Product assignment recommender

Effectiveness of software development process depends on the accuracy of data in supporting tools. In particular, a customer issue assigned to a wrong product team takes much longer to resolve (negatively affecting user-perceived quality) and wastes developer effort. In Open Source Software (OSS) and in commercial projects values in issue-tracking systems (ITS) or Customer Relationship Management (CRM) systems are often assigned by non-developers for whom the assignment task is difficult. We propose PAR (Product Assignment Recommender) to estimate the odds that a value in the ITS is incorrect. PAR learns from the past activities in ITS and performs prediction using a logistic regression model. Our demonstrations show how PAR helps developers to focus on fixing real problems, and how it can be used to improve data accuracy in ITS by crowd-sourcing non-developers to verify and correct low-accuracy data. http://youtu.be/IuykbzSTj8s