Early failure prediction in feature request management systems

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 automatically constructs failure prediction models using machine-learning classification algorithms and compares the performance 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.