Software Risk Management Research Articles

The Fault Diagnosis Method for Photovoltaic Modules Based on Machine Learning

Based on the urban natural gas pipeline accident statistics and semi-quantitative risk evaluation index system, this paper applies Bayesian network to establish a network model between various types of risk factors and the risk of natural gas pipeline failure. The EM algorithm was used to learn from the statistical accident data to obtain the parameters of the model. Based on the principle of evidential reasoning in reverse, the probability of occurrence of all risk indicators can be obtained when the probability of occurrence of urban natural gas pipeline accidents is 100%, the index weight is obtained by normalizing the occurrence probability. On this basis, this paper develops an efficient urban natural gas pipeline integrity risk identification and management software. The software can realize the basic data management of urban natural gas pipeline system, pipeline relative risk value calculation, pipeline risk level calculation and other functions, and the results are visualized. Finally, the practicability and effectiveness of the model and software are verified by a case of natural gas pipeline evaluation in a block.

Risk Evaluation and Software Development of Urban Natural Gas Pipelines Based on Bayesian Networks

Based on the urban natural gas pipeline accident statistics and semi-quantitative risk evaluation index system, this paper applies Bayesian network to establish a network model between various types of risk factors and the risk of natural gas pipeline failure. The EM algorithm was used to learn from the statistical accident data to obtain the parameters of the model. Based on the principle of evidential reasoning in reverse, the probability of occurrence of all risk indicators can be obtained when the probability of occurrence of urban natural gas pipeline accidents is 100%, the index weight is obtained by normalizing the occurrence probability. On this basis, this paper develops an efficient urban natural gas pipeline integrity risk identification and management software. The software can realize the basic data management of urban natural gas pipeline system, pipeline relative risk value calculation, pipeline risk level calculation and other functions, and the results are visualized. Finally, the practicability and effectiveness of the model and software are verified by a case of natural gas pipeline evaluation in a block.

Machine Learning for Credit Risk Prediction: A Systematic Literature Review

In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use Artificial Intelligence (AI) and ML to assess credit risk, analyzing large volumes of information. We posed research questions about algorithms, metrics, results, datasets, variables, and related limitations in predicting credit risk. In addition, we searched renowned databases responding to them and identified 52 relevant studies within the credit industry of microfinance. Challenges and approaches in credit risk prediction using ML models were identified; we had difficulties with the implemented models such as the black box model, the need for explanatory artificial intelligence, the importance of selecting relevant features, addressing multicollinearity, and the problem of the imbalance in the input data. By answering the inquiries, we identified that the Boosted Category is the most researched family of ML models; the most commonly used metrics for evaluation are Area Under Curve (AUC), Accuracy (ACC), Recall, precision measure F1 (F1), and Precision. Research mainly uses public datasets to compare models, and private ones to generate new knowledge when applied to the real world. The most significant limitation identified is the representativeness of reality, and the variables primarily used in the microcredit industry are data related to the Demographic, Operation, and Payment behavior. This study aims to guide developers of credit risk management tools and software towards the existing ability of ML methods, metrics, and techniques used to forecast it, thereby minimizing possible losses due to default and guiding risk appetite.

A Study of Software Defect Prediction Using Artificial Intelligent and Business Risk Management Approach

Abstract: The usage of several artificial intelligence algorithms in the area of software defect prediction is reviewed in the literature in this paper, it review the use of various techniques for artificial intelligence in the field of software defect prediction. Software system flaws remain a significant issue, and modern software development now faces many difficulties. Identification, estimation, and evaluation of the risks are crucial steps in order to avoid, minimize, and monitor the risks and their effects.This literature review is quite helpful because it improves understanding of the studied area. A software fault is a flaw in an executable product that results in software failure. Saving money, time, and effort may be accomplished by identifying general software process areas that may require attention from the start of a project and understanding the potential causes of problems. Planning, managing, and carrying out software development activities could be aided by the ability for early fault estimation. In order to study how many prediction approaches have been developed over this time to address the problems linked to software defect, this research conducts a literature review of works from the previous many years.In the field of software development, the study also discusses software risk management. This study makes a significant contribution by proposing a method for analyzing, organizing, and utilizing information technology initiatives in the context of software defect prediction and risk management.

