Acritical process in a software project life-cycle is risk assessment and mitigation. Risks exist in every software project and recognizing and evaluatingrisks and uncertainties is a challenging process for practitioners with little historical data. In our study, by using a survey data, we identify and provide a relatively wider coverage of risks and ratings of software project. The risk register and evaluations are
useful for practitioner of small organizations at initial phase of risk identification and assessment. There are 128 risks in this study which are analyzed. Furthermore, the study provides a top risk list according to this study alongside a highly cross correlatedrisks table. Additionally, previous studies have also provided top risks lists regarding the corresponding surveys. This study extends previous studies to provide a recent
risk study. Outcomes are also compared to previous works in discussion.
Acritical process in a software project life-cycle is risk assessment and mitigation. Risks exist in every
software project and recognizing and evaluating risks and uncertainties is a challenging process for
practitioners with little historical data. In our study, by using a survey data, we identify and provide a relatively wider coverage of risks and ratings of software project. The risk register and evaluations are useful for practitioner of small organizations at initial phase of risk identification and assessment. There
are 128 risks in this study which are analyzed. Furthermore, the study provides a top risk list according to this study alongside a highly cross correlated risks table. Additionally, previous studies have also provided top risks lists regarding the corresponding surveys. This study extends previous studies to provide a recent risk study. Outcomes are also compared to previous works in discussion.
One of the most important issues that organizations have to deal with is the timely identification and detection of risk factors aimed at preventing incidents. Managers’ and engineers’ tendency towards minimizing risk factors in a service, process or design system has obliged them to analyze the reliability of such systems in order to minimize the risks and identify the probable errors. Concerning what was just mentioned, a more accurate Failure Mode and Effects Analysis (FMEA) is adopted based on fuzzy logic and fuzzy numbers. Fuzzy TOPSIS is also used to identify, rank, and prioritize error and risk factors. This paper uses FMEA as a risk identification tool. Then, Fuzzy Risk Priority Number (FRPN) is calculated and triangular fuzzy numbers are prioritized through Fuzzy TOPSIS. In order to have a better understanding toward the mentioned concepts, a case study is presented.
Risk Assessment Model and its Integration into an Established Test Processijtsrd
In industry, testing has to be performed under severe pressure due to limited resources. Risk based testing which uses risks to guide the test process is applied to allocate resources and to reduce product risks. Risk assessment, i.e., risk identi cation, analysis and evaluation, determines the signi cance of the risk values assigned to tests and therefore the quality of the overall risk based test process. In this paper we provide a risk assessment model and its integration into an established test process. This framework is derived on the basis of best practices extracted from published risk based testing approaches and applied to an industrial test process. Ashwani Kumar | Prince Sood "Risk Assessment Model and its Integration into an Established Test Process" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26757.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26757/risk-assessment-model-and-its-integration-into-an-established-test-process/ashwani-kumar
Application Development Risk Assessment Model Based on Bayesian NetworkTELKOMNIKA JOURNAL
This paper describes a new risk assessment model for application development and its
implementation. The model is developed using a Bayesian network and Boehm’s software risk principles.
The Bayesian network is created after mapping top twenty risks in software projects with interrelationship
digraph of risk area category. The probability of risk on the network is analyzed and validated using both
numerical simulation and subjective probability from several experts in the field and a team of application
developers. After obtaining the Bayesian network model, risk exposure is calculated using Boehm's risk
principles. Finally, the implementation of the proposed model in a government institution is shown as a real
case illustration.
Risk Driven Approach to Test Device Softwareijtsrd
Software testing is one of the most crucial testings in the software development process. Software testing should be scheduled and managed very effectively. The risk is the situation that has not occurred yet and may not occur in the future as well. After looking at this definition, risks can refer to the probability of the failure for a particular project. Risk based testing is the type of testing that is based on the priority and importance of the software that has to be tested. In this research work, the new technique to test the device software has been proposed using the JAVA language. The new system is able to test the software based on various risks and provide alternatives based on that the risk can be reduced in the future. It also calculates the updated cost and duration required to complete the software when a risk has occurred. The proposed application is able to provide efficient and accurate results in terms of entered risks on the device software. In the future, the software can be used to test the device software for more number of risks to make it more suitable as per the user's requirements. Ashwani Kumar | Prince Sood "Risk Driven Approach to Test Device Software" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25230.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/25230/risk-driven-approach-to-test-device-software/ashwani-kumar
Application of the analytic hierarchy process (AHP) for selection of forecast...Gurdal Ertek
In this paper, we described an application of the Analytic Hierarchy Process (AHP) for the ranking and selection of forecasting software. AHP is a multi-criteria decision making (MCDM) approach, which is based on the pair-wise comparison
of elements of a given set with respect to multiple criteria. Even though there are applications of the AHP to software selection problems, we have not encountered a study that involves forecasting software. We started our analysis by filtering
among forecasting software that were found on the Internet by undergraduate students as a part of a course project. Then we processed a second filtering step, where we reduced the number of software to be examined even further. Finally we
constructed the comparison matrices based upon the evaluations of three “semiexperts”, and obtained a ranking of forecasting software of the selected software using the Expert Choice software. We report our findings and our insights, together with the results of a sensitivity analysis.
http://research.sabanciuniv.edu.
Acritical process in a software project life-cycle is risk assessment and mitigation. Risks exist in every
software project and recognizing and evaluating risks and uncertainties is a challenging process for
practitioners with little historical data. In our study, by using a survey data, we identify and provide a relatively wider coverage of risks and ratings of software project. The risk register and evaluations are useful for practitioner of small organizations at initial phase of risk identification and assessment. There
are 128 risks in this study which are analyzed. Furthermore, the study provides a top risk list according to this study alongside a highly cross correlated risks table. Additionally, previous studies have also provided top risks lists regarding the corresponding surveys. This study extends previous studies to provide a recent risk study. Outcomes are also compared to previous works in discussion.
One of the most important issues that organizations have to deal with is the timely identification and detection of risk factors aimed at preventing incidents. Managers’ and engineers’ tendency towards minimizing risk factors in a service, process or design system has obliged them to analyze the reliability of such systems in order to minimize the risks and identify the probable errors. Concerning what was just mentioned, a more accurate Failure Mode and Effects Analysis (FMEA) is adopted based on fuzzy logic and fuzzy numbers. Fuzzy TOPSIS is also used to identify, rank, and prioritize error and risk factors. This paper uses FMEA as a risk identification tool. Then, Fuzzy Risk Priority Number (FRPN) is calculated and triangular fuzzy numbers are prioritized through Fuzzy TOPSIS. In order to have a better understanding toward the mentioned concepts, a case study is presented.
Risk Assessment Model and its Integration into an Established Test Processijtsrd
In industry, testing has to be performed under severe pressure due to limited resources. Risk based testing which uses risks to guide the test process is applied to allocate resources and to reduce product risks. Risk assessment, i.e., risk identi cation, analysis and evaluation, determines the signi cance of the risk values assigned to tests and therefore the quality of the overall risk based test process. In this paper we provide a risk assessment model and its integration into an established test process. This framework is derived on the basis of best practices extracted from published risk based testing approaches and applied to an industrial test process. Ashwani Kumar | Prince Sood "Risk Assessment Model and its Integration into an Established Test Process" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26757.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26757/risk-assessment-model-and-its-integration-into-an-established-test-process/ashwani-kumar
Application Development Risk Assessment Model Based on Bayesian NetworkTELKOMNIKA JOURNAL
This paper describes a new risk assessment model for application development and its
implementation. The model is developed using a Bayesian network and Boehm’s software risk principles.
