Implementing risk management on software intensive projects

Conrow, Edmund H.; Shishido, P.S.

doi:10.1109/52.589242

Cited by 53 publications

(27 citation statements)

References 4 publications

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“…In this study, we have investigated various works (Conrow and Shishido, 1997;Fairley and Rook, 1997;Karolak, 1996;Sisti and Joseph, 1994) regarding risk management and the experience of the SSBC. Based on the results of this investigation, we have summarized all key risk factors and classified them into the following five viewpoints:…”

Section: Five Viewpointsmentioning

confidence: 99%

“…In developing our questionnaire, we made use of lessons learned from past project experience and related works in the literature (Conrow and Shishido, 1997;Fairley and Rook, 1997;Karolak, 1996;Sisti and Joseph, 1994). From the results of the questionnaire and past project performance, we identified Bconfused'' projects and their problems, and we analyzed such project problems using logistic regression.…”

Section: Introductionmentioning

confidence: 99%

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An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis

2005

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During software development, projects often experience risky situations. If projects fail to detect such risks, they may exhibit confused behavior. In this paper, we propose a new scheme for characterization of the level of confusion exhibited by projects based on an empirical questionnaire. First, we designed a questionnaire from five project viewpoints, requirements, estimates, planning, team organization, and project management activities. Each of these viewpoints was assessed using questions in which experience and knowledge of software risks are determined. Secondly, we classify projects into Bconfused'' and Bnot confused,'' using the resulting metrics data. We thirdly analyzed the relationship between responses to the questionnaire and the degree of confusion of the projects using logistic regression analysis and constructing a model to characterize confused projects. The experimental result used actual project data shows that 28 projects out of 32 were characterized correctly. As a result, we concluded that the characterization of confused projects was successful. Furthermore, we applied the constructed model to data from other projects in order to detect risky projects. The result of the application of this concept showed that 7 out of 8 projects were classified correctly. Therefore, we concluded that the proposed scheme is also applicable to the detection of risky projects.

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Section: Five Viewpointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis

2005

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“…Given a deadline, the area under the PDF to the right of the deadline represents the probability of unacceptable process durations. Substantial literature addresses the use of activity network models and Monte Carlo simulation to evaluate schedule uncertainty [e.g., [10], [27], [39], and [44]]. However, these methods typically equate risk with uncertainty and do not account for varied consequences.…”

Section: G Process Cost and Schedule Uncertainty And Riskmentioning

confidence: 99%

Modeling impacts of process architecture on cost and schedule risk in product development

Browning

Eppinger

2002

IEEE Trans. Eng. Manage.

385

293

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Abstract-To gain competitive leverage, firms that design and develop complex products seek to increase the efficiency and predictability of their development processes. Process improvement is facilitated by the development and use of models that account for and illuminate important characteristics of the process. Iteration is a fundamental but often unaddressed feature of product development (PD) processes. Its impact is mediated by the architecture of a process, i.e., its constituent activities and their interactions. This paper integrates several important characteristics of PD processes into a single model, highlighting the effects of varying process architecture. The PD process is modeled as a network of activities that exchange deliverables. Each activity has an uncertain duration and cost, an improvement curve, and risks of rework based on changes in its inputs. A work policy governs the timing of activity execution and deliverable exchange (and thus the amount of activity concurrency). The model is analyzed via simulation, which outputs sample cost and schedule outcome distributions. Varying the process architecture input varies the output distributions. Each distribution is used with a target and an impact function to determine a risk factor. Alternative process architectures are compared, revealing opportunities to trade cost and schedule risk. Example results and applications are shown for an industrial process, the preliminary design of an uninhabited combat aerial vehicle. The model yields and reinforces several managerial insights, including: how rework cascades through a PD process, trading off cost and schedule risk, interface criticality, and occasions for iterative overlapping.Index Terms-Activity network, budgeting, cycle time, design iteration, design structure matrix, engineering design management, process architecture, process modeling, process structure, product development, rework, risk management.

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“…Projects in such an uncontrolled state have also been seen in other organizations [3]. Hereafter we shall refer to such projects as "risky projects."…”

Section: Yasunari Takagi Keishi Sakamotomentioning

confidence: 98%

“…The idea of risk management has been suggested as one way of dealing with risky projects [3,4,8,121. Risk management aims to identify the risk factors which may be the reason for loss of control, and mitigate the risks in the projects.…”

Section: Yasunari Takagi Keishi Sakamotomentioning

confidence: 99%

Characterization of risky projects based on project managers evaluation

Mizuno¹,

Kikuno²,

Takagi³

et al.

Proceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium

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During the process of software development, senior managers often find indications that projects are risky and take appropriate actions to recover them from this dangerous status. If senior managers fail to detect such risks, it is possible that such projects may collapse completely.In this paper, we propose a new scheme for the characterization of risky projects based on an evaluation by the project manager. In order to acquire the relevant data to make such an assessment, we first designed a questionnaire from five viewpoints within the projects: requirements, estimations, team organization, planning capability and project management activities. Each of these viewpoints consisted of a number of concrete questions. We then analyzed the responses to the questionnaires as provided by project managers by applying a logistic regression analysis. That is, we determined the coefficients of the logistic model from a set of the questionnaire responses. The experimental results using actual project data in Company A showed that 21 projects out of 32 were predicted correctly. Thus we would expect that the proposed characterizing scheme is the first step toward predicting which projects are risky at an early phase of the development.

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Implementing risk management on software intensive projects

Cited by 53 publications

References 4 publications

An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis

An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis

Modeling impacts of process architecture on cost and schedule risk in product development

Characterization of risky projects based on project managers evaluation

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