HPR122 Current Healthcare Policy and Environmental Status of Digital Health Medical Devices in South Korea

COVID 19 and increasing unmet needs of health technology had accelerated an adoption of digital health globally and the major categories are mobile-health, health information technology, telemedicine. Digital health interventions have various benefit on clinical efficacy, quality of care and reducing healthcare costs. The objective of the study is to identify new reimbursement policy trend of digital health medical devices in South Korea. Official announcements published in national bodies and supplementary secondary research were used to capture policies, frameworks and currently approved products since 2019. With policy development, several digital health devices and AI software have been introduced as non-reimbursement by utilizing new Health Technology Assessment (nHTA) pathway including grace period of nHTA and innovative medical devices integrated assessment pathway. AI based cardiac arrest risk management software (DeepCARS®) and electroceutical device for major depressive disorders (MINDD STIM®) have been approved as non-reimbursement use for about 3 years. Two digital therapeutics for insomnia and AI software for diagnosis of cerebral infarction were approved as the first innovative medical devices under new integrated assessment system, and they could be treated in the market. In addition, there is remote patient monitoring (RPM) reimbursement service fee. Continuous glucose monitoring devices have been reimbursed for type 1 diabetes patients by the National Health Insurance Service (NHIS) since January 2019. Homecare RPM service for peritoneal dialysis patients with cloud platform (Sharesource®) has been reimbursed since December 2019, and long-term continuous ECG monitoring service fee for wearable ECG monitoring devices (ATpatch®, MEMO®) became reimbursement since January 2022. Although Korean government has been developed guidelines for digital health actively, only few products had been reimbursed. To introduce new technologies for improved patient centric treatment, novel value-based assessment and new pricing guideline of digital health medical devices are quite required.

Design and Implementation of a Track n trace 2D Multi Scanner Bulk Code Reader for Pharmaceutical Packaging

With various compliance initiatives surrounding the traceability of medical drugs coming into prominence and with federal regulations becoming more stringent, there is a need to efficiently manage the supply chain process involving drug packaging. To enable traceability of the drugs being packaged, many manufacturers are implementing barcoding of product labels which contains a minimum of the attributes namely GTIN, lot number, serial numbers, expiry date, manufacturing dates, etc., to enable traceability of the packaged item. Currently, manufacturers are looking for ways to keep their supply chain moving at an efficient and optimal process. One of the challenges was to build automated equipment providing a well-defined and properly structured framework for functioning, designing, and implementing of Track N Trace Counterfeit Risk Management (CRM) application to increase the speed of the aggregation with the child-to-parent aggregation and generation of online case labels after the required child's Quantity in Multi carton aggregation station. Track N Trace Counterfeit Risk Management (CRM) allows the manufacturers to establish the parent-child relationship during packaging their drugs. Also, various guidelines US Drug Supply Chain Security Act (DSCSA), EU regulation (Falsified Medicines Directive-FMD), Brazilian Health Regulatory Agency (ANVISA), Indonesia's National Agency of Drug and Food Control (NADFC), India’s Directorate General of Foreign Trade - DGFT, and Australia TGA will need to be integrated into one single source of equipment that can meet with their barcoding regulation for tracking of drugs packed. Machine Vision systems are finding themselves in use in the pharma machinery and the "multi-carton" equipment that has been built for implementation for pharmaceutical packaging uses the vision systems architecture. We aimed to build a counterfeit risk management software solution that ensures compliance with various regulatory agencies by building a common framework that can be extensible for any further changes to the guidelines and/or additional regulatory guidelines from other countries. Lots of design architecture work has been refined through continuous process improvement practices and we developed a software tool that improves efficiency and requires minimal changes to the current manufacturing/packaging supply chain lines.