The Bayesian network is created after mapping top twenty risks in software projects with interrelationship
digraph of risk area category. The probability of risk on the network is analyzed and validated using both
numerical simulation and subjective probability from several experts in the field and a team of application
developers. After obtaining the Bayesian network model, risk exposure is calculated using Boehm's risk
principles. Finally, the implementation of the proposed model in a government institution is shown as a real
case illustration.
Risk Driven Approach to Test Device Softwareijtsrd
Software testing is one of the most crucial testings in the software development process. Software testing should be scheduled and managed very effectively. The risk is the situation that has not occurred yet and may not occur in the future as well. After looking at this definition, risks can refer to the probability of the failure for a particular project. Risk based testing is the type of testing that is based on the priority and importance of the software that has to be tested. In this research work, the new technique to test the device software has been proposed using the JAVA language. The new system is able to test the software based on various risks and provide alternatives based on that the risk can be reduced in the future. It also calculates the updated cost and duration required to complete the software when a risk has occurred. The proposed application is able to provide efficient and accurate results in terms of entered risks on the device software. In the future, the software can be used to test the device software for more number of risks to make it more suitable as per the user's requirements. Ashwani Kumar | Prince Sood "Risk Driven Approach to Test Device Software" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25230.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/25230/risk-driven-approach-to-test-device-software/ashwani-kumar
Application of the analytic hierarchy process (AHP) for selection of forecast...Gurdal Ertek
In this paper, we described an application of the Analytic Hierarchy Process (AHP) for the ranking and selection of forecasting software. AHP is a multi-criteria decision making (MCDM) approach, which is based on the pair-wise comparison
of elements of a given set with respect to multiple criteria. Even though there are applications of the AHP to software selection problems, we have not encountered a study that involves forecasting software. We started our analysis by filtering
among forecasting software that were found on the Internet by undergraduate students as a part of a course project. Then we processed a second filtering step, where we reduced the number of software to be examined even further. Finally we
constructed the comparison matrices based upon the evaluations of three “semiexperts”, and obtained a ranking of forecasting software of the selected software using the Expert Choice software. We report our findings and our insights, together with the results of a sensitivity analysis.
http://research.sabanciuniv.edu.
Project risk management model based on prince2 and scrum frameworksijseajournal
Agile methods grew out of the real-life project experiences of leading software professionals who
had experienced the challenges and limitations of traditional waterfall development
methodologies on projects after projects. The agile development frameworks are widely used and
they don’t contain any risk management techniques because it is believed that short iterative
development cycles will minimize any unpredictable impact related to product development [1],
[2]. However in larger projects or during development of complex products, especially in the
global environment, the need of proper risk management is required. From the audit perspective,
there is the clear control requirement “BAI01.10 Manage programme and project risk“ defined by
COBIT 5 that requires that project risks should be systematically identified, analysed, responded
to, monitored and controlled. Additionally the risks should be centrally recorded [3, p. 125].
Additionally, controlling risk in software projects is considered to be a major contributor to
project success [4].
The need to manage risks in agile project management is also identified by various authors.
The SOA principles from the agile project management perspective were used to create
a framework for understanding agile risk management strategies for global IT projects [5]. Main
risk models and frameworks used by software engineers are discussed with conclusion that
the risk management steps are required for delivery of quality software [6], [7]. Agile
methodologies don’t cover the risk management knowledge area that can be taken from project
management frameworks like PMBOK [8]. Risks related to global software development projects
using Scrum have been researched and a conceptual framework to mitigate them designed [9].
Also the increasing variety of security threats should be managed as risks in the agile
development projects [10], [11].
A NEW MATHEMATICAL RISK MANAGEMENT MODEL FOR AGILE SOFTWARE DEVELOPMENT METHO...ijseajournal
This paper proposes a new mathematical model for estimating the cost of explicit Agile software
development risk management with its Impact Benefit s (savings/profits). This is necessitated by the fact
that despite the increase in the need for managing risks explicitly in medium-to-large scale agile software
development projects presently, there are no known ways to estimate explicit risk management
costs/benefits. With the proposed model, explicit risk management procedures alongside with risk
management estimation techniques is made known to Stakeholders who will be able to make the right
decisions on risk management costs and its impacts as well as when to utilise implicit or explicit risk
management. The proposed system proves to be feasible and dependable and is evidently capable of
enhancing the agile methods for use for all sizes of software projects while still maintaining the swiftness of
the agile process.
Software Cost Estimation Using Clustering and Ranking SchemeEditor IJMTER
Software cost estimation is an important task in the software design and development process.
Planning and budgeting tasks are carried out with reference to the software cost values. A variety of
software properties are used in the cost estimation process. Hardware, products, technology and
methodology factors are used in the cost estimation process. The software cost estimation quality is
measured with reference to the accuracy levels.
Software cost estimation is carried out using three types of techniques. They are regression based
model, anology based model and machine learning model. Each model has a set of technique for the
software cost estimation process. 11 cost estimation techniques fewer than 3 different categories are
used in the system. The Attribute Relational File Format (ARFF) is used maintain the software product
property values. The ARFF file is used as the main input for the system.
The proposed system is designed to perform the clustering and ranking of software cost
estimation methods. Non overlapped clustering technique is enhanced with optimal centroid estimation
mechanism. The system improves the clustering and ranking process accuracy. The system produces
efficient ranking results on software cost estimation methods.
Practical Guidelines to Improve Defect Prediction Model – A Reviewinventionjournals
Defect prediction models are used to pinpoint risky software modules and understand past pitfalls that lead to defective modules. The predictions and insights that are derived from defect prediction models may not be accurate and reliable if researchers do not consider the impact of experimental components (e.g., datasets, metrics, and classifiers) of defect prediction modeling. Therefore, a lack of awareness and practical guidelines from previous research can lead to invalid predictions and unreliable insights. Through case studies of systems that span both proprietary and open-source domains, find that (1) noise in defect datasets; (2) parameter settings of classification techniques; and (3) model validation techniques have a large impact on the predictions and insights of defect prediction models, suggesting that researchers should carefully select experimental components in order to produce more accurate and reliable defect prediction models.
SOFTWARE COST ESTIMATION USING FUZZY NUMBER AND PARTICLE SWARM OPTIMIZATIONIJCI JOURNAL
Software cost estimation is a process to calculate effort, time and cost of a project, and assist in better
decision making about the feasibility or viability of project. Accurate cost prediction is required to
effectively organize the project development tasks and to make economical and strategic planning, project
management. There are several known and unknown factors affect this process, so cost estimation is a very
difficult process. Software size is a very important factor that impacts the process of cost estimation.
Accuracy of cost estimation is directly proportional to the accuracy of the size estimation.
Failure of Software projects has always been an important area of focus for the Software Industry.
Implementation phase is not the only phase for Software projects to fail, instead planning and estimation
steps are the most crucial ones, which lead to their failure. More than 50% of the total projects fail which
go beyond the estimated time and cost. The Standish group‘s CHAOS reports failure rate of 70% for the
software projects. This paper presents the existing algorithms for software estimation and the relevant
concepts of Fuzzy Theory and PSO. Also explains the proposed algorithm with experimental results.
