TaDeR: A New Task Dependency Recommendation for Project Management Platform
Quynh Nguyen, Dac H. Nguyen, Son T. Huynh, Hoa K. Dam, Binh T. Nguyen

TL;DR
This paper introduces TaDeR, a deep learning-based task dependency recommendation system for project management, demonstrating significant accuracy improvements on large datasets from Moodle and Apache projects.
Contribution
It presents a novel deep neural network approach with feature engineering and embedding methods for task dependency prediction in project management tools.
Findings
GloVe embeddings and CNN model achieved best performance
Adding time filter improves recommendation accuracy
Model outperforms traditional TF-IDF baseline
Abstract
Many startups and companies worldwide have been using project management software and tools to monitor, track and manage their projects. For software projects, the number of tasks from the beginning to the end is quite a large number that sometimes takes a lot of time and effort to search and link the current task to a group of previous ones for further references. This paper proposes an efficient task dependency recommendation algorithm to suggest tasks dependent on a given task that the user has just created. We present an efficient feature engineering step and construct a deep neural network to this aim. We performed extensive experiments on two different large projects (MDLSITE from moodle.org and FLUME from apache.org) to find the best features in 28 combinations of features and the best performance model using two embedding methods (GloVe and FastText). We consider three types of…
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Taxonomy
TopicsSoftware System Performance and Reliability