data to automate the creation of test cases, identifying patterns and predicting potential failure points. Reduces manual labor, enhances focus on critical areas, and increases testing efficiency.

Scenario simulation ML models simulate a broad range of testing scenarios, including unusual or extreme conditions that might be overlooked manually. Covers more potential use cases and operational conditions, ensuring robustness under varied circumstances.

Continuous learning and adaptation ML models continuously learn from new data, dynamically adjusting test cases and strategies to align with the application's evolving nature. Keeps testing procedures up-to-date with application changes, enhancing the relevance and effectiveness of tests.

Real-time anomaly detection AI tools monitor applications during testing to detect anomalies in software behavior, comparing expected outcomes with actual results to flag discrepancies. Provides immediate feedback on errors, allowing for rapid correction and reducing the time to market for FA.

Risk-based test optimization AI and ML prioritize test cases based on the calculated risk and impact, optimizing the order of test execution. Focuses resources on high-impact tests, reduces time and cost, and minimizes the risk of significant defects going undetected.

Comprehensive assessment These technologies allow for a thorough assessment of performance and security, accommodating the complex requirements of financial systems. Ensures high accuracy and reliability in tests, offering a robust framework for critical financial operations.

Source: developed by the author

According to the author, the use of AI and ML in testing represents a significant advancement over traditional methods, equipping developers with powerful tools to improve the quality and reliability of FA. This is confirmed by the experience of JPMorgan Chase, which implemented the COIN platform that uses ML technologies to automate the testing and analysis of legal documents. The adoption of this technology has allowed the company to reduce the time required to process documents from several hours to seconds, significantly enhancing process efficiency and reducing human errors, thereby affirming the high value of innovative TM in the financial industry.

Test automation using optimization algorithms

Optimization algorithms are increasingly integral to automating the testing of FA, offering a method to enhance both efficiency and efficacy. These advanced algorithms optimize the allocation of testing resources, ensuring that critical components of financial systems are rigorously evaluated while less critical areas receive appropriate, but not excessive, attention.

Table 2

Optimization techniques utilized in test automation for FA [6,7]

Technique Description Application in financial systems

Genetic Algorithms Mimics natural evolutionary processes to evolve solutions towards optimal configurations. Optimizes test cases for complex transactional processes.

Simulated Annealing Uses a probabilistic technique to approximate the global optimum of a given function. Effective in finding optimal configurations under diverse conditions.

Particle Swarm Optimization Simulates social behavior patterns to optimize problem solving with agent cooperation. Enhances performance tuning of FA under load.

Ant Colony Optimization Inspired by the behavior of ants finding paths to food, it excels in network routing problems. Useful for optimizing data flow paths in distributed financial systems.

Gradient Descent A first-order iterative optimization algorithm for finding the minimum of a function. Commonly used in risk assessment and mitigation algorithms.

Source: developec by the author

These algorithms facilitate a more intelligent allocation of testing resources by prioritizing test scenarios that have the highest impact on system performance and reliability. They adaptively refine their strategies based on real-time feedback, which is especially beneficial in the dynamic landscape of FA where new features and updates are continually rolled out.

By implementing such sophisticated testing frameworks, financial institutions can not only speed up their

development cycles but also ensure that their applications meet the stringent standards required for handling sensitive financial data. This strategic approach to test automation not only saves time and resources but also significantly enhances the resilience and reliability of financial systems.

According to the author, the effectiveness of these optimization techniques in enhancing testing processes is evident. This is corroborated by the experience of a company like Citibank, which implemented ML algorithms to automate and optimize testing across its global transaction services. This deployment has not only streamlined their operations but also significantly improved the security and efficiency of their financial services, confirming the substantial benefits of these innovative TM.

New technologies and approaches in optimizing FA performance and reliability

The ongoing evolution of financial technology has spurred the adoption of cutting-edge technologies and methodologies that significantly enhance the performance and reliability of FA. Among these, cloud technologies, containerization, microservices architecture, and serverless computing are particularly noteworthy:

• Cloud Technologies offers scalable resources and flexible infrastructure that can adapt to varying loads, which is crucial for FA experiencing unpredictable usage patterns. The ability to dynamically allocate resources based on real-time demand ensures both cost efficiency and high performance.

• Containerization: by encapsulating applications in containers, developers can achieve a higher level of consistency across environments from development to production. Containers facilitate easier management, deployment, and scalability of FA, all while isolating processes and reducing cross-dependencies.

• Microservices architecture: this approach structures applications as a collection of loosely coupled services, which simplifies updating and scaling individual components without disrupting the entire system. Microservices enhance the agility and resilience of financial platforms, enabling quicker iterations and more robust error handling.

• Serverless computing abstract the underlying infrastructure away from the developers, allowing them to focus solely on the code. This model can lead to reduced operational costs and improved scalability, as the cloud provider dynamically manages the allocation of machine resources.

• Blockchain technology offers unprecedented security and data integrity benefits for FA. Its decentralized nature and cryptographic layer add an extra level of protection against data tampering and fraud.

These technologies collectively not only enhance the resilience and efficiency of FA but also create synergies with advanced TM. By integrating cloud computing, containerization, and microservices with AI-driven testing and optimization algorithms, financial institutions can develop a highly adaptive testing environment. This integration allows for continuous testing and deployment, which is crucial for maintaining high standards of performance and security amid frequent updates and feature expansions. Together, these technologies and TM ensure that financial platforms not only keep pace with market demands but also exceed the rigorous compliance and security expectations of the financial sector.

