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    block this user Anthony Finkelstein

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    Computer Science, UCL, London

    Early Failure Prediction in Feature Request Management Systems

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    Online feature request management systems are popular tools for gathering stakeholder requirements during system evolution. Deciding which feature requests require attention and how much upfront analysis to perform on them is an important problem in this context: too little upfront analysis may result in inadequate functionalities being developed, costly changes, and wasted development effort; too much upfront analysis is a waste of time and resources. Early predictions about which feature requests are most likely to fail due to insufficient or inadequate upfront analysis could facilitate such decisions. Our objective is to study whether it is possible to make such predictions automatically from the characteristics of the online discussions on feature requests. The paper presents a tool-implemented framework that au- tomatically constructs failure predictionmodels usingmachine- learning classification algorithms and compares the perfor- mance of the different techniques for the Firefox and Netbeans projects. The comparison relies on a cost-benefit model for assessing the value of additional upfront analysis. In this model, the value of additional upfront analysis depends on its probability of success in preventing failures and on the relative cost of the failures it prevents compared to its own cost. We show that for reasonable estimations of these two parameters automated prediction models provide more value than a set of baselines for some failure types and projects. This suggests that automated failure prediction during online requirements elicitation may be a promising approach for guiding requirements engineering efforts in online settings

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    Description

    Title : Early Failure Prediction in Feature Request Management Systems
    Author(s) : Camilo Fitzgerald, Emmanuel Letier, Anthony Finkelstein
    Abstract : Online feature request management systems are popular tools for gathering stakeholder requirements during system evolution. Deciding which feature requests require attention and how much upfront analysis to perform on them is an important problem in this context: too little upfront analysis may result in inadequate functionalities being developed, costly changes, and wasted development effort; too much upfront analysis is a waste of time and resources. Early predictions about which feature requests are most likely to fail due to insufficient or inadequate upfront analysis could facilitate such decisions. Our objective is to study whether it is possible to make such predictions automatically from the characteristics of the online discussions on feature requests. The paper presents a tool-implemented framework that au- tomatically constructs failure predictionmodels usingmachine- learning classification algorithms and compares the perfor- mance of the different techniques for the Firefox and Netbeans projects. The comparison relies on a cost-benefit model for assessing the value of additional upfront analysis. In this model, the value of additional upfront analysis depends on its probability of success in preventing failures and on the relative cost of the failures it prevents compared to its own cost. We show that for reasonable estimations of these two parameters automated prediction models provide more value than a set of baselines for some failure types and projects. This suggests that automated failure prediction during online requirements elicitation may be a promising approach for guiding requirements engineering efforts in online settings
    Keywords : early failure prediction, cost benefit require, feature requests management systems, global software development, ments engineering, open source

    Subject : unspecified
    Area : Other
    Language : English
    Year : 2011

    Affiliations Computer Science, UCL, London
    Journal : Proceedings of the Requirements Engineering conference 2011
    Publisher : IEEE
    Pages : 229-238
    Url : http://www.cs.ucl.ac.uk/staff/A.Finkelstein/papers/repredict.pdf

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