GENERALIZED RISK ASSESSMENT PROCEDURE FOR SOFTWARE TESTING OF LEGALLY REGULATED MEASURING INSTRUMENTS

The legal metrology covers measuring instruments (MI), the measurement results of which are used in calculations for consumed energy resources, in the fields of information protection, security, environmental protection, etc. Most modern MIs use microcontrollers or are controlled by computers. The software (SW) of such MIs provides an opportunity not only to automate the processes of measurement and calculation of results but also to ensure long-term storage and data transfer. The manufacturer is responsible for investigating and assessing all possible risks related to the MI SW. The task of the conformity assessment body is to assess the conformity of MIs adequately in general and software, in particular, to the established requirements based on the analysis of risk classes. Standards for information security risk management, information technology security assessment, and information technology security assessment criteria consider only general issues of software security and risk assessment without taking into account the scope of its application. The existing regulatory documents on software risk management were considered. Modern methods of assessing the risks of the MI SW were studied. To assess the risks of software of legally regulated MIs, a general classification of threats and vulnerabilities of MI SW was made. For choosing threats that affect functionality, only those that affect metrological characteristics during measurement are taken into account. Possible manifestations of the impact of threats on stored data can be their distortion or destruction, and transmissions of data can be data distortion during transmission or data loss due to a break in the telecommunications connection. A proposed simplified risk assessment methodology for assessing the compliance of MI SW without statistical data on the probabilities of threats and the amount of harm from the implementation of threats is presented. Risk is defined as the probability of harm due to a certain vulnerability, taking into account the conditional amount of harm.

Upravljanje rizicima u softverskim projektima primenom alata veštačke inteligencije

In recent years, there has been a significant surge in interest in the incorporation of artificial intelligence (AI) within the field of software engineering. This phenomenon can be attributed to the fact that AI has become ubiquitous and increasingly accessible, thereby finding effective application across various pivotal facets of software systems. Its contribution extends not only to the creation of novel functionalities but also to the enhancement of existing processes within software projects, often resulting in substantially improved adaptability to specific user requirements. Within this paper, we provide an overview of the application of AI tools within one of the critical domains of software project management - risk management. To achieve this objective, a bibliometric analysis of literature pertaining to risk management in software projects employing AI tools has been conducted. The primary aim of this study is to identify and analyze key trends, authors, journals, and keywords within this multidisciplinary domain, in order to gain a better understanding of the progress and relevance of research concerning risk management in software projects utilizing AI tools. The methodology encompasses a review of pertinent databases and the identification of relevant publications using keywords associated with software projects, risk management, and artificial intelligence. Quantitative parameters such as the number of published works, author collaborations, citation frequency, and the distribution of articles over time and across journals are analyzed.

Implementing an Artificial Intelligence System in the Work of General Practitioner in the Yamalo-Nenets Autonomous Okrug: Pilot Cross-sectional Screening Observational Study

Background. Early identification of risk factors (RF) associated with cardiovascular diseases (CVD) is essential for the prevention of CVDs and their complications. CVD risk factors can be identified using Artificial Intelligence (AI) systems, which are capable of learning, analyzing and drawing conclusions. The advantage of AI systems consists in their capacity to process large amounts of data over a short period of time and produce ready-made information. Objectives. Evaluation of the efficiency of implementing an AI software application by a general practitioner for identifying CVD risk factors.Methods. The study included data from 1778 electronic medical histories of patients aged over 18, assigned to an outpatient and polyclinic department of Muravlenkovskaya Gorodskaya Bolnitsa (Muravlenko municipal hospital), Yamalo-Nenets Autonomous Okrug (Russia). The study was conducted in four stages. The first stage involved a preliminary training of the Artificial Intelligence (AI) system under study using numerous CVD risk assessment scales. The Webiomed predictive analytics and risk management software by K-SkAI, Russia, was selected as a platform for this purpose. The second stage included an analysis of medical data to identify CVD risk factors according to the relative risk scale for patients under 40 and the SCORE scale for patients over 40. At the third stage, a specialist analyzed the previous and new information received about each patient. According to the results of the third stage, four risk groups for CVD (low, medium, high and very high) were formed. At the fourth stage, newly diagnosed patients with a high risk of CVD, who had not been previously subject to regular medical check-up, were directed for additional clinical, laboratory and instrumental follow-up examination and consultations of relevant specialists. Statistical data in absolute terms and as a percentage were obtained. Statistical processing of the results was carried out by a computer program aimed at medical decision support. Content visualization was performed in spreadsheets and charts.Results. Based on the data obtained, the AI system under study divided all patients into CVD risk groups and identified uncounted factors. The AI system confirmed a high and very high risk of CVD according to SCORE (Systematic COronary Risk Evaluation) in 623 people, who were already receiving appropriate cardiological assistance. The RFs that had not previously been taken into account in the diagnosis were recorded in 41 (11.5%) patients from the very highrisk group and in 37 (12.7%) high-risk patients. The AI system identified a high risk of CVD in 29 people who had not been previously under care of a general practitioner or other specialists due to their infrequent visits to health care facilities. These patients were detected by the AI system following periodic and preliminary medical check-ups (35%), full in-patient treatment for other diseases (31%), when seeking help of other specialists (17%), as well as when obtaining a medical certificate for a driving license (12%), admission to a swimming pool (3%) or possessing a weapon (2%). In a group with the newly diagnosed patients at a high risk of CVD, men dominated (24 persons, 82%) and women comprised only 8% (5 persons). All these people were of working age between 40 and 50. In order to confirm the information received, the supervising physician subsequently referred patients for a follow-up examination, as a result of which only 1 person (3%) was not diagnosed with a somatic pathology.Conclusion. The efficiency of the AI system under study comprised 97%. Permanent monitoring of all parameters of electronic medical histories and outpatient records is an efficient method for timely identification of RF at any visit of a person to a health care facility (preventive and periodic medical examinations, regular check-ups, specialist consultations, etc.) and their assignment to respective CVD risk groups. Such monitoring ensures an effective medical supervision of able-bodied populations.

