Статті в журналах: "Software risk assessment"

1

Greene, N. "Computer Software for Risk Assessment." Journal of Chemical Information and Computer Sciences 37, no. 1 (January 1997): 148–50. http://dx.doi.org/10.1021/ci960095h.

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2

Chen, Xingyuan, and Yong Deng. "An Evidential Software Risk Evaluation Model." Mathematics 10, no. 13 (July 2, 2022): 2325. http://dx.doi.org/10.3390/math10132325.

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Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area. The aim of this study is to establish a data-driven software risk assessment model named DDERM. In the proposed model, experts’ risk assessments of probability and severity can be transformed into basic probability assignments (BPAs). Deng entropy was used to measure the uncertainty of the evaluation and to calculate the criteria weights given by experts. In addition, the adjusted BPAs were fused using the rules of Dempster–Shafer evidence theory (DST). Finally, a risk matrix was used to get the risk priority. A case application demonstrates the effectiveness of the proposed method. The proposed risk modeling framework is a novel approach that provides a rational assessment structure for imprecision in software risk and is applicable to solving similar risk management problems in other domains.

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3

Ammar, H. H., T. Nikzadeh, and J. B. Dugan. "Risk assessment of software-system specifications." IEEE Transactions on Reliability 50, no. 2 (June 2001): 171–83. http://dx.doi.org/10.1109/24.963125.

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4

Hyatt, Lawrence E., and Linda H. Rosenberg. "Software metrics program for risk assessment." Acta Astronautica 40, no. 2-8 (January 1997): 223–33. http://dx.doi.org/10.1016/s0094-5765(97)00148-3.

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5

Xu Jian, Yan Han, and Li Qianmu. "A Methodology for Software Reliability Risk Assessment." Journal of Convergence Information Technology 6, no. 4 (April 30, 2011): 188–200. http://dx.doi.org/10.4156/jcit.vol6.issue4.22.

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6

Chittister, C., and Y. Y. Haimes. "Assessment and management of software technical risk." IEEE Transactions on Systems, Man, and Cybernetics 24, no. 2 (1994): 187–202. http://dx.doi.org/10.1109/21.281419.

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7

Barki, Henri, Suzanne Rivard, and Jean Talbot. "Toward an Assessment of Software Development Risk." Journal of Management Information Systems 10, no. 2 (September 1993): 203–25. http://dx.doi.org/10.1080/07421222.1993.11518006.

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8

Funieru, Cristian, and Răzvan Funieru. "Caries Risk software – a new tool used for children caries risk assessment." Romanian Journal of Stomatology 61, no. 2 (June 30, 2015): 179–82. http://dx.doi.org/10.37897/rjs.2015.2.13.

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Introduction. Dental caries risk is a key factor which may influence dental health in the near future. It is a very important factor for developing individual or community preventive strategies. Material and method. 46 dental records were analyzed including data of caries baseline and caries risk factors in a group of 46 schoolchildren from city of Bucharest. The overall caries risk was estimated using version 1.0 of Caries Risk software, the first software designed and calibrated to provide caries risk for Romanian patients. Results. The caries risk was approximately 40% in our sample. The girls had a higher risk by about 13% compared to boys. Conclusions. Caries Risk software is an effective tool for estimating caries risk by having an easy mode for input data and a short time needed for processing.

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9

Taylor, David, Derek Williamson, Harprit Kaur, Kevin Macintosh, Leo Brewster, and Ramon Roach. "BARBADOS NATIONAL COASTAL RISK INFORMATION AND PLANNING PLATFORM A SOFTWARE SYSTEM FOR HAZARDS, VULNERABILITY & RISK." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 55. http://dx.doi.org/10.9753/icce.v36.risk.55.

