In the paper deterministic and random approaches to testing are analyzed. The advantages and disadvantages of both approaches are given.  The  use  of  pseudorandom  test  sequences  as  a  combination  of  the  advantages  of  deterministic and  random  approaches  is considered. The connection between deterministic and random approaches to testing is shown

Ilyina, A. Y.

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59-я Научная Конференция Аспирантов, Магистрантов и Студентов БГУИР, Минск, 2023 377  In another research the participants observed fictitious logos of various colour and determined which personality traits or human characteristics came to mind when they saw a brand logo [6]. The findings of these two studies were really similar. They show that white, being the total reflection of all colours, can be linked to sincerity as it is associated with purity, simplicity, hygiene, clarity, and peace.  It is also associated with happiness. Red can be linked to excitement as it is considered an arousing, exciting, and stimulating colour. It is generally associated with the characteristics of activity, strength, speed, and stimulation and it is also considered up-to-date. These two researches have shown that longer wavelength hues (e.g., red, orange, yellow) induce states of arousal and excitement. Orange is arousing and fun, so it is considered lively, energetic, extroverted, and sociable. Having a medium-long wavelength, yellow also holds qualities of arousal and excitement, it taps the cheerful facet of sincerity as it generally elicits feelings of optimism, extraversion, friendliness and happiness. Green’s primary association with nature creates feelings of security and a connection with the outdoors. Blue is linked to competence, as it is associated with intelligence, communication, trust, efficiency, reliability, duty, and logic. It is also seen as a secure colour. Black stands for sophistication and glamour. It is a very powerful colour that signals stateliness and dignity. In fashion world, black expresses status, elegance and wealth. Likewise, purple is a colour that also connotes luxury, authenticity, and quality. Purple is also seen as a dignified and stately colour, which is likely due to its historical past as a colour reserved for royalty and to connote social roles. Like a carefully chosen brand name, color carries intrinsic meaning that becomes central to the brand’s identity and communicates the desired image. Finally, marketing managers might use colour knowledge to choose an appropriate colour scheme for logos, packaging, advertisements, storefronts, and websites that will create and reinforce a specific brand personality. To conclude, the use of colour in advertising is an essential aspect of creating successful campaigns that capture the interest of the target audience. Colour can play a critical role in shaping consumer perception, so advertisers should carefully consider the use of colour in their campaigns to ensure that it aligns with their branding and messaging goals. By understanding the psychological impact of colour on consumers, advertisers can use it to create powerful and effective advertisements that resonate with their target audience.  References: 1. Using Colour In Advertising / Plyndina, I.N. // Current issues of economic sciences, 2015. – № 43. – 48-51 p. 2. Impact of Colours on the Psychology of Marketing – A Comprehensive over View / Singh, N., & Srivastava, S. K. // Management and Labour Studies, 2011. – 36(2), 199-209 p. 3. Synestesia Of Colour In The Advertising / Shalimova, L.A. // World of science, culture, education, 2013. – № 6 (43) 4. The Effectiveness of Information and Colour in Yellow Pages Advertising / Fernandez, K. V., & Rosen, D. L. // Journal of Advertising, 2000. – 29(2), 61-73 p. 5. Colour Assignment / Hallock, J. [Electronic resource]. – Mode of access: joehallock.com/?page_id=1281. ─ Date of access: 22.03.2023. 6. Exciting red and competent blue: the importance of colour in marketing / Labrecque, L. I., & Milne, G. R. // Journal of the Academy of Marketing Science, 2011. – 40(5), 711-727 p.  16. DETERMINISTIC AND RANDOM TESTING APPROACHES ANALYSIS Ilyina A.Y. Belarusian State University of Informatics and Radioelectronics Minsk, Republic of Belarus Liakh Y.V. – Senior Lecturer In the paper deterministic and random approaches to testing are analyzed. The advantages and disadvantages of both approaches are given. The use of pseudorandom test sequences as a combination of the advantages of deterministic and random approaches is considered. The connection between deterministic and random approaches to testing is shown. In deterministic testing deterministic input sequences are fed to the object under study. Random or pseudorandom sequences are used in random testing. A deterministic test sequence is a sequence defined analytically as a recurrent function depending on the previous test sets. Abstract mathematical models exist for deterministic test sequences, which makes it possible to effectively implement them both at the software and hardware level. Such sequences have various properties, including the properties of randomness, uniformity and maximum length. The main feature of deterministic test sequences is reproducibility [1]. A random test consists of random and independent test sets. Test cases are selected, randomly generated from a set of input data [2]. The