A Common Terminology for Software Risk Management

In order to improve and sustain their competitiveness over time, organisations nowadays need to undertake different initiatives to adopt frameworks, models and standards that will allow them to align and improve their business processes. In spite of these efforts, organisations may still encounter governance and management problems. This is whereRisk Management (RM)can play a major role, since its purpose is to contribute to the creation and preservation of value in the context of the organisation's processes. RM is a complex and subjective activity that requires experience and a high level of knowledge about risks, and it is for this reason that standardisation institutions and researchers have made great efforts to define initiatives to overcome these challenges. However, the RM field nevertheless presents a lack of uniformity in its terms and concepts, due to the different contexts and scopes of application, a situation that can generate ambiguities and misunderstandings. To address these issues, this paper aims to present an ontology calledSRMO (Software Risk Management Ontology), which seeks to unify the terms and concepts associated with RM and provide an integrated and holistic view of risk. In doing so, the Pipeline framework has been applied in order to assure and verify the quality of the proposed ontology, and it has been implemented in Protégé and validated by means of competency questions. Three application scenarios of this ontology demonstrating their usefulness in the software engineering field are presented in this paper. We believe that this ontology can be useful for organisations that are interested in: (i) establishing an RM strategy from an integrated approach, (ii) defining the elements that help to identify risks and the criteria that support decision-making in risk assessment, and (iii) helping the involved stakeholders during the process of risk management.

An Evidential Software Risk Evaluation Model

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.

Risk Identification and Analysis in Software Development in Bangladesh IT Industry: A Hybrid Model

Software risk management is a critical and multi-stage process. All over the world, IT Industries face some threats during software risk management processes. Bangladesh is not exceptional. Our principal goal is to manage risk for Bangladesh's IT Industry. To gain a clear and transparent idea survey is the most effective way. And so, we arranged a survey questionnaire and collected data for risk impact areas on Bangladesh IT Industry. A workable and feasible risk management approach prompts an idea for the risk-mitigating plan. In any case, the high proportion of IT project failures demonstrates the futility of risk mitigation activities. From the survey, it is discovered that the most irritating obstructions behind software disappointment for the presence of covered-up and inconspicuous risks and lack of user communication, and lack of proper training on new technology which is overlooked in the greater part of the models. The proposed model works with the improvement of the risk mitigation plan through four phases, the DVC committee, and the NUT train-up team. Depending upon the survey replies added another unique feature called the NUT train-up team. The model considered all four phases of risk management, with the mitigation phase and training on new technologies receiving the greatest attention.