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The Barbados National Coastal Risk Information and Planning Platform (NCRIPP) is a study nearing completion to quantify coastal risk and develop a software system to serve this information country-wide. The NCRIPP follows extensive coastal baseline studies and includes assessment of eight hazards, vulnerability assessment and finally a risk assessment. The software platform is a cloud-based system that includes a large catalogue of assets, hazard overlays, and the ability to calculate damages , and mitigation alternatives.

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10

Kumar, Chandan, and Dilip Kumar Yadav. "A Probabilistic Software Risk Assessment and Estimation Model for Software Projects." Procedia Computer Science 54 (2015): 353–61. http://dx.doi.org/10.1016/j.procs.2015.06.041.

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11

BIFFL, STEFAN, THOMAS MOSER, and DIETMAR WINKLER. "RISK ASSESSMENT IN MULTI-DISCIPLINARY (SOFTWARE+) ENGINEERING PROJECTS." International Journal of Software Engineering and Knowledge Engineering 21, no. 02 (March 2011): 211–36. http://dx.doi.org/10.1142/s0218194011005232.

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Software systems in safety-critical industrial automation systems, such as power plants and steel mills, become increasingly large, complex, and distributed. For assessing risks, like low product quality and project cost and duration overruns, to trustworthy services provided by software as part of automation systems there are established risk analysis approaches based on data collection from project participants and data models. However, in multi-disciplinary engineering projects there are often semantic gaps between the software tools and data models of the participating engineering disciplines, e.g., mechanic, electrical, and software engineering. In this paper we discuss current limitations to risk assessment in (software+) engineering projects and introduce the SEMRISK approach for risk assessment in projects with semantically heterogeneous software tools and data models. The SEMRISK approach provides the knowledge engineering foundation to allow an end-to-end view for service-relevant data elements such as signals, by providing a project domain ontology and mappings to the tool data models of the involved engineering disciplines. We empirically evaluate the effectiveness and efficiency of the approach based on a real-world industrial use case from the safety-critical power plant domain. Major results are that the approach was effective and considerably more efficient than the current approach at the industry partner.

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12

Joó, József Gábor, Mónika Csanád, Katalin Tóth, Szabolcs Máté, and Zsolt Nagy. "Risk assessment in familial breast cancer." Orvosi Hetilap 152, no. 19 (May 2011): 758–62. http://dx.doi.org/10.1556/oh.2011.29110.

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Women with a history of breast cancer are common at centers for cancer genetic risk all over Europe. Given limited health care resources, managing this demand, while achieving good value for money coming from health services, is generally a major challenge. This paper recapitulates and summarizes the available methods of the risk assessment of familial breast cancer. After a systematic review of the literature Gail-model, Claus-model and BOADICEA-model were selected, as well as softwares (LINKAGE software; MENDEL v3.3 software) available in the application of these algorhythms are also summarized. Comparisons were made between the models concerning their advantages and disadvantages. The really reliable methods of risk estimation of familial breast cancer are always based on the analysis of the pedigree structure and allow the estimation of the patient’s probability of carrying a susceptibility gene under a particular genetic model, given her family history. For this method the knowledge of BRCA mutation status is absolutely indispensable. The methods of BRCA mutation analysis as well as the main characteristics of the occurrence of BRCA mutation carrier condition are discussed in details. Orv. Hetil., 2011, 152, 758–762.

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13

BARMAK, ALEXANDER, VIKTOR KUDRIAVTSEV, YRII FORKUN, and OKSANA YASHYNA. "SOFTWARE CODE ANALYSIS SYSTEM FOR RISK ASSESSMENT AND QUALITY ASSURANCE OF SOFTWARE." HERALD OF KHMELNYTSKYI NATIONAL UNIVERSITY 297, no. 3 (July 2, 2021): 25–29. http://dx.doi.org/10.31891/2307-5732-2021-297-3-25-29.