main advantage of random testing is that there is no need to build a deterministic test – generating a deterministic test may have high complexity and even be 59-я Научная Конференция Аспирантов, Магистрантов и Студентов БГУИР, Минск, 2023 378  unrealizable. Among the advantages of random testing are also low time and resource consumption, detection of new faults, ease of automation and efficiency in stress and load testing. However, the test performed during random testing is so occasional that it is very difficult or impossible to recreate the conditions for detecting a specific defect. Random testing does not make it possible to reproduce the fault – there is no exact description of it and steps to reproduce it. The most significant disadvantages of random testing also include the complexity of defining test scenarios and high time complexity, while there is no guarantee of the effectiveness of random tests. In random testing it is impossible or difficult to reform a previously generated random test sequence to reproduce an experiment, so pseudorandom sequences are used. A sequence of non-random numbers is called a pseudorandom if it has all the properties of a random sequence. Pseudorandom sequences are essentially deterministic and are close to random sequences in probabilistic characteristics [1]. An important feature of pseudorandom testing is the repeatability of the test experiment while preserving all the main advantages of random testing [1]. Pseudorandom sequences are used to implement random testing and controlled random testing. Antirandom testing represents the development of random testing. Antirandom testing is based on the generating each subsequent test set using a characteristic, or several characteristics obtained on the basis of previous sets [1]. In random testing each test set must be selected randomly, regardless of previously generated test sets. In antirandom testing only the first test set is randomly selected. Each subsequent test set is formed depending on the previously generated sets by choosing the one that has the maximum distance to all previously completed test sets, and the sequence of these sets is deterministic.  There are various modifications of antirandom testing: adaptive random testing, good random testing, mirror random testing and other solutions. Antirandom testing and its modifications are used to increase the effectiveness of random tests and have received the general name – controlled random testing [3]. Controlled random testing is a random test sequence in which the next test set is formed as different as possible from all previously generated sets [4]. The key feature of controlled generation of random test sets is information extracted in the form of certain characteristics (metrics) from previously generated test sets. This information is used to form the next test set. The main disadvantage of antirandom testing and its modifications is their high computational complexity. The implementation of the antirandom method of constructing tests requires listing all possible input tests and calculating the distance for each potential candidate for the next test sets [1]. In controlled random testing it is also difficult to determine the next test set in relation to the previous test sets. In conclusion, it should be mentioned that deterministic, random, and pseudorandom testing approaches are widely used in practice. It is important to emphasize that the approach to testing should be chosen based on the characteristics of the object under study and the testing conditions.  References: 1. Yarmolik, V.N. Computer Systems Testing and Diagnoses / V.N. Yarmolik. – Minsk : Bestprint, 2019. – 387 p. 2. An empirical comparison of combinatorial testing, random testing and adaptive random testing / H. Wu [et al.] // IEEE Transactions on Software Engineering. – 2020. – Vol. 46, № 3. – P. 302-320. 3. Levantsevich, V.A. Multiple controlled random testing / V.A. Levantsevich, V.N. Yarmolik // Doklady BGUIR. – 2019. –  № 3 (121). – P. 65-69. 4. A Survey on Adaptive Random Testing / R. Huang [et al.] // IEEE Transactions on Software Engineering. – 2021. – Vol. 47, № 10. – P. 2052-2083.  17. NEURAL NETWORKS: FORECASTING ECONOMIC TIME SERIES Kadushkevich D.R. Belarusian State University of Informatics and Radioelectonics Minsk, Republic of Belarus  Ladyjenko M.V. – Senior Lecturer  The potential of machine learning techniques in the field of economic forecasting is presented in this paper. The use of Recurrent Neural Networks (RNNs) and their variants is described.   Economic forecasting is a challenging task that is vital to many industries and policy-making bodies. Accurate forecasts of economic time series can help businesses make informed decisions about investments, inventory, workforce planning, and can also help governments and central banks set monetary policy. Historically, economists have relied on statistical models such as autoregressive integrated moving average (ARIMA) and vector autoregression (VAR) to forecast economic variables. However, in recent years, there has been growing interest in using neural networks for such purposes, Recurrent Neural Networks (RNNs) specifically.  

Deterministic and random testing approaches analysis

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Deterministic and random testing approaches analysis

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