Successful application of innovative TM in practice

Goldman Sachs has leveraged cloud-based testing environments to streamline the deployment and scaling of their trading platforms. By using cloud services, they were able to simulate real-world trading volumes and stress test their systems under peak load conditions. This approach allowed for rapid identification and resolution of performance bottlenecks, ensuring robustness and reliability during market volatilities. The successful implementation of innovative TM in FA is evidenced by the company's increased capitalization. In 2022, it was $114 billion, and in 2023, it reached $133 billion [8].

JPMorgan Chase implemented AI-driven testing for its mobile banking applications, significantly reducing the time required for test execution. AI algorithms were used to analyze user interaction data, which helped

predict where failures were most likely to occur and prioritize testing in those areas. As a result, there was a reduction in fraud levels associated with the use of the applications and an improvement in customer service quality, with the account verification failure rate decreasing by 15-20% [9].

Citibank utilized service virtualization to test their payment gateways, allowing them to simulate various payment scenarios, including international transactions and peak load periods. This method enabled Citibank to identify and address service vulnerabilities without the need to access live payment systems, thereby enhancing security and reducing the risk of transaction failures.

Visa has integrated performance engineering into their development process, particularly for their contactless payment systems. By embedding performance testing early in the lifecycle, Visa managed to increase transaction handling capacity without compromising security. This preemptive approach to performance issues ensures that Visa's systems can handle large volumes of transactions securely and efficiently. In 2023, Visa announced the generative AI ventures initiative, valued at $100 million, to invest in next-generation companies specializing in the development of technologies and applications with generative AI, which will impact the future of commerce and payments [10].

MasterCard adopted fuzz testing to enhance the security of their credit card processing systems. By automatically generating and inputting thousands of random transaction data sets, they were able to uncover and fix vulnerabilities that traditional TM might have missed.

These examples highlight how embracing advanced testing technologies and methodologies can lead to significant improvements in the functionality and security of FA. By adopting these innovative approaches, financial institutions are better equipped to meet the demands of a dynamic financial environment and maintain consumer trust in their digital solutions. Conclusion

The integration of innovative methods significantly enhances the efficiency and precision of testing processes within the financial sector. These advanced technologies facilitate automated and optimized testing, reducing operational costs and expediting development cycles, which is crucial for adapting to rapid market changes. AI and ML enable a comprehensive understanding of application behaviors under diverse scenarios, significantly mitigating the risk of critical failures. This results in the delivery of more reliable financial services that meet the stringent expectations of consumers and regulatory standards.

References:

1. Lukasczyk, S., KroiR, F. & Fraser, G. An empirical study of automated unit test generation for Python. Empir Software Eng 28, 36 (2023). https://doi.org/10.1007/s10664-022-10248-w

2. Zakharau A. The impact of fintech application implementation on improving consumer credit ratings // Sciences of Europe # 138, pp. 14-16, 2024. DOI: 10.5281/zenodo.10957276

3. Most popular uses of AI in the development workflow among developers worldwide as of 2023 // Statista URL:https://www.statista.com/statistics/1401409/popular-ai-uses-in-development-workflow-globally/#: ~:text=According%20to%20the%202023%20Stack,using%20it%20as%20of%202023 (date of application: 07.05.2024)

4. Arghavan Moradi Dakhel, Amin Nikanjam, Vahid Majdinasab, Foutse Khomh, Michel C. Desmarais, Effective test generation using pre-trained Large Language Models and mutation testing, Information and Software Technology, Volume 171, 2024

5. Bukhtueva I. THE IMPACT OF AI TECHNOLOGIES ON BUSINESS PERFORMANCE // Vestnik nauki №3 (72) vol.

5. pp. 467 - 476. 2024 r. ISSN 2712-8849

6. Kaliuta K. Integration of AI for Routine Tasks Using Salesforce. Asian Journal of Research in Computer Science.

2023 Jul 17;16(3):119-27.

7. Ogarkov A. Enhancing commercial effectiveness in pharmaceuticals: a case study on the implementation of successful sales and marketing strategies / A. Ogarkov // Issues of management and economics: current state of current problems: collection. Art. based on materials from the LXXX International Scientific and Practical Conference "Issues of Management and Economics: Current State of Current Problems." - No. 2(71). - M., Ed. "Internauka", 2024.

8. The Goldman Sachs Group Capitalization / MarketCap URL: https://marketcap.ru/stocks/GS/financial-statements/income-statement/market-cap (date of application: 08.05.2024)

9. How AI will make payments more efficient and reduce fraud // J.P. Morgan URL: https://www.jpmorgan.com/insights/payments/payments-optimization/ai-payments-efficiency-fraud-reduction (date of application: 08.05.2024)

10.Kaliuta K. Personalizing the user experience in Salesforce using AI technologies. Computer-Integrated Technologies: Education, Science, Production. 2023 Sep 24(52):48-53.

© Bobunov A.Yu., 2024

УДК 33

Orazberdieva G.,

student

International horse breeding Academy named after Aba Annaev

Charyberdiev K.,

teacher

International horse breeding Academy named after Aba Annaev

Arkadag, Turkmenistan

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This article examines the vital role that small and medium enterprises (SMEs) play in economic growth. It discusses how SMEs drive innovation, create jobs, and contributeto economic diversification. The paper also explores challenges faced by SMEs and suggests policies to support their development.

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Keywords:

small and medium enterprises, economic growth, innovation, job creation, economic diversification, SME support policies.

Small and medium enterprises (SMEs) are the backbone of many economies, contributing significantly to economic growth, job creation, and innovation. This article explores the crucial role of SMEs in driving economic development, the challenges they face, and the policies needed to support their growth and sustainability.

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