Managing in the Presence of UncertantyGlen Alleman
Managing in the Presence of Uncertainty requires making decision with Models of that Uncertainty
Monte Carlo Simulation and some related approaches can be the basis of making informed decisions in the presence of Uncertainty
A guide to deal with uncertainties in software project managementijcsit
Various project management approaches do not consider the impact that uncertainties have on the project.
The identified threats by uncertainty in a projec day-to-day are real and immediate and the expectations in
a project are often high. The project manager faces a dilemma: decisions must be made in the present
about future situations which are inherently uncertain. The use of uncertainty management in project can
be a determining factor for the project success. This paper presents a systematic review about uncertainties
management in software projects and a guide is proposed based on the review. It aims to present the best
practices to manage uncertainties in software projects in a structured way including techniques and
strategies to uncertainties containment.
A REVIEW OF SECURITY INTEGRATION TECHNIQUE IN AGILE SOFTWARE DEVELOPMENTijseajournal
Agile software development has gained a lot of popularity in the software industry due to its iterative and
incremental approach as well as user involvement. Agile has also been criticized due to lack of its ability to
deliver secure software. In this paper, extensive literature has been performed, in order to highlight the
existing security issues in agile software development. Majority of challenges reported in literature,
occurred due to lack of involvement of security expert. Improving security of a software system without
damaging the real essence of Agile can achieved with the continuous involvement of security engineer
throughout development lifecycle with its defined role and responsibilities.
The most common consequences of ineffective or no procedural risk management are the excessive time spent by managers in dealing with unanticipated difficulties.
Acritical process in a software project life-cycle is risk assessment and mitigation. Risks exist in every
software project and recognizing and evaluating risks and uncertainties is a challenging process for
practitioners with little historical data. In our study, by using a survey data, we identify and provide a
relatively wider coverage of risks and ratings of software project. The risk register and evaluations are
useful for practitioner of small organizations at initial phase of risk identification and assessment. There
are 128 risks in this study which are analyzed. Furthermore, the study provides a top risk list according to
this study alongside a highly cross correlatedrisks table. Additionally, previous studies have also provided
top risks lists regarding the corresponding surveys. This study extends previous studies to provide a recent
risk study. Outcomes are also compared to previous works in discussion.
Project risk management model based on prince2 and scrum frameworksijseajournal
Agile methods grew out of the real-life project experiences of leading software professionals who
had experienced the challenges and limitations of traditional waterfall development
methodologies on projects after projects. The agile development frameworks are widely used and
they don’t contain any risk management techniques because it is believed that short iterative
development cycles will minimize any unpredictable impact related to product development [1],
[2]. However in larger projects or during development of complex products, especially in the
global environment, the need of proper risk management is required. From the audit perspective,
there is the clear control requirement “BAI01.10 Manage programme and project risk“ defined by
COBIT 5 that requires that project risks should be systematically identified, analysed, responded
to, monitored and controlled. Additionally the risks should be centrally recorded [3, p. 125].
Additionally, controlling risk in software projects is considered to be a major contributor to
project success [4].
The need to manage risks in agile project management is also identified by various authors.
The SOA principles from the agile project management perspective were used to create
a framework for understanding agile risk management strategies for global IT projects [5]. Main
risk models and frameworks used by software engineers are discussed with conclusion that
the risk management steps are required for delivery of quality software [6], [7]. Agile
methodologies don’t cover the risk management knowledge area that can be taken from project
management frameworks like PMBOK [8]. Risks related to global software development projects
using Scrum have been researched and a conceptual framework to mitigate them designed [9].
Also the increasing variety of security threats should be managed as risks in the agile
development projects [10], [11].
A NEW MATHEMATICAL RISK MANAGEMENT MODEL FOR AGILE SOFTWARE DEVELOPMENT METHO...ijseajournal
This paper proposes a new mathematical model for estimating the cost of explicit Agile software
development risk management with its Impact Benefit s (savings/profits). This is necessitated by the fact
that despite the increase in the need for managing risks explicitly in medium-to-large scale agile software
development projects presently, there are no known ways to estimate explicit risk management
costs/benefits. With the proposed model, explicit risk management procedures alongside with risk
management estimation techniques is made known to Stakeholders who will be able to make the right
decisions on risk management costs and its impacts as well as when to utilise implicit or explicit risk
management. The proposed system proves to be feasible and dependable and is evidently capable of
enhancing the agile methods for use for all sizes of software projects while still maintaining the swiftness of
the agile process.
Software Cost Estimation Using Clustering and Ranking SchemeEditor IJMTER
Software cost estimation is an important task in the software design and development process.
Planning and budgeting tasks are carried out with reference to the software cost values. A variety of
software properties are used in the cost estimation process. Hardware, products, technology and
methodology factors are used in the cost estimation process. The software cost estimation quality is
measured with reference to the accuracy levels.
Software cost estimation is carried out using three types of techniques. They are regression based
model, anology based model and machine learning model. Each model has a set of technique for the
software cost estimation process. 11 cost estimation techniques fewer than 3 different categories are
used in the system. The Attribute Relational File Format (ARFF) is used maintain the software product
property values. The ARFF file is used as the main input for the system.
The proposed system is designed to perform the clustering and ranking of software cost
estimation methods. Non overlapped clustering technique is enhanced with optimal centroid estimation
mechanism. The system improves the clustering and ranking process accuracy. The system produces
efficient ranking results on software cost estimation methods.
Practical Guidelines to Improve Defect Prediction Model – A Reviewinventionjournals
Defect prediction models are used to pinpoint risky software modules and understand past pitfalls that lead to defective modules. The predictions and insights that are derived from defect prediction models may not be accurate and reliable if researchers do not consider the impact of experimental components (e.g., datasets, metrics, and classifiers) of defect prediction modeling. Therefore, a lack of awareness and practical guidelines from previous research can lead to invalid predictions and unreliable insights. Through case studies of systems that span both proprietary and open-source domains, find that (1) noise in defect datasets; (2) parameter settings of classification techniques; and (3) model validation techniques have a large impact on the predictions and insights of defect prediction models, suggesting that researchers should carefully select experimental components in order to produce more accurate and reliable defect prediction models.
SOFTWARE COST ESTIMATION USING FUZZY NUMBER AND PARTICLE SWARM OPTIMIZATIONIJCI JOURNAL
Software cost estimation is a process to calculate effort, time and cost of a project, and assist in better
decision making about the feasibility or viability of project. Accurate cost prediction is required to
effectively organize the project development tasks and to make economical and strategic planning, project
management. There are several known and unknown factors affect this process, so cost estimation is a very
difficult process. Software size is a very important factor that impacts the process of cost estimation.
Accuracy of cost estimation is directly proportional to the accuracy of the size estimation.
Failure of Software projects has always been an important area of focus for the Software Industry.
Implementation phase is not the only phase for Software projects to fail, instead planning and estimation
steps are the most crucial ones, which lead to their failure. More than 50% of the total projects fail which
go beyond the estimated time and cost. The Standish group‘s CHAOS reports failure rate of 70% for the
software projects. This paper presents the existing algorithms for software estimation and the relevant
concepts of Fuzzy Theory and PSO. Also explains the proposed algorithm with experimental results.