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The paper presents the results of research of various standards, rules and methods of writing software code and analysis of their impact on software quality and the likelihood of technical risks associated with information processes within the system. Most of the risks that arise while developing software products are due to errors in building the system architecture or writing code. As a solution for such problems, it is proposed to apply the developed set of rules and methods to build the system architecture and assess the quality of writing software objects. Metrics have been developed to estimate the size and complexity of the module by combining elements of Halsted and Chepin metrics. Also, a set of principles for optimizing the structure of the system, also known as SOLID principles, was presented. The application of these principles for system construction and analysis was substantiated in order to minimize risks, ensure the quality of the software system and provide opportunities for easy extensibility of the project. Using these methods will optimize the project both for use and for further development. The need for such optimization processes in terms of risk management is that the clearer the system and the easier it is to expand, the less likely it is that errors will occur in the future when adding new functionality.

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14

Nagamalleswari, D., J. Nagalakshmi, G. Karthik, and P. Harthita. "A study on risk assessment techniques in information systems." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 313. http://dx.doi.org/10.14419/ijet.v7i1.1.9711.

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In today’s world, IT industry is rushing forward with an advancement of developing the advanced software. The Developers always try to develop the software projects without any errors or failures. Even though the developers take many measures to avoid software project failures, they are facing the failures that are occurred due to Risks that take place in the software projects. We cannot remove risk completely to the 100% extent, but we can try to minimize the risk in the projects by assessing the risks. So, in this paper we are providing a survey which overviews on different risk assessment techniques. This survey provides information about various risk assessment techniques which will be further useful for the software developers to minimize the risk and make the successful project.

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15

Kang, Hyun Gook, Heung-Seop Eom, and Han Seong Son. "Software Failure Probability Quantification for System Risk Assessment." Scholarly Research Exchange 2009 (2009): 1–9. http://dx.doi.org/10.3814/2009/163456.

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16

Huff, Philip, and Qinghua Li. "Towards Automating the Assessment of Software Vulnerability Risk." ICST Transactions on Security and Safety 8, no. 27 (January 12, 2021): 170247. http://dx.doi.org/10.4108/eai.25-6-2021.170247.

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17

Saveliev, D. V. "Risk Management and Assessment in Software Development Projects." Èlektronnoe modelirovanie 43, no. 4 (August 2, 2021): 113–24. http://dx.doi.org/10.15407/emodel.43.04.113.

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The article defines the concept of threat model. Described a list of current security guidelines for the development and administration of web systems. Formed the list of cybersecurity threats, the consequences of their implementation are determined. Described the process of forming a model of cybersecurity threats of web systems. Defined the list of threats based on the recommendations and experience of authoritative organizations in the world and Ukraine. Defined the concepts of risk, risk index and risk status for the security of web systems. Defined the main principles of risk management in software development projects.

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18

Ni, M., J. D. McCalley, V. Vittal, S. Greene, C. W. Ten, V. S. Ganugula, and T. Tayyib. "Software Implementation of Online Risk-Based Security Assessment." IEEE Power Engineering Review 22, no. 11 (November 2002): 59. http://dx.doi.org/10.1109/mper.2002.4311833.

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19

Novakovic, Zoran, and Zoran Milojevic. "Software package for risk assessment in the workplace." Vojnotehnicki glasnik 56, no. 2 (2008): 217–26. http://dx.doi.org/10.5937/vojtehg0802217n.

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20

Ming Ni, J. D. McCalley, V. Vittal, S. Greene, C. Ten, V. S. Ganugula, and T. Tayyib. "Software implementation of online risk-based security assessment." IEEE Transactions on Power Systems 18, no. 3 (August 2003): 1165–72. http://dx.doi.org/10.1109/tpwrs.2003.814909.

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21

Moni, Mohammad, and Pietro Liò. "comoR: a software for disease comorbidity risk assessment." Journal of Clinical Bioinformatics 4, no. 1 (2014): 8. http://dx.doi.org/10.1186/2043-9113-4-8.

