Managing the software process
The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering - Special issue on software reliability
Improvement of software process by process description and benefit estimation
Proceedings of the 17th international conference on Software engineering
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Analyzing effects of cost estimation accuracy on quality and productivity
Proceedings of the 20th international conference on Software engineering
Death March: The Complete Software Developer's Guide to Surviving "Mission Impossible" Projects
Death March: The Complete Software Developer's Guide to Surviving "Mission Impossible" Projects
Software Engineering Risk Management: Finding Your Path through the Jungle
Software Engineering Risk Management: Finding Your Path through the Jungle
A Discipline for Software Engineering
A Discipline for Software Engineering
Identifying Key Attributes of Projects that Affect the Field Quality of Communication Software
COMPSAC '00 24th International Computer Software and Applications Conference
Empirical Software Engineering
Patterns of conflict among software components
Journal of Systems and Software
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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 27 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.