Managing in the Presence of UncertantyGlen Alleman
Managing in the Presence of Uncertainty requires making decision with Models of that Uncertainty
Monte Carlo Simulation and some related approaches can be the basis of making informed decisions in the presence of Uncertainty
A guide to deal with uncertainties in software project managementijcsit
Various project management approaches do not consider the impact that uncertainties have on the project.
The identified threats by uncertainty in a projec day-to-day are real and immediate and the expectations in
a project are often high. The project manager faces a dilemma: decisions must be made in the present
about future situations which are inherently uncertain. The use of uncertainty management in project can
be a determining factor for the project success. This paper presents a systematic review about uncertainties
management in software projects and a guide is proposed based on the review. It aims to present the best
practices to manage uncertainties in software projects in a structured way including techniques and
strategies to uncertainties containment.
A REVIEW OF SECURITY INTEGRATION TECHNIQUE IN AGILE SOFTWARE DEVELOPMENTijseajournal
Agile software development has gained a lot of popularity in the software industry due to its iterative and
incremental approach as well as user involvement. Agile has also been criticized due to lack of its ability to
deliver secure software. In this paper, extensive literature has been performed, in order to highlight the
existing security issues in agile software development. Majority of challenges reported in literature,
occurred due to lack of involvement of security expert. Improving security of a software system without
damaging the real essence of Agile can achieved with the continuous involvement of security engineer
throughout development lifecycle with its defined role and responsibilities.
The most common consequences of ineffective or no procedural risk management are the excessive time spent by managers in dealing with unanticipated difficulties.
Acritical process in a software project life-cycle is risk assessment and mitigation. Risks exist in every
software project and recognizing and evaluating risks and uncertainties is a challenging process for
practitioners with little historical data. In our study, by using a survey data, we identify and provide a
relatively wider coverage of risks and ratings of software project. The risk register and evaluations are
useful for practitioner of small organizations at initial phase of risk identification and assessment. There
are 128 risks in this study which are analyzed. Furthermore, the study provides a top risk list according to
this study alongside a highly cross correlatedrisks table. Additionally, previous studies have also provided
top risks lists regarding the corresponding surveys. This study extends previous studies to provide a recent
risk study. Outcomes are also compared to previous works in discussion.
5 Project Risk Identification Tools I Use & How You Can Use Them TooSHAZEBALIKHAN1
Risk is an inherent property of a project. Risk identification is the first step in risk management. The article details the 5 risk identification tools that have helped me in my projects. The explanation shall enable you to use the risk identification techniques for your projects.
PORM: Predictive Optimization of Risk Management to Control Uncertainty Probl...IJECEIAES
Irrespective of different research-based approaches toward risk management, developing a precise model towards risk management is found to be a computationally challenging task owing to critical and vague definition of the origination of the problems. This research work introduces a model called as PROM i.e. Predictive Optimization of Risk Management with the perspective of software engineering. The significant contribution of PORM is to offer a reliable computation of risk analysis by considering generalized practical scenario of software development practices in Information Technology (IT) industry. The proposed PORM system is also designed and equipped with better risk factor assessment with an aid of machine learning approach without having more involvement of iteration. The study outcome shows that PORM system offers computationally cost effective analysis of risk factor as assessed with respect to different quality standards of object oriented system involved in every software projects.
BBA 4226, Risk Management 1
Course Learning Outcomes for Unit II
Upon completion of this unit, students should be able to:
1. Examine the elements of the risk management process.
1.1 Illustrate the use of the risk management process.
2. Analyze the parameters used to categorize risks.
2.1 Categorize risks based on specific parameters.
Course/Unit
Learning Outcomes
Learning Activity
1.1 Unit II Scholarly Activity
2.1
Chapter 3
Unit II Lesson
Unit II Scholarly Activity
Reading Assignment
Chapter 3: Risk
Unit Lesson
We live in interesting and uncertain times, and they are only going to become more complex and risky as the
future unfolds. Individuals and organizations must adapt to an increasingly uncertain environment in order to
identify, mitigate, and survive potential damaging risks. Often, when we think of corporate risks, we think of
natural disasters (e.g., earthquakes and tornados) or even man-made disasters (e.g., fires or attacks such as
the one on September 11, 2001, an oil spill such as the one caused by BP in the Gulf of Mexico, or
information technology (IT) security breaches). Yet, we tend to forget man-made disasters such as the
financial crisis of 2008, which is considered one of the worst global financial crises of all time because of its
ripple effect around the world. We also tend to overlook disruptions in the supply chain of corporations that
could be equally distressing to a company’s survival. Because of the interconnectedness of the world, an
interruption of supplies in one side of the world can have very disruptive and lasting negative effects in
another side.
In today’s economy, many risks have an effect around the world. Thus, it is critical that corporations and
governments implement risk management strategies. First, however, let’s start with defining what risks are all
about.
Risk
Risk is defined as the probability and consequence of not achieving a specific goal. As an example, can the
project be completed within budget? Risk is the probability of loss or the expectation of an unfavorable
outcome as a result of a particular action. Risk is really a measure of future uncertainties or the combination
of an event occurring and the consequences of that event. Newsome (2014) noted that there are different
standards of risk definitions but settled on risks as the “changes, effects, and consequences” of an event’s
potential returns (p. 25). Thus, risk is associated with all future adverse outcomes of an action.
A good description of risks facilitates the understanding, identification, and analysis of risks (Newsome, 2014).
A detailed risk definition and description is the first step to identifying risks. An example of a structured
standardized version of risk description is depicted in Table 3.2 on page 29 of your textbook.
UNIT II STUDY GUIDE
Risk
BBA 4226, Risk Management 2
UNIT x STUDY GUIDE
Title
Components of Risk
Risk ...
Software Defect Trend Forecasting In Open Source Projects using A Univariate ...CSCJournals
Our objective in this research is to provide a framework that will allow project managers, business owners, and developers an effective way to forecast the trend in software defects within a software project in real-time. By providing these stakeholders with a mechanism for forecasting defects, they can then provide the necessary resources at the right time in order to remove these defects before they become too much ultimately leading to software failure. In our research, we will not only show general trends in several open-source projects but also show trends in daily, monthly, and yearly activity. Our research shows that we can use this forecasting method up to 6 months out with only an MSE of 0.019. In this paper, we present our technique and methodologies for developing the inputs for the proposed model and the results of testing on seven open source projects. Further, we discuss the prediction models, the performance, and the implementation using the FBProphet framework and the ARIMA model.
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9
MPM344 Project Risk Management
Medical Needs Risk Management
Connie Farris
11/28/2018
Table of Contents
Project Outline 3
Project Description 3
Method (Agile or Waterfall) 3
Overall Risk Management Strategy 4
Risk Categories 5
Project Risk Identification 7
Project Risks 7
Risk Register 8
Project Risk Analyses 9
Qualitative Risk Analysis 9
Quantitative Risk Analysis 10
Project Risk Response Strategy 12
Risk Response Strategy 12
Project Risk Controlling 13
Plan for Reviewing Risk Responses 13
Identification of New Risks 13
Project Risk Communications Plan 14
Communications Matrix 14
Memo to the Project Sponsor 14
References 15
Project OutlineProject Description
The quality gauges of therapeutic devices contain numerous viewpoints, for example, plan control, Risk management, vendor management etc. (Singh, 2017) In this paper we are going to discuss the importance of Risk management for medical devices and steps to perform during the product development stage. Risk management is a critical part of the medical device product development lifecycle. (Singh, 2017) It enables medical device engineers to guarantee that the item is dependable, functions obviously and will be no damage to the patients, administrators or the earth. In other words, the main purpose of the Risk management cycle is to lessen or curb the odds of disappointment in the item.