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22

Lima, Ana Paula Souza, Tânia Couto Machado Chianca, and Meire Chucre Tannure. "Assessment of nursing care using indicators generated by software." Revista Latino-Americana de Enfermagem 23, no. 2 (April 2015): 234–41. http://dx.doi.org/10.1590/0104-1169.0177.2547.

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OBJECTIVE: to analyze the efficacy of the Nursing Process in an Intensive Care Unit using indicators generated by software. METHOD: cross-sectional study using data collected for four months. RNs and students daily registered patients, took history (at admission), performed physical assessments, and established nursing diagnoses, nursing plans/prescriptions, and assessed care delivered to 17 patients using software. Indicators concerning the incidence and prevalence of nursing diagnoses, rate of effectiveness, risk diagnoses, and rate of effective prevention of complications were computed. RESULTS: the Risk for imbalanced body temperature was the most frequent diagnosis (23.53%), while the least frequent was Risk for constipation (0%). The Risk for Impaired skin integrity was prevalent in 100% of the patients, while Risk for acute confusion was the least prevalent (11.76%). Risk for constipation and Risk for impaired skin integrity obtained a rate of risk diagnostic effectiveness of 100%. The rate of effective prevention of acute confusion and falls was 100%. CONCLUSION: the efficacy of the Nursing Process using indicators was analyzed because these indicators reveal how nurses have identified patients' risks and conditions, and planned care in a systematized manner.

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23

Fryer, M. O. "Risk Assessment of Computer Controlled Systems." IEEE Transactions on Software Engineering SE-11, no. 1 (January 1985): 125–29. http://dx.doi.org/10.1109/tse.1985.231849.

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24

Veevers, Alan. "Some issues in software reliability assessment." Software Testing, Verification and Reliability 1, no. 1 (1991): 17–22. http://dx.doi.org/10.1002/stvr.4370010104.

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25

Dale, Chris. "The assessment of software reliability." Reliability Engineering & System Safety 34, no. 1 (January 1991): 91–103. http://dx.doi.org/10.1016/0951-8320(91)90101-c.

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26

Lindermeier, Robert. "Quality assessment of software prototypes." Reliability Engineering & System Safety 43, no. 1 (January 1994): 87–94. http://dx.doi.org/10.1016/0951-8320(94)90097-3.

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27

Chandani, Priyanka, and Chetna Gupta. "An Early Multi-Criteria Risk Assessment Model." Journal of Cases on Information Technology 21, no. 2 (April 2019): 51–64. http://dx.doi.org/10.4018/jcit.2019040104.

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Accurate time and budget is an essential estimate for planning software projects correctly. Quite often, the software projects fall into unrealistic estimates and the core reason generally owes to problems with the requirement analysis. For investigating such problems, risk has to identified and assessed at the requirement engineering phase only so that defects do not seep down to other software development phases. This article proposes a multi-criteria risk assessment model to compute risk at a requirement level by computing cumulative risk score based on a weighted score assigned to each criterion. The result of comparison with other approaches and experimentation shows that using this model it is possible to predict the risk at the early phase of software development life cycle with high accuracy.

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28

Zhu, Zhicheng, Kun Lan, Zhihong Rao, and Yuguang Zhang. "Risk assessment method for IoT software supply chain vulnerabilities." Journal of Physics: Conference Series 1732 (January 2021): 012051. http://dx.doi.org/10.1088/1742-6596/1732/1/012051.

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29

Karari, Poojanjali, and Pooja Jain. "A Survey on Software Cost and Risk Assessment Models." International Journal of Computer Applications 171, no. 4 (August 17, 2017): 13–18. http://dx.doi.org/10.5120/ijca2017915016.

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30

Sambantha Moorthy, Jayaletchumi. "DEVELOPING USABLE SOFTWARE PRODUCT USING USABILITY RISK ASSESSMENT MODEL." International Journal of Digital Information and Wireless Communications 4, no. 1 (2014): 95–102. http://dx.doi.org/10.17781/p001088.

