Classification of Software Project Risk Factors Using Machine Learning Approach

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 385)


Software project risk can be defined as a various future harms that could be possible on the software due to some non-noticeable mistakes done during the development of software project. Analyzing the risk is required in order to reduce the risk before it can harm the quality of the project. This paper interprets an idea of software project risk factors classification which involves the use of support vector machines (SVM) i.e., machine learning approach to improve the accuracy of the results. Risk assessment is a crucial task as the projects are facing increased complexity with higher uncertainties. In order to make the risk assessment easier, it is necessary for the developers to identify the hardbound and less hardbound risk factors. Classifying the risk factors will help the developers to identify the most effective risk which will ultimately become easy for the software developer to take some mitigation actions as early as possible. Hence the proposed approach reduces the developer’s effort and increases the accuracy in identifying the harmful risk factors.


Analytical Hierarchy Process (AHP) Support Vector Machines (SVM) Risk Factors WEKA Tool 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Prerna Chaudhary
    • 1
  • Deepali Singh
    • 1
  • Ashish Sharma
    • 1
  1. 1.GLA UniversityMathuraIndia

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