Adaptation of Porter's Five Forces Model to Risk Management

Prominent tools for assessing and managing risk include risk cubes, risk burndown charts, and automated risk management software. They are generally lacking, however, in accommodating ideation and brainstorming to identify potential problems. A suggested approach for improving the process is to apply strategic management models currently used as commercial best practices. Many are directly applicable and adaptable to systems engineering processes including risk management. This article presents traditional risk tools and introduces a complementary management model tailored to the identification, scoring, and tracking of potential program threats. Additional management models are presented for further investigation and adaptation.

Analysis of Risk Management in Construction Projects Using SPSS method

Risk management is about projects Risk analysis, identification like seeing and responding Defined as a formal process. It is negative events Reduce probability and impact While positive events Opportunities and impact includes increasing, among others to meet project objectives. Construction Risk Management is Occurs in construction projects To reduce the impact of risks Evaluate procedures is the process of implementation. This risk the management process is risk management Complete to build project Involves planning, which is planning Managers identify risks, Allows monitoring and mitigation. Project like project manager Management Software Risk Management Makes the process much easier. Full Risk with the feature Gantt chart Develop a management plan, then real-time dashboards and manage your projects with reports Monitor. Try it for free today of the construction planning process Construction risk management in early stages The project was created. What is this project Risks can occur and take them Describes the risk response to judgment? And to take ownership of the problem Someone on the team to resolve it This includes recruiting. Construction Risk Management and Fire Prevention Plan There are many benefits to creating, including: Increased safety and workers well being of improved company Reputation. with workplace accidents Avoid associated costs. Construction projects are a building or to build a structure It is an organized effort. Civil Engineering and Architecture In the fields, construction Projects are an infrastructure or Integrates the building firmly involves the process. Evaluation parameters: Political instability risk, Economic risk,Social risk, Work quality risk, Time and cost risk, Technological risk, Documents and information risk, Stakeholder’s risk.

Software Risk Assessment and Management with Rules Based on Fuzzy Approach

Up to now, several software risk parameters have been determined in order to assess and manage software development projects: Productivity, Engagement, Attention to Quality, Code Based Knowledge and Management, Adherence to Coding Guidelines and Techniques, Learning and Skills, Personal Responsibility and etc. However, there isn’t any universally accepted methodology to apply software risk assessment and management. There are three main reasons of this situation: Firstly, each part of software creation is unique. There is no compelling reason to assemble two times the same parts of software as it might be duplicated by copying it. This makes it truly difficult to make a formal and thorough correlation between two parts of software. Secondly, the current technology is something that changes at a truly fast phase. So, each time a methodology in respect to a certain wave of technology is dependable enough, it is for the most part as of recently old. Thirdly, there is a gigantic zone for innovativeness in discovering the diverse answers for a unique issue. Because of these reasons, the technique “Fuzzy Approach” has a very convenient and proper process for defining software risks due to their nature that has no certainty – uncertainty – structure and principle. Also, software risks are defined as the probability and the severity of damages that are caused by occurring of bad or undesirable events in a system. Thus, the system suffers from strategic, financial, operational, structural or integrity loss and damage. So, there is need to apply and carry out an efficient “Software Risk Assessment and Management” in order to determine and recognize software risks on time before causing problems and troubles into software projects for providing successfully accomplishment in software development process. In this paper, usability and efficiency of “Fuzzy Approached” linguistic and logical rules based on “Fuzzy Logic” in “Software Risk Assessment and Management” have been shown and expressed in detail.

Development of Rule-Based Software Risk Assessment and Management Method with Fuzzy Inference System

There is an enormous budget and financial plan in software development projects, and it is required that they take a huge investment to carry on. When looked at, the costs depend on the global substantial information about software development: in 1985, $150 billion; in 2010, $2 trillion; in 2015, $5 trillion; and in 2020, over $7 trillion. Additionally, on the first new days of 2021, a day-by-day Apple Store’s quantity has been approximately $500 million. In spite of the expenditures and the margins that are dramatically expanding and increasing each year, the phase of software development accomplishment is not high enough. In light of the “CHAOS” report arranged in 2015, just 17% of the software projects were finished in an opportune way, in the allotted financial plan, and as per the necessities. However, 53% of the software projects were finished in the long run or potentially over a spending plan as well as without satisfying the prerequisites precisely. In addition, software development projects were not completed and were dropped out as well in the ratio of 30%. Also, the “CHAOS” report published in 2020 has figured out that only 33% of the software projects were completed successfully all over the world. In order to cope with these unsuccessful and failure results, an effective method for software risk assessment and management has to be specified, designated, and applied. In this way, before causing trouble that has the power of preventing successful accomplishment of software development projects, software risks are able to be noticed and distinguished on time. In this study, a new and original rule set, which could be used and carried out effectively in software risk assessment and management, has been designed and developed based on the implementation of fuzzy approached technique integrated with machine learning algorithm—Adaptive Neuro-Fuzzy Inference System (ANFIS). By this approach and technique, machines (computers) are able to create several software risk rules not to be seen, not to be recognized, and not to be told by human beings. In addition, this fuzzy inference approach aims to decrease risks in the software development process in order to increase the success rate of the software projects. Also, the experimental results of this approach show that rule-based software risk assessment and management method has a valid and accurate model with a high accuracy rate and low average testing error.

