Risk Management Method Using Data from EVM in Software Development Projects

2008 ◽  
Author(s):  
Akihiro Hayashi ◽  
Nobuhiro Kataoka
Keyword(s):  
Risk Management ◽  
Software Development ◽  
Development Projects ◽  
Management Method ◽  
Using Data

Risk Management and Assessment in Software Development Projects

2021 ◽  
Vol 43 (4) ◽  
pp. 113-124
Author(s):  
D.V. Saveliev ◽  
Keyword(s):  
Risk Management ◽  
Software Development ◽  
Risk Index ◽  
Development Projects ◽  
Risk Status ◽  
Threat Model ◽  
The World ◽  
Web Systems

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

Enhancing DotProject to Support Risk Management Aligned with PMBOK in the Context of SMEs

2015 ◽  
Vol 6 (2) ◽  
pp. 40-60 ◽  
Author(s):  
Rafael Queiroz Gonçalves ◽  
Elisa de Freitas Kühlkamp ◽  
Christiane Gresse von Wangenheim
Keyword(s):  
Risk Management ◽  
Project Management ◽  
Best Practices ◽  
Software Development ◽  
Small And Medium Enterprises ◽  
Management Tool ◽  
Development Projects ◽  
Management Tools ◽  
Management Processes ◽  
Medium Enterprises

Many problems in software development projects are due to risks and could be avoided or minimized if identified and treated pro-actively. In this context, software tools to support risk management could be very helpful. However, it is difficult to find a project management tool, accessible to Small and Medium Enterprises (SMEs) that provides adequate support to risk management in conformance with best practices such as the PMBOK. Therefore, this paper has the objective to review support provided by popular project management tools with respect to risk management and to present enhancements made to the open-source tool – dotProject – in order to systematically support risk management aligned with the PMBOK. An initial evaluation identified benefits in the implementation of risk management processes in software SMEs, and, thus, contributing to their projects' success.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Mustafa Batar ◽  
Kökten Ulaş Birant ◽  
Ali Hakan Işık
Keyword(s):  
Risk Assessment ◽  
Software Development ◽  
Fuzzy Inference ◽  
Development Projects ◽  
Software Projects ◽  
Inference System ◽  
Rule Based ◽  
Management Method ◽  
Software Risk ◽  
Risk Assessment And Management

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.

A Framework for Risk Management in AI System Development Projects

2019 ◽  
pp. 1-20
Author(s):  
Kitti Photikitti ◽  
Kitikorn Dowpiset ◽  
Jirapun Daengdej
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Project Management ◽  
Software Development ◽  
System Development ◽  
Systems Development ◽  
Development Projects ◽  
Software Project ◽  
Software Projects ◽  
Very High

It has been well-known that the chance of successfully delivering a software project within an allocated time and budget is very low. Most of the researches in this area have concluded that “user's requirements” of the systems is one of the most difficult risks to deal with in this case. Interestingly, until today, regardless of amount of effort put into this area, the possibility of project failure is still very high. The issue with requirement can be significantly increased when developing an artificial intelligence (AI) system, where one would like the systems to autonomously behave. This is because we are not only dealing with user's requirements, but we must also be able to deal with “system's behavior” that, in many cases, do not even exist during software development. This chapter discusses a preliminary work on a framework for risk management for AI systems development projects. The goal of this framework is to help project management in minimizing risk that can lead AI software projects to fail due to the inability to finish the projects on time and within budget.

Agent-Based Approach for Monitoring Risks in Software Development Projects

2021 ◽  
pp. 91-104
Author(s):  
Jirapun Daengdej
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Software Development ◽  
Development Projects ◽  
New Approach ◽  
Agent Based ◽  
Risk Monitoring ◽  
Proposed Model ◽  
High Degree ◽  
Very High

According to various surveys conducted, regardless of how many studies in software development projects have been done, the chance that software development projects may fail remains very high. A relatively new approach to the problem of failure is using the concept of artificial intelligence (AI) to help automate a certain part(s) of the projects in order to minimize the issue. Unfortunately, most of the works proposed to date use AI as a standalone system, which leads to limiting the degree of automation that the overall system can benefit from the technology. This chapter discusses a preliminary work on a novel risk monitoring, which utilizes a number of agent-based systems that cooperate with each other in minimizing risks for the projects. The proposed model not only leads to a high degree of automation in risk management, but this extensible model also allows additional tasks in risk monitoring to be easily added and automated if required.

Developing a risk assessment model for banking software development projects based on rough-grey set theory

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tooraj Karimi ◽  
Yalda Yahyazade
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Expert System ◽  
Software Development ◽  
Set Theory ◽  
Rough Set ◽  
Risk Level ◽  
Development Projects ◽  
Content Type ◽  
Grey Clustering

PurposeRisk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology in all fields and the high failure rate of software development projects, it is essential to predict the risk level of each project effectively before starting. Therefore, the main purpose of this paper is proposing an expert system to infer about the risk of new banking software development project.Design/methodology/approachIn this research, the risk of software developing projects is considered from four dimensions including risk of cost deviation, time deviation, quality deviation and scope deviation, which is examined by rough set theory (RST). The most important variables affecting the cost, time, quality and scope of projects are identified as condition attributes and four initial decision systems are constructed. Grey system theory is used to cluster the condition attributes and after data discretizing, eight rule models for each dimension of risk as a decision attribute are extracted using RST. The most validated model for each decision attribute is selected as an inference engine of the expert system, and finally a simple user interface is designed in order to predict the risk level of any new project by inserting the data of project attributesFindingsIn this paper, a high accuracy expert system is designed based on the combination of the grey clustering method and rough set modeling to predict the risks of each project before starting. Cross-validation of different rule models shows that the best model for determining cost deviation is Manual/Jonson/ORR model, and the most validated models for predicting the risk of time, quality and scope of projects are Entropy/Genetic/ORR, Manual/Genetic/FOR and Entropy/Genetic/ORR models; all of which are more than 90% accurateResearch limitations/implicationsIt is essential to gather data of previous cases to design a validated expert system. Since data documentation in the field of software development projects is not complete enough, grey set theory (GST) and RST are combined to improve the validity of the rule model. The proposed expert system can be used for risk assessment of new banking software projectsOriginality/valueThe risk assessment of software developing projects based on RST is a new approach in the field of risk management. Furthermore, using the grey clustering for combining the condition attributes is a novel solution for improving the accuracy of the rule models.

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