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31

Joh, Hyun Chul. "Modeling Software Risk Assessment Based on Multi-Vulnerability Lifecycle." International Review on Computers and Software (IRECOS) 11, no. 2 (February 29, 2016): 116. http://dx.doi.org/10.15866/irecos.v11i2.8552.

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32

Joh, HyunChul, and JooYoung Lee. "Empirical Risk Assessment in Major Graphical Design Software Systems." Journal of Multimedia Information System 8, no. 4 (December 31, 2021): 259–66. http://dx.doi.org/10.33851/jmis.2021.8.4.259.

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33

JPT staff, _. "Software Application Optimizes Well-Planning and Risk-Assessment Process." Journal of Petroleum Technology 56, no. 02 (February 1, 2004): 26–80. http://dx.doi.org/10.2118/0204-0026-jpt.

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34

Gavanski, Dušan, Azra Korjenic, and Verica Milanko. "Fire risk assessment in buildings using fire protection software." International Journal of Risk Assessment and Management 17, no. 1 (2013): 1. http://dx.doi.org/10.1504/ijram.2013.054375.

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35

Peercy, D. E., M. D. Estill, and K. L. Shaw. "Risk Assessment Methodology for Software Supportability (RAMSS): User's handbook." Computers & Security 7, no. 5 (October 1988): 510. http://dx.doi.org/10.1016/0167-4048(88)90211-8.

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36

Bowlds, Ted F., John M. Fossaceca, and Ronald Iammartino. "Software obsolescence risk assessment approach using multicriteria decision-making." Systems Engineering 21, no. 5 (May 28, 2018): 455–65. http://dx.doi.org/10.1002/sys.21446.

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37

Reece, Wendy J., and David I. Gertman. "NUCLARR: a workstation software package to support risk assessment." Reliability Engineering & System Safety 37, no. 2 (January 1992): 173–78. http://dx.doi.org/10.1016/0951-8320(92)90011-9.

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38

Woolridge, Richard, Denise McManus, and Joanne Hale. "Stakeholder Risk Assessment: An Outcome-Based Approach." IEEE Software 24, no. 2 (March 2007): 36–45. http://dx.doi.org/10.1109/ms.2007.54.

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39

Madachy, Raymond J. "Knowledge-based risk assessment and cost estimation." Automated Software Engineering 2, no. 3 (September 1995): 219–30. http://dx.doi.org/10.1007/bf00871798.

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40

Nalbandyan, М. А., and А. О. Nersisyan. "Environmental Risk Assessment in the Masrik River Basin." Agrology 4, no. 2 (2021): 71–76. http://dx.doi.org/10.32819/021009.

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Анотація:

The purpose of this study is to assess the environmental risks of water pollutionin the Masrik River catchment area. The risk assessment studies are based on the results of thehydrochemical monitoring of the Masrik River Basin water quality in 2012‒2014. The main riskfactors for pollution of the river basin area are mining, agriculture, uncontrolled utility flows.Zoning was performed and areas of zones were calculated using a digital elevation model (DEM)in the ArcGIS software environment and taking into account sampling points and river basins. Atthe same time, the territory was ranked by population density. Then a geodatabase (GDB) wascompiled. GDB presents the point assessments of the state of river waters according to the level ofthe pollutant concentration factor and population density. The thematic layers based on the resultsof the indicators in the ArcGIS program were compiled. A map was obtained according to a pointassessment of environmental risk. An integrated assessment of river water pollution was carriedout in different parts of the basin. The population density and total risk index were calculated. Thethree risk zones were identified in the river basin: high, medium and low. In addition to presentingtheoretical value, the conclusions are also of practical importance and can be used in the developmentof river basin risk management programs, measures for quality management and control ofpollution sources in the aread.

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41

Craigmyle, Mac, and Irwin Fletcher. "Improving IT effectiveness through software process assessment." Software Quality Journal 2, no. 4 (December 1993): 257–64. http://dx.doi.org/10.1007/bf00403768.

