Automated Risk Identification of CMMI Project Planning Using Ontology

Rojrattanakorn, Chanapan; Vatanawood, Wiwat

doi:10.1109/acit-csii-bcd.2017.26

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The evaluation system of CMMI also divides the process of software development into 22 process domains. It defines the CMMI management level of the company by evaluating the completion quality of each [47], [48].…”

Section: B the Theoretical Basis Of Software Project Management Domainmentioning

confidence: 99%

A Case Study for Software Quality Evaluation Based on SCT Model With BP Neural Network

et al. 2020

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With the increasing for function, scale, hierarchy and complexity of software project, the software life cycle and development stage show a trend of cross-cutting and fuzzy boundary. The non-technical factors, such as poor management and control during the implementation of software projects, are the major reason for causing the low success rate of software projects recently. Therefore, the software quality evaluation under complex environment should take the cross-influence between different stages of software life cycle and different quality evaluation standards into consideration. Our research is to construct a new software quality evaluation model by using the influence relationship and the influence intensity index between project management domain and project quality evaluation criteria including scope, cost, and time. First, we came up with the definition of software project management domain in the process of software project development and management. Second, we proposed a mathematical method for extracting the direct or indirect influence relation between them, and give a definition for the quantitative evaluation index and its calculation formula. At last we proposed to construct a neural network training model which includes evaluation model logic relationships and software quality quantitative evaluation index. Through study and training by simulated software project management data, we can discover some key data, such as normal threshold range of influence, factor weights, etc. Therefore, a complete evaluation system is built, and the scientific nature and accuracy of the proposal evaluation system will be improved. INDEX TERMS Software quality evaluation model, back propagation neural network, project management domain, project sub-management domain, SCT, CMMI.

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Section: B the Theoretical Basis Of Software Project Management Domainmentioning

confidence: 99%

A Case Study for Software Quality Evaluation Based on SCT Model With BP Neural Network

et al. 2020

View full text Add to dashboard Cite

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“…Educational level, to assist in training on software risk analysis [111].  Project level, to help integrate RM into IT project governance practices [135]; to assist with RM in distributed software projects and to qualify the operational aspects of their realisation [91]; to support RM and assist in risk prevention through its integration into an ERP system [36]; to support automated risk identification in software projects [113]; to support RM learning in organisations and serve as a knowledge base for decision-making about risks of software processes and projects [27] and to undertake risk classification based on the software life cycle, through the use of an HBRS and assist in qualitative risk analysis using analytical hierarchical process (AHP) [24].…”

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confidence: 99%

A Common Terminology for Software Risk Management

Masso

García

Pardo

et al. 2022

ACM Trans. Softw. Eng. Methodol.

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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 where Risk 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 called SRMO (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.

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Automated Risk Identification of CMMI Project Planning Using Ontology

Cited by 2 publications

References 7 publications

A Case Study for Software Quality Evaluation Based on SCT Model With BP Neural Network

A Case Study for Software Quality Evaluation Based on SCT Model With BP Neural Network

A Common Terminology for Software Risk Management

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