Medical Needs is a company that designs and manufactures medical devices and will be opening a new company and would like for us to do the risk management involved with the materials used for these devices and the design to ensure the devices are compliant to all regulations during production. There are many areas we will cover during this project to ensure that a quality product is available for the public consumer. We are looking at completing this project in less than six months before the manufacturing process commences. We will look at everything from vendor, mechanical and that everything planned before the company is open will be as risk free as possible.
Method (Agile or Waterfall)
. We will be using an Agile Method to conduct this risk assessment. The agile methodology with more checkpoints, gives you a chance to refine what you are working for the client, and what they are at last given. (Tan, 2016) Agile gives the chance to take what you made, offer it to a customer, and bring out criticism. Considering the input, you can decide if you are going the correct way. (Tan, 2016) If changes are required, you give yourself the chance to adjust and alter. When creating medicinal devices, numerous sources of info that must be contemplated. Distinguish the requirements of the end client however comprehend the potential dangers that the item may present to end clients and guarantee that your item is adequately protected and powerful by structuring risk adjustment into your medical device.
When using the agile process, you must understan.
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AN EVALUATION STUDY OF GENERAL SOFTWARE PROJECT RISK BASEDON SOFTWARE PRACTITIONERS EXPERIENCES
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
DOI:10.5121/ijcsit.2016.8601 1
AN EVALUATION STUDY OF GENERAL SOFTWARE
PROJECT RISK BASEDON SOFTWARE
PRACTITIONERS EXPERIENCES
Sahand Vahidnia 1
andÖmerÖzgürTanrıöver2
and I. N. Askerzade3
1 23
Ankara University / Computer Engineering Department, Ankara University, Gölbaşı
50.yılYerleşkesiBahçelievlerMh., 06830 Ankara 06100, Turkey
ABSTRACT
Acritical process in a software project life-cycle is risk assessment and mitigation. Risks exist in every
software project and recognizing and evaluatingrisks and uncertainties is a challenging process for
practitioners with little historical data. In our study, by using a survey data, we identify and provide a
relatively wider coverage of risks and ratings of software project. The risk register and evaluations are
useful for practitioner of small organizations at initial phase of risk identification and assessment. There
are 128 risks in this study which are analyzed. Furthermore, the study provides a top risk list according to
this study alongside a highly cross correlatedrisks table. Additionally, previous studies have also provided
top risks lists regarding the corresponding surveys. This study extends previous studies to provide a recent
risk study. Outcomes are also compared to previous works in discussion.
KEYWORDS
Software Project Risk Assessment, Risk Matrix, Software Engineering
1. INTRODUCTION
As indicated byresearches [1] [2][3], the worldwide software business sectoris evaluated to have
an estimationof US$330 billion in 2014. Further, the Chaos Report from the Standish Group
reported that the success rate of worldwide (fundamentally U.S. and Europe) software projects in
2015 is only 29% [4].In all projects, risks should be identified, assessed by its probability of
occurrence and impact, and a contingency plan should be developed for remediating the problem
actually occur [5]. The procedure of risk identification can be troublesome and misleading in
some cases. Moreover, knowing whether a risk is important or not is also is another issue which
requires more research on cases.
This research`s aim is to assess the initial rating of risks for small to medium size software
projects. One can see that in recent years, there are not as much studies to determine general risk
factors and their possible effects on small to medium size software projects.In order to identify
general risks and deploy a survey and obtain rating from practitioners, previous researches in risk
assessment and managementfield had to be reviewed.Some of thesestudies are briefly given
below.
Zardari[6] in his research about managing software risks, attempts to provide practitioners with
necessary basic information regarding the management of risks in software. The research defines
the term of proactive and reactive management, a list of top risks, probability and impact and their
levels alongside other terms.
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
2
Keshlaf[7] specifically focuses on web and distributed software development project risks in this
study, and provides a summarized risk set from other works. The research studies risks and
challenges in the field and alsostudies and analysis existing methods and frameworks of software
risk management including SD-RM-Concept and EBIOS Methodology.
One of the most influential works in the field is Arnuphaptrairong`s research [8] regarding the
risk factor lists from other works and top risks of each list. The research compares and analyzes
top risk lists from Bohem and other studies. As for our study, we had a comparison of results
amongst the top risks and our research results. Further assessment is conducted in discussion
section.
Song’s research [9] is another research which attempts to show the importance of risk
management in software engineering projects. The research utilizes an information entropy
approach to analyze risks in three dimensions of loss, uncertainty and probability. The research
also provides awide risk list which was directly used in this research.
In other studies including [10] risk avoidance models are provided and some researches like [11]
have gathered and explained and analyzed risks. Some studies have taken different approaches
like analyzing multi characteristics of risks like multi dimension and multi situations and etc. like
Wang’s work [12].
One of the problems with previous studies is that each adopt a sets of risk factors which are
sometimes completely disjoint or overlapping. Also most of the studies consider a limited set of
risks, therefore we aim to put a wide coverage list of software related risks. We ask software
practitioners for their experience over of these risks and study the result to generate a super set of
risks and ratings for reference.
In our study, by conducting a survey, wemean to distinguish and provide a wider coverage of
initial rating to software generalproject risks and their relations especially for practitioner of small
organizations. A top risk list, highly cross correlated risksare provided for use by software teams
or practitioners with relatively little experience and previous historical data. The identified wider
coverage of general risks and ratings may be used in further studies.Furthermore, we discuss the
outcomes and compare our results to other results from a survey of previous works[8].
2. GENERAL SOFTWARE PROJECT RISK FACTOR COLLECTION
Although, in previous studies diverse risk factors, categories and analysis tools have been
utilized, in this study we considered studies using keywords of “risk” and “software”. Some of the
studies have a wider coverage over risks. Among cited researches, throughout recent years,
especially from 2006 to 2016, there have been few researches about software development risk
factors. However, since 2006, many aspects of software engineering have evolved.
In various studies, risk factors included are constrained to particular aspect or phase of software
development. But it is desired to have a near complete set of general risk factors and their effects.
So as the initial phase of this study, we accumulated risk factors and categories from related
researches [6], [7],[9],[10], [11], [13], [14], [15]. By gathering risk factors, we came up with a
superset of risks which has a greater coverage over risks of software projects and development
phases. But the risk set, in addition to have too many risks to consider and assess, includes
numeroussimilar and covering risks. So an action is taken to minimize the risk set.
In order to discover similar risk factors,at firsta keyword search was performed. This search
prompted a keyword sorting, and also finding exceedingly comparative risks which are dispensed
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
3
based on their definitions. Additionally, some available risks were unpredictable and were
outcome of project rather than a possible risk to endanger project. These risks are also eliminated
from the list. Next table is presenting few examples of eliminated risks.
Table 1- Risk elimination example
Development Over-
Schedule
Team Disaster Recovery
We consider over
schedule development
as a failure type, rather
than a risk. Over
scheduling is result of
many risks happening
during project design
and development
phases.
Team, itself is too
general to be
considered as a risk
factors. There are
other risk factors in
this study which will
cover team.