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42

Aggarwal, Alankrita, Kanwalvir Singh Dhindsa, and P. K. Suri. "A Pragmatic Assessment of Approaches and Paradigms in Software Risk Management Frameworks." International Journal of Natural Computing Research 9, no. 1 (January 2020): 13–26. http://dx.doi.org/10.4018/ijncr.2020010102.

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Software risk management involves the process of prior recognition and the assessment of vulnerabilities with the classification approach so that the risk avoidance mechanism can be implemented. It includes one of the key factors in software project management with the goal to improve quality as well as the avoidance of vulnerabilities. The term defect refers to the imperfection that may arise because of reasons including programmers' skills, lack of suitable testing strategies, and many others. When there is difference in actual and expected result or meeting the wrong requirement it is called a defect and it forms the basis of risk escalation in the software project, which is obviously not accepted in any type of deployment. To make software reliable, the software should be risk-free from any type of vulnerability factor. Along with reliability, another issue that has arisen is software quality in which the associated factor is with software risk management. The quality of software is to reduce the occurrence of risks and defects with the objective to produce effective valued software.

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43

Marx, Ben, and Deon Oosthuizen. "Risk assessment and mitigation at the information technology companies." Risk Governance and Control: Financial Markets and Institutions 6, no. 2 (2016): 44–51. http://dx.doi.org/10.22495/rcgv6i2art6.

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Developing computer software that is free from material defects is the ultimate goal for software developers; however, due to the cost and complexity of software development, it is a goal that is unlikely to be achieved. As a consequence of the inevitable defects that manifest within computer software, the task of software patch management becomes a key focus area for software companies, IT departments, and even end users. Audit departments, as part of their responsibilities, are required to provide assurance on the patching process and therefore need to understand the various decision-making factors. Software flaws that exist within computer systems may put confidential information at risk and may also compromise the availability of such systems. The study investigated the recommended approaches for the task of software patching, with a view to balancing the sometimes conflicting requirements of security and system availability. The study found that there are a number of key aspects that are required to ensure a successful patching process and that the internal auditors of the ‘big four’ South African banks considered most of these factors to be important.

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44

SCHNEIDEWIND, NORMAN. "APPLYING NEURAL NETWORKS TO SOFTWARE RELIABILITY ASSESSMENT." International Journal of Reliability, Quality and Safety Engineering 17, no. 04 (August 2010): 313–29. http://dx.doi.org/10.1142/s0218539310003834.

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Анотація:

We adapt concepts from the field of neural networks to assess the reliability of software, employing cumulative failures, reliability, remaining failures, and time to failure metrics. In addition, the risk of not achieving reliability, remaining failure, and time to failure goals are assessed. The purpose of the assessment is to compare a criterion, derived from a neural network model, for estimating the parameters of software reliability metrics, with the method of maximum likelihood estimation. To our surprise the neural network method proved superior for all the reliability metrics that were assessed by virtue of yielding lower prediction error and risk. We also found that considerable adaptation of the neural network model was necessary to be meaningful for our application – only inputs, functions, neurons, weights, activation units, and outputs were required to characterize our application.

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45

Al-Rousan, Thamer. "Cloud Computing for Global Software Development." International Journal of Cloud Applications and Computing 5, no. 1 (January 2015): 58–68. http://dx.doi.org/10.4018/ijcac.2015010105.

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Анотація:

The cloud computing paradigm offers an innovative and promising vision concerning Information and Communications Technology. Actually, it provides the possibility of improving IT systems management and is changing the way in which hardware and software are designed and purchased. This paper introduces challenges in Global Software Development (GSD) and application of cloud computing platforms as a solution to some problems. Even though cloud computing provides compelling benefits and cost-effective options for GSD, new risks and difficulties must be taken into account. Thus, the paper presents a study about the risk issues involved in cloud computing. It highlights the different types of risks and how their existence can affect GSD. It also proposes a new risk management process model. The risk model employs new processes for risk analysis and assessment. Its aim is to analyse cloud risks quantitatively and, consequently, prioritise them according to their impact on different GSD objectives.

