Recommender Systems for Software Project Managers
This work tackles coordination challenges for software project managers, but it is incremental as it focuses on evaluating existing tools rather than introducing new methods.
The study evaluated four open-source recommender systems and implemented a custom engine using Lenskit and Mahout to address coordination issues in software project management, identifying key functions and problems during the experiment.
The design of recommendation systems is based on complex information processing and big data interaction. This personalized view has evolved into a hot area in the past decade, where applications might have been proved to help for solving problem in the software development field. Therefore, with the evolvement of Recommendation System in Software Engineering (RSSE), the coordination of software projects with their stakeholders is improving. This experiment examines four open source recommender systems and implemented a customized recommender engine with two industrial-oriented packages: Lenskit and Mahout. Each of the main functions was examined and issues were identified during the experiment.