The risk of bad
disaster recovery is
important, but we
decided to have the
risk of Disaster /
Catastrophe to cover
this risk and prevent
overlapping.
The corresponding table of risk-factors and gathered data is shown in Appendix. in its final state.
This phase led us to a set of risk factors which itself can be helpful for development teams as
initial set for risk register formulation.
To make it possible for survey participants to judge and evaluate the risk factors, we reworded the
risk factors in a way that these factors are consistent, understandable and rated more naturally.
For instance, “Project Management Approach / method” has a high risk potential and possibly
will receive a 2 or 3 risk rating from experts (moderate to high). But for a developer it's difficult
to evaluate a project considering this factor. In order to address this issue, we added "wrong
method" to this factor in parenthesis. In short we make factors easier to be rated, by adding
adjectives or other words to factor phrases. Other examples:
Definition of the Program (ambiguity)
• Mix Of Team Skills (Bad Or Low)
• Technical Feedback (lack of)
Furthermore, all risk factors are stated to with negative statements. As an example "programming
language experience" factor is actually a positive and good factor in project. So listing this in risk
factors may confuse experts and developers. So, we added "lack of" in parenthesis to risk factor.
Now probability of confusion for participants is less likely to occur.
Risk can be divided into three items of probability, impact and risk [16]. Risk is product of
probability and impact items [17]. Scale definitions of probability and impact levels are reused
from[6].
Risk Score = Probability * Impact
Probability levels definitions:
1. High / Very Likely: High chance of this risk occurring, thus becoming a problem {70% <
x}.
2. Medium / Probable: Risk like this may turn into a problem once in a while {30% < x <
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
4
70%}.
3. Low / Improbable: Not much chance this will become a problem {0% < x < 30%}.
Impact levels definitions:
1. High / Catastrophic: Loss of system; unrecoverable failure of project; major problem;
schedule slip causing launch date to be missed; cost overrun greater than 50% of budget.
2. Medium / Critical: Considerable problem with project with recoverable operational
capacity; cost overrun exceeding 10% (but less than 50% of planned cost).
3. Low / Marginal: Minor problem project; recoverable loss of operational capacity; internal
schedule slip that does not impact launch date cost overrun less than 10% of planned cost
or timeframe.
Further to make risk factors more understandable and precise, we gave brief information about
some risk factors from [18].
3. DATA COLLECTION AND ANALYSIS
3.1. EXPERT DATA COLLECTION
An essential phase in this research is acquisition of expert data.An onlıne survey data is used for
this purpose which was conducted during the research. The purpose of this survey was to collect
the risk-factor probabilities and potential impact based on real projects of developers which are
happened in the past. The survey comprises 3 parts ofI, II and III. In part I we asked developers to
give us a brief information about an unsuccessful/challenged/failed project which they
participated in. There are 12 important questions in part I, including questions about experience of
the expert and questions about project size and type. These part also comprises questions about
programming paradigm, design pattern and methodology of project.Part II of the survey contains
10 questions about project’s fate. Questions in this part are generally excerptedfrom references[6],
[7], [10], [13], [14] and some are adjusted to prevent the possible confusion in answering. This
section provides the failure data of projects which will be evaluated on future studies.Part III
consists of the risk factor ratings Part III consists of the risk factor ratings as described earlier.
The analysis of part II is elaborated in a yet to be published study. In this one, we analyze
information obtained in part III. The structure of the survey can be accessed at
http://survey.labs.tips/result.php .
At this period of theresearch, assembled data is pre-processed. To do as such,all faulty data are
eliminated. There were a total of 86 participants in this survey, yet some answers were inadequate
and inappropriate which were removed. Also to prevent cheating (random answers) 5 check
questions were among part III questions. Toward the end, there were only 40 dependable answer
which are considered in final dataset.
Subsequent to making a rectified dataset, dataset is processed. The mean of all entered data for
each risk is used to order risks by importance according to current survey data, considering the
risk factors shown in appendix. The fundamental objective of this research is aggregation and
usage of these risk-factors in a more applicable manner for real world software projects. So we
chose to implement risk matrix at this stage.
3.2. GENERAL RISK MATRIX
Risk matrices are most likelyone of widespread tools for risk evaluation. Risk matrix is much
easier to comprehend than raw data and other methods.They are for the most partused to
determine the extent of a risk and whether or not the risk is adequately controlled. Probability and
Impact are two dimensions of a risk matrix. The combination of these dimensions creates a risk
matrix which makes the assessment easier. Risk matrix dimensions or axes are divided into 3
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
5
level each, which creates a 9 cell qualitative matrix [19]. This matrix has 3 part:
• High / Major Concern (red): Risk is high in these sections and an action should be taken.
• Medium / Concern (yellow): Risk is moderate in these sections and there is a chance that
risks in these areas may affect project.
• Low / No Concern (green): Risk in these sections are low and acceptable and can be ignored.
Probability
Low Medium High
High
Medium
Low
Impact
Figure 1 - Risk Matrix
There are likewise different arrangements of risk matrices like 5x5, 7x5 and 7x4 risk matrices
which are not adapted in this study due to simplicity of 3x3 matrices [20].
A risk matrix is acquired utilizing averages of 40 data points for every risk factor according to
Figure 1. According to this general risk matrix, only risk factors listed below are categorized as
relatively important.
Table 2 – Top risk factor list according to risk matrix
# Risk Factor # Risk Factor
1 Lack Of Development Technology
Experience Of Project Team
11 Short Term Solution (lack Of Long Term
Solution)
2 Project- Resource Conflict 12 Lack Of Testability
3 Large Project Size 13 Implementation Difficulty
4 Bad Project Management Approach /
Method
14 Late Identification Of Defects
5 Lack Of Project Management
Experience
15 Bad Defect Tracking
6 Poor Project Planning 16 Lack Of Experience With Software
Engineering Process
7 Expansion Of Software Requirements 17 Lack Of Training Of Team
8 Low Knowledge And Understanding Of
Clients Regarding The Requirements
18 Dependency On A Few Key People
9 Bad Development Schedule 19 Need To Integrate With Other Systems
10 Lack Of Analyst Capability 20 Lack Of Platform Experience
Risk matrix take into account both probability and impact perspective. Analysis and comparisons
regarding the table 2 is discussed at discussion section.
3.3. FREQUENCY AND CORRELATIONS
For the 128 risk factors and 40 data points, a statistical preprocessing is conducted. Firstly,
descriptive statistics and frequencies has been obtained for results to achieve a better
understanding over data points.Calculating average risk scores for available data points is
required in this step. In order to perform this action, two methods could be implemented. The first
method is simply calculating products of acquired average impact and probability values. This
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
6
approach yields in loss of validity because of ordinal scale definition. Hence, to produce risk
score from multiplication of each probability and impact data point, and then calculating the
average and frequencies.
Figure 2 – Frequency of average of every risk
Table 3 – Frequency data
Probability Impact Risk Val.