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46

Pradhan, Padma Lochan. "Proposed Round Robin CIA Pattern on RTS for Risk Assessment." International Journal of Digital Crime and Forensics 9, no. 1 (January 2017): 71–85. http://dx.doi.org/10.4018/ijdcf.2017010105.

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The preventive, detective and corrective control is well advanced automated control in order to attain the maximum objectives of preserving the CIA mechanism on RTS. The risk analysis is the process of identifying the risk on RTS to achieving business objectives and deciding the measure components of RTOS on optimizing the lowest level of risk. This proposed round robin mechanism is going to be implemented on RTOS and mean while providing actionable, accountable & measurable for individuals who are accessing sensitive information on multiple relation functions, operation and services (RFOS) on multiple resources of RTS. We have to develop the RRP for risk assessment on MIMD. This research work going to be applied into security pattern on the measure component of the system security as well as RTOS. Furthermore, this RRP optimize the cost, time & resources is supposed to optimize the system risks and maximize the performance of business, resource & technology all the time & every time.

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Thanwadee T. Sunetnanta, and Morakot Choetkiertikul. "Quantitative CMMI Assessment for Software Process Quality and Risk Monitoring in Software Process Improvement." International Journal of Digital Content Technology and its Applications 6, no. 21 (November 30, 2012): 95–102. http://dx.doi.org/10.4156/jdcta.vol6.issue21.11.

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Garvey, P. R., D. J. Phair, and J. A. Wilson. "An Information Architecture For Risk Assessment And Management." IEEE Software 14, no. 3 (May 1997): 25–34. http://dx.doi.org/10.1109/ms.1997.589228.

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Bouquegneau, Christian. "The Risk Multilingual 3 Software: Applying the New Trends in Lightning Risk Assessment." Journal of Lightning Research 4, no. 1 (July 13, 2012): 129–32. http://dx.doi.org/10.2174/1652803401204010129.

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Jimoh, Rasheed Gbenga, Olayinka Olufunmilayo Olusanya, Joseph Bamidele Awotunde, Agbotiname Lucky Imoize, and Cheng-Chi Lee. "Identification of Risk Factors Using ANFIS-Based Security Risk Assessment Model for SDLC Phases." Future Internet 14, no. 11 (October 26, 2022): 305. http://dx.doi.org/10.3390/fi14110305.

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Анотація:

In the field of software development, the efficient prioritizing of software risks was essential and play significant roles. However, finding a viable solution to this issue is a difficult challenge. The software developers have to adhere strictly to risk management practice because each phase of SDLC is faced with its individual type of risk rather than considering it as a general risk. Therefore, this study proposes an adaptive neuro-fuzzy inference system (ANFIS) for selection of appropriate risk factors in each stages of software development process. Existing studies viewed the SDLC’s Security risk assessment (SRA) as a single integrated process that did not offer a thorough SRA at each stage of the SDLC process, which resulted in unsecure software development. Hence, this study identify and validate the risk factors needed for assessing security risk at each phase of SDLC. For each phase, an SRA model based on an ANFIS was suggested, using the identified risk factors as inputs. For the logical representation of the fuzzification as an input and output variables of the SRA risk factors for the ANFIS-based model employing the triangular membership functions. The proposed model utilized two triangular membership functions to represent each risk factor’s label, while four membership functions were used to represent the labels of the target SRA value. Software developers chose the SRA risk factors that were pertinent in their situation from the proposed taxonomy for each level of the SDLC process as revealed by the results. As revealed from the study’s findings, knowledge of the identified risk factors may be valuable for evaluating the security risk throughout the SDLC process.

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