Risk Management of the Project and the Use of Software in SME

Víchová, Kateřina; Taraba, Pavel; Belantová, Tereza

doi:10.37394/23207.2020.17.54

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…Statistically, the Chi-square statistic is the sum of squared differences between observed frequencies (actual, experimentally determined) and expected frequencies (those proportional to the formulated null hypothesis), divided by the expected frequency for each cell. The formula for the Chisquare statistic used in the Chi-square test is given by [14]:…”

Section: Feature Selectionmentioning

confidence: 99%

Software Reliability Prediction Using Deep Learning and Feature Selection Algorithms

I. Khalee,

Salih

2024

IRJIET

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Software reliability is crucial in preventing user issues, financial losses, and reputational damage to companies. Developing accurate models for estimating reliability is imperative. Deep learning, a branch of artificial intelligence, uses neural networks to understand and analyze data, playing a vital role in predicting errors and improving software quality. In this research, Neural Networks (NN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) algorithms, along with statistical methods like Chi-square and Regression Coefficient, and intelligent algorithms such as Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA), were employed for feature selection. The results highlighted the superiority of PSO and WOA over traditional methods, with LSTM outperforming other algorithms. Evaluation metrics, including Accuracy, Precision, Recall, and F1-Score, indicated that WOA with LSTM achieved 100% accuracy across datasets. For DS1, accuracy was 97% for all networks, reaching 100% with WOA. DS2 showed accuracy improvement from 80% to 82% with statistical methods and up to 100% with WOA. DS3 demonstrated 99% accuracy with statistical methods and PSO, reaching 100% with WOA. DS4 maintained 99% accuracy with all methods. DS5 exhibited accuracy ranging from 82% to 84%, reaching 100% with WOA. DS6 had accuracy between 78% and 77%, reaching 100% with WOA. This underscores the effectiveness of deep learning, especially with PSO and WOA, in enhancing software reliability.

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Section: Feature Selectionmentioning

confidence: 99%

Software Reliability Prediction Using Deep Learning and Feature Selection Algorithms

I. Khalee,

Salih

2024

IRJIET

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“…It prevents the risk that can affect the desired outcomes and results. Preventive measures are taken by small- and medium-sized enterprises (SME) to minimize the risk [ 31 ]. To look for any undesired or unexpected errors in a project, a well-planned risk management strategy is always needed by SME [ 32 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Risk Factors in Global Software Development: A Cross-Continental Study Using Modified Firefly Algorithm

Iftikhar

Ali

Alam

et al. 2022

Computational Intelligence and Neuroscience

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In today’s competitive world, software organizations are moving towards global software development (GSD). This became even more significant in times such as COVID-19 pandemic, where team members residing in different geographical locations and from different cultures had to work from home to carry on their tasks and responsibilities as travelling was restricted. These teams are distributed in nature and work on the same set of goals and objectives. Some of the key challenges which software practitioners face in GSD environment are cultural differences, communication issues, use of different software models, temporal and spatial distance, and risk factors. Risks can be considered as a biggest challenge of other challenges, but not many researchers have addressed risks related to time, cost, and resources. In this research paper, a comprehensive analysis of software project risk factors in GSD environment has been performed. Based on the literature review, 54 risk factors were identified in the context of software development. These were further classified by practitioners into three dimensions, i.e., time, cost, and resource. A Pareto analysis has been performed to discover the most important risk factors, which could have bad impact on software projects. Furthermore, a modified firefly algorithm has been designed and implemented to evaluate and prioritize the pertinent risk factors obtained after the Pareto analysis. All important risks have been prioritized according to the fitness values of individual risks. The top three risks are “failure to provide resources,” “cultural differences of participants,” and “inadequately trained development team members.”

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“…They identified sources of market risk as follows: stagnation of the market, losing customers, unreliability of suppliers, and strong competition. The importance of market risk management is a very current topic analysed currently in many scientific papers (see, e.g., Belas et al, 2020;Dvorsky et al, 2020;Khan et al, 2019;Vichova et al, 2020) as well as due to the changes caused by the pandemic situation (El Baz & Ruel, 2021;Kumar et al, 2021). Virglerova et al (2020) found out that there is a statistically significant difference in the way how SMEs consider legal risks between Czech Republic and other analysed countries (Slovakia, Hungary, Poland, and Ukraine).…”

Section: Literature Reviewmentioning

confidence: 99%

Selected factors of internationalisation and their impact on the SME perception of the market risk

Ivanová

Dvorský

Belás

et al. 2021

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Research background: Internationalisation of entrepreneurial activities provides an ample opportunity for the growth and sustainability of small and medium-sized enterprises (SMEs). When making their business activities international, SMEs are facing various obstacles. Investigating the key factors of internationalisation and their impact on SME decision making constitutes a key factor of the research work. Purpose of the article: The main purpose of the paper is to examine the impact of selected factors of SME internationalisation on the positive perception of the market risk. Higher export costs, differences in legal environment, taxes, and linguistic and cultural differences were set to be the significant factors influencing the positive perception of the market risk. Bearing the main purpose in mind, the Visegrad Four (V4 ? Czech Republic, Slovakia, Poland, Hungary) were examined. Methods: The questionnaire in mother tongue of each country in online and paper form was used. The questionnaire consists of 77 questions divided into several sections. Likert five-point scale was used to allow the respondents to express their opinion. In total, 1585 responses were completed. A regression analysis was used to verify the statistical significance of the independent variables and to quantify the causal relationships (determining the direction and strength of the effect) on the dependent variable. As the variables are of one metric, a linear regression model (LRM) was selected to validate the hypothesis. Findings & value added: The internationalisation of SME activities brings along many risks and barriers that SMEs need to overcome if they want to enter international markets. Many SMEs consider the barriers and restrictions to enter foreign markets to be severe. Therefore, they decide to do their business domestically. It was established in the research that only 30.2% of the respondents expanded their business activities internationally. The biggest impact on the positive assessment of the market risk was found to have linguistic and cultural differences. They do not have a negative effect on the positive perception of the market risk, so they do not hinder SMEs in their international expansion. The second most important was the factor of the export costs. The factor of possible increased costs is quite important for SMEs in making their decisions whether to expand internationally or not. This factor also shapes a positive approach to assessing the market risk by SMEs. In the research, SMEs confirmed that potential higher costs do not represent a major barrier for them in shaping their attitudes towards the market risk. The remaining factors being analysed, namely differences in legal environment and taxes, did not have a significant impact on the positive assessment of the market risk internationally. The practical implications can be found in new information about market risk in process of internationalisation provided from four selected countries.

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Risk Management of the Project and the Use of Software in SME

Cited by 11 publications

References 20 publications

Software Reliability Prediction Using Deep Learning and Feature Selection Algorithms

Software Reliability Prediction Using Deep Learning and Feature Selection Algorithms

Analysis of Risk Factors in Global Software Development: A Cross-Continental Study Using Modified Firefly Algorithm

Selected factors of internationalisation and their impact on the SME perception of the market risk

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