Utilizing Creator Profiles for Predicting Valuable User Enhancement Reports

Users of software applications use issue tracking systems (ITSs) to file enhancement reports, which leads to a large quantity of user requests. These reports play a pivotal role in shaping software requirements and continuous product improvement. However, the manual evaluation of these reports by developers and maintainers can be a time‐consuming and labor‐intensive process due to the constant influx of enhancement requests. Timely handling and implementation of these enhancement reports are crucial for enhancing user satisfaction and product competitiveness. In response to this challenge, research has concentrated on automated methods to predict which enhancement reports are likely to gain approval, aiming to maximize the value extracted from user feedback. Nevertheless, existing approaches still fall short in delivering practical results. In this paper, we introduce a novel creator profile‐based approach designed to uncover the dependency between creators' identity and the value of enhancement reports, ultimately enhancing prediction accuracy. Firstly, we present the concept of a “creator profile” and outline a comprehensive methodology for generating creator profiles from the dataset. We then demonstrate how creator profiles can be effectively applied to the task of predicting the approval of enhancement reports. Subsequently, we assess the performance of our approach using a dataset of 40,551 enhancement reports collected from ITSs. The experimental results indicate a substantial improvement over the existing state of the art, particularly in predicting approved reports. For cross‐application prediction, the accuracy reaches 80.7%, while for non–cross‐application prediction, the overall accuracy is 83.6%. In essence, with the proposed approach, over 80% of user requests can be automatically identified for exacting valuable user requirements, which significantly reduces labor costs. The replication package is available at https://github.com/feifeiniu‐se/approval_prediction .