IMPROVING THE EFFECTIVENESS OF THE RISK MANAGEMENT SYSTEM

The article considers the importance of an effective risk management system in the conditions of uncertainty and instability for Russian companies. It concludeis that it is necessary to implement such a system to improve the management efficiency of a modern organization, using the example of PJSC “Geotek Seismo Razvedka”. The advantages and disadvantages of the existing risk management policy are identified. The authors suggest measures to improve the risk management system for this company. The article proposes to develope (define) the key risk indicators, offers to intensify risk management activities in the field of risk monitoring. To do this, the authors recommend to implement the Omnitracker Risk Management software product as a system for automating the company’s risk management processes. The advantages of this product are considered, and a schedule for the implementation of Omnitracker Risk Management in PJSC “Geotek Seismo Razvedka”is developed.

Resource Optimization-Based Software Risk Reduction Model for Large-Scale Application Development

Software risks are a common phenomenon in the software development lifecycle, and risks emerge into larger problems if they are not dealt with on time. Software risk management is a strategy that focuses on the identification, management, and mitigation of the risk factors in the software development lifecycle. The management itself depends on the nature, size, and skill of the project under consideration. This paper proposes a model that deals with identifying and dealing with the risk factors by introducing different observatory and participatory project factors. It is assumed that most of the risk factors can be dealt with by doing effective business processing that in response deals with the orientation of risks and elimination or reduction of those risk factors that emerge over time. The model proposes different combinations of resource allocation that can help us conclude a software project with an extended amount of acceptability. This paper presents a Risk Reduction Model, which effectively handles the application development risks. The model can synchronize its working with medium to large-scale software projects. The reduction in software failures positively affects the software development environment, and the software failures shall reduce consequently.

Performance-Aware Approach for Software Risk Management Using Random Forest Algorithm

Software quality assurance and related methodologies are quite prominent before actual launching the application so that any type of issues can be resolved at prior notifications. The process of software evaluation is one of the key tasks that are addressed by the quality assurance teams so that the risks in the software suite can be identified and can be removed with prior notifications. Different types of metrics can be used in defect prediction model and widely used metrics are source code and process metrics. The focus of this research manuscript is to develop a narrative architecture and design for software risk management using soft computing in integration with the proposed approach of random forest approach is expected to have the effectual results on multiple parameters with the flavor of multiple decision trees. The proposed approach is integrated with the framework of meta-heuristics with random forest in different substances and elements to produce a new substance.

Developing Relationship Among Risk Factors and Project Factors for Large Scale Healthcare Applications

The impact and probability of healthcare software risks are widely discussed in software risk management. The identification of healthcare software risk factor will not be useful without measuring its impact and probability. The impact filed determines the damage level of the risk factors if it appears, while the probability determines how probable is the existence of that risk. The risks of having high probability and higher impact levels have to be addressed urgently. The orientation of the risks takes place in response to unbalanced resource allocation. Establishing an association among the project factors and risk factors helps in gaining more effective control of the resource allocation to the healthcare application development. This paper identifies the conceptual and numerical association between the project factors and risk factors by conducting a mixed-method research analysis to optimize the resource allocation and reduce (eliminate) the software risks in large scale healthcare applications. The resultant associations between the project factors and risk factors are categorized as weak or strong based on the resultant data of this study and can be used as a guideline document for developing the large scale healthcare systems.