Risk No. 128 128 128
Std. Deviation 0.19815 0.22983 0.40558
Std. Error of
Mean
0.01745 0.02024 0.03571
Range 1.00 1.30 2.03
Minimum 0.10 0.35 0.43
Maximum 1.40 1.65 2.45
Mean 0.8587 0.8965 1.244
For average of the 40 data points of every one of the128 risk factors, rest of calculated data are
appearedin the table above. As exhibited in figures above, most risks have an average of 1.2
which is the reasonaverage data is not a decent information to rely on. So we decided to analyze
data using frequencies. When we investigate frequencies of risks, rather thancomparing their
averages, we compare the number of people who marked a risk as important. By important we
mean a risk score of 6 or 9. To do so, all scores except 6 and 9 among all data are excluded from
evaluation to compare risks with dangerous behavior only. Table 5 also demonstrates the
generated top risk list.
Another objective of this study was to determine fundamental risk factors on software project
failures. In order to see if one consider only subset of risk factors which have high effect on
project failures, a dimensionality reduction technique was considered.
To minimize the risk factor dimension correlation coefficients of 128 risk factors are extracted.
Since the risk data is distributed normally, Pearson’s correlation coefficient is used as a statistical
measure of the strength of a linear relationship between paired data. In a sample it is denoted by r
and is by design constrained as follow:
-1≤ r ≤+1
According to Weinberg [21], Pearson correlation coefficients of r = ± 0.5 are considered strong
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
7
and correlation coefficients close to ±1 are the strongest. Also 0 means there are no correlation
among variables.Evans [22] recommends a correlation coefficient of ±0.6 to ±0.79 as a strong and
±0.8 to ±1as very strong correlation coefficient. In our calculation, correlation coefficients which
are among ±0.8 and ±1 are considered as very strong. Next table shows a list of very strongly
cross correlated risk factors i.e±0.8. Strong cross correlation among risk pairs creates a duplicate
variable effect which makes the data unhealthy.
Statistical calculations in this research was performed using SPSS tool [23], except for the
correlation values which are generated using Matlab’s Pearson’s correlation function.
Table 4 - Very strongly correlated risks
There are a total of 33 highly correlated risks separated from entire risk factor list. Statistically
these 33 risks, represent rehashed data among 128 risk. Due to limits in gathering data-points and
low confidence in data analysis, it is not encouraged to rely on this correlation data for
eliminating all 33 risk factors.
Furthermore, we investigated fewrisks in correlation table and discovered that some correlations’
logically make sense and some don’t. For instance, risks of “Poor Project Planning” and
“Dependency On a Few Key People” are highly correlated with a score of 0.80 and there is a real
logical relation visible to engineers. On the other hand, there are no direct logical relation among
some risks like “Short Term Solution (lack of Long Term Solution)” and “Staff Turnover” with
correlation coefficient of 0.83. The issue needs to be addressed in future works after acquiring
more project and expert data.
4. DISCUSSION
As mentioned previously, [8] has conducted a literature review to compare risk factors in other
studies. In this study we also provide two tables of top risks with different methods. In this
section combination of top risks provided in [8] is compared to our results. Due to differences in
definition and implication of risks introduced and compared in other studies, we consider closest
risk and overlapping risks in definition and mark them with a star sign (*). It is worth mentioning
that prior to elimination of overlapping risks in section II., many overlapping risks were in our
superset of risks. This partially justifies our use overlapping and similar risks in table 5.
Very Strongly Correlated Some Highly Correlated (among ±0.7 and ±0.8 )
Risk
ID
Risk
ID
Correlation
Coefficient
Risk
ID
Risk
ID
Correlation
Coefficient
Risk
ID
Risk
ID
Correlation
Coefficient
18 125 0.83277 3 126 0.73161 65 120 0.76273
55 111 0.84347 9 54 0.70722 70 74 0.74817
37 51 0.82162 26 11 0.71208 71 93 0.77567
21 30 0.80628 27 48 0.71146 97 115 0.75964
87 93 0.85617 28 95 0.72975 84 98 0.70398
24 50 0.85583 29 31 0.77262 85 120 0.74108
25 74 0.84403 34 109 0.70646 105 124 0.70634
90 109 0.80085 47 126 0.71017 122 115 0.76228
38 126 0.84679 56 74 0.78317 43 104 0.71679
101 111 0.80295 57 88 0.74041 44 95 0.71029
39 49 0.82401 58 117 0.70219 36 126 0.74008
8. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
8
Table 5 – Comparison table of current and previous researches
#
TOP 20 RISKS (WITH SUM OF OVER 30) SUM A B C D E F G H I
1. Development Technology Not Match To
Project
30 * +
2. Lack Of Analyst Capability 30 * + + + +
3. Short Term Solution 30 *
4. Lack Of Organizational Maturity 30 *
5. Large Project Size 33 *
6. Lack Of Training Of Team 33 * * * * *
7. Lack Of Experience With Software
Engineering Process
33 *
8. High System Dependencies 33
9. Problem With Hardware Platform 33
10. Lack Of Project Management Experience 36 * * * * *
11. Incorrect Project Size Estimation 36 * *
12. Dependency On A Few Key People 36
13. Lack Of Or Bad Change Control For Work
Products
36 * *
14. Large Database Size 39 *
15. Bad Development Schedule 39 * *
16. Lack Of Platform Experience 39 * * *
17. Expansion Of Software Requirements 42 * * * *
18. Bad Project Management Approach / Method 42 * * * *
19. Lack Of Use Of Modern Programming
Practices
42 *
20. Lack Of Reusable Components 42
A. Is available at table 2
B. Is available at top 10 list of Bohem[8]
C. Is available at top 10 list of Schmidt et al. (USA) [8]
D. Is available at top 10 list of Schmidt et al. (HONG KONG) [8]
E. Is available at top 10 list of Schmidt et al. (FINLAND) [8]
F. Is available at top 10 list of Addison and Vallabh[8]
G. Is available at top 10 list of Addison [8]
H. Is available at top 10 list of Han and Huang [8]
I. Is available at top 10 list of Pare et al. [8]
+ means the risk can also cover named risk, or the risk can be concluded from named risk, but
not precisely the same.
* means the risk is very similar or exactly the same
10 risk factors out of 20 risk factors in table 5 are also present in table 2. Table 2 was generated
using risk matrix, but due to general project risks, unless the data is not filtered into sub
categories, arithmetic meanon data will be misleading. As a result, the data presented in table 5 is
much healthier and reliable.
As presented in table 5, many of the important risks from other works, are not recognized as
important in our survey data. Also the reason might not be clear in some cases, but in many cases
it can be explained. We suggest thatthese differences in the results can be related to time
dimensions of conducted researches regarding the risks. Also [8] is suggesting a similar
explanation for cases like Bohem. These researches are conducted between the years of 1991 and
2008 which makes our hypothesis stronger. In past two decades, technology advancements
havehad great contributions to advancements and changes in software industry and software risk
9. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
9
factors and their effects. As an example, “Developing wrong user interface” might not be that
much of a concern for software developers nowadays.
Another finding in this comparison is that, unsurprisingly our survey participants did not
recognize user related risks as important. This might indicate the lack of management experience,
dealing with clients and users for the participants, as our survey participants are all composed of
young computer engineers, students and software developers.
Another proof for this finding is that the risk of “Lack of Reusable Components” has not been
repeated or slightly mentioned in prior researches, whereas in this survey, this risk is on top of the
list. This indicates that participants were too much concerned about programming than managing
and user dimension.
5. CONCLUSION
In this study we introduce a reference software risk set for software development. We also
provide a risk matrix for software projects which can be used to assess probable and common
risks in developing software solutions.
