Real-Time Personalization in Adaptive IDEs

Schmidmaier, Matthias; Han, Zhiwei; Weber, Thomas; Liu, Yuanting and Hussmann, Heinrich (2019): Real-Time Personalization in Adaptive IDEs. In: Adjunct Publication of the 27Th Conference on User Modeling, Adaptation and Personalization (Acm Umap '19 Adjunct): pp. 81-86

Abstract

Integrated Development Environments (IDEs) are used for a variety of software development tasks. Their complexity makes them challenging to use though, especially for less experienced developers. In this paper, we outline our approach for an user-adaptive IDE that is able to track the interactions, recognize the user's intent and expertise, and provide relevant, personalized recommendations in real-time. To obtain a user model and provide recommendations, interaction data is processed in a two-stage process: first, we derive a bandit based global model of general task patterns from a dataset of labeled interactions. Second, when the user is working with the IDE, we apply a pre-trained classifier in real-time to get task labels from the user's interactions. With those and user feedback we fine-tune a local copy of the global model. As a result, we obtain a personalized user model which provides user-specific recommendations. We finally present various approaches for using these recommendations to adapt the IDE's interface. Modifications range from visual highlighting to task automation, including explanatory feedback.

• Item Type: Journal article
• Faculties: Mathematics, Computer Science and Statistics > Computer Science
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• Language: English