Also in this study a dimensionality reduction was performed using statistical correlations among
risk factors. So software practitioners can see correlations of risks as Table 4 and predict which
risk may also affect them according to previous risks.
Throughout this study analysis on survey data was conducted to highlight important risks in
software project development, using risk matrix and a custom method of priority table. For better
precision, further studies and more data-points are required, which will be available in next
research. Having more data-points in addition to having more reliable risk matrix and risk priority
tables, will also make it possible to perform other analysis including regression analysis.
ACKNOWLEDGEMENTS
The authors would like to thank all participants in the survey for their great contributions to this
research.
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APPENDIX
Unsorted Risk Statement
Medium/CriticalImpact
Medium/Probable
probability
Reference
Unsorted Risk
Statement
Medium/CriticalImpact
Medium/Probable
probability
Reference
1. Large Database Size * * [14] 65. Developing Wrong
Software Functions
[6][7]
2. Main Storage Constraint [14] 66. Developing Wrong
User Interface
[6][7]
3. High Platform Volatility [14] 67. Gold Plating [6][7]
4. Bad Development Schedule [14] 68. Shortfalls In
Outsourced
Components
[6][7]
5. Lack Of Analyst Capability * * [14] 69. Shortfalls In Externally
Performed Tasks
[6][7]
11. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
11
6. Lack Of Platform Experience * * [14] 70. Real-time Performance
Shortfalls
[6][7]
7. Lack Of Use Of Modern
Programming Practices
* [14] 71. Bad Traceability [7]
8. Low Usage Of Software
Support Tools
* [14] 72. Insufficient
Verification And
Validation
[7]
9. Lack Of Software Developer
Competence
[24] 73. Customer Unsatisfied
At Project Delivery
[7]
10. Project NOT Fit To Customer
Organization
[13] 74. Risk Reducing
Technique Producing
New Risk
[7]
11. Lack Of Customer Perception [13] 75. Catastrophe / Disaster [7]
12. Project- Resource Conflict * * [13] 76. Incorrect Project Size
Estimation
* [11]
13. Customer Conflict [13] 77. Project Funding
Uncertainty
* * [11]
14. Lack Of Leadership [13] 78. Rapid Change Of Job [11]
15. Definition Of The Program
(ambiguity)
[13] 79. Change In Working
Circumstances By
Management
[11]
16. High Political Influences [13] 80. Hardware Default
Changes
[11]
17. Inconvenient Date [13] 81. Requirement
Postponement
* [11]
18. Short Term Solution (lack Of
Long Term Solution)
* * [13] 82. Presence Of High
Bugs/errors Count
[11]
19. Lack Of Organization
Stability
[13] 83. Technology Change [11]
20. Lack Of Organization Roles
And Responsibilities
[13] 84. Underestimation Of
Data Increase Due To
Software Success
[11]
21. Lack Of Policies And
Standards
[13] 85. Lack Of Design And
Development Tool
Independence
[11]
22. Lack Of Management Support
And Involvement
[13] 86. Risk Of Intruders
(hackers, Viruses,
Trojan Horse)
[11]
23. Lack Of Project Objectives * [13] 87. Misleading Estimation
About Skills Of
Workers
* [11]
24. Lack Of User Involvement [13] 88. Lack Of Technical
Feedback
* * [11]
25. Lack Of User Acceptance [13] 89. Compromise On Profit
To Save Name
[11]
26. High User Training Needs [13] 90. Risk Of Economy
Distortion
* [11]
27. Large Project Size * * [13] 91. Expansion Of Software
Requirements
* * [15]
28. Hardware Constraints [13] 92. Inaccurate Estimation
Of Software Effort
[15]
29. Lack Of Reusable
Components
[13] 93. Low Knowledge And
Understanding Of
Clients Regarding The
Requirements
* * [9]
30. Lack Of Cost Controls * * [13] 94. Incorrect Requirements [9]
31. Lack Of Delivery
Commitment
[13] 95. Lack Of Frozen
Requirements
[9]
32. Lack Of Requirements
Stability
[13] 96. Undefined Project
Success Criteria
[9]
33. Requirements NOT Complete
And Clear
[13] 97. Conflicting System
Requirements
[9]
34. Lack Of Testability * [13] 98. Conflict Between User [9]
12. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
12
Departments
35. Implementation Difficulty [13] 99. Low Number Of Users
In And Outside The
Organization
[9]
36. High System Dependencies * [13] 100. Instability Of The
Client's Business
Environment
[9]
37. Lack Of Response Or Other
Performance Factors
[13] 101. Dependency On A Few
Key People
[9]
38. High Customer Service
Impact
[13] 102. Lack Of Staff
Commitment, Low
Morale
* [9]
39. Data Migration Required [13] 103. Instability And Lack
Of Continuity In
Project Staffing
[9]
40. Lack Of Pilot Approach [13] 104. High Number Of
People On Team
-
41. Lack Of Alternatives Analysis [13] 105. Low Team Diversity * [9]
42. Lack Of Quality Assurance
Approach
[13] 106. Lack Of
Organizational
Maturity
[9]
43. 15Lack Of Development
Documentation
[13] 107. Lack of Project leader's
experience
* [9]
44. No Use Of Defined
Engineering Process
[13] 108. High Extent Of
Changes In The Project
* * [9]
45. Late Identification Of Defects [13] 109. Excessive Schedule
Pressure
[9]
46. Bad Defect Tracking [13] 110. Inadequate Cost
Estimating
* [9]
47. Lack Of Or Bad Change
Control For Work Products
[13] 111. Poor Project Planning * * [9]
48. Problem With Physical
Facilities
[13] 112. Ineffective
Communication
[9]
49. Problem With Hardware
Platform
* [13] 113. Improper Definition Of
Roles And
Responsibilities
[9]
50. Tools Unavailability [13] 114. Need To Integrate
With Other Systems
[9]
51. Bad Project Management
Approach / Method
* * [13] 115. Inadequate
Configuration Control
[9]
52. Lack Of Project Management
Experience
* * [13] 116. Low Quality Of
Software And
Hardware Supplier
Support
[9]
53. Bad Project Management
Attitude
[13] 117. Excessive Reliance On
A Single Development
Environment
[9]
54. Lack Of Project Management
Authority
[13] 118. High Extent Of
Linkage To Other
Organizations
-
55. Team Member Unavailability * * [13] 119. Resource Insufficiency [9]
56. Bad Or Low Mix Of Team
Skills
[13] 120. Intensity Of Conflicts [9]
57. Lack Of Experience With
Software Engineering Process
* * [13] 121. Lack Of Control Over
Consultants, Vendors
,sub-contractors
[9]
58. Lack Of Training Of Team * * [13] 122. Massive User Stress [10]
59. Lack Of Expertise With
Application Area (Domain)
* [13] 123. Lack Of Project
Delivery Milestones
[10]
60. Development Technology
NOT Match To Project
[13] 124. Over-optimistic
Technology Perceives
[10]
61. Lack Of Development
Technology Experience Of
* * [13] 125. Staff Turnover [10]
13. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 6, December 2016
13
Project Team
62. Immaturity Of Development
Technology
[13] 126. Backup Issues [10]
63. High Design Complexity * * [13] 127. Bad Preservation Of
Intellectuals
[10]
64. Lack Of Support Personnel [13] 128. Inability To Secure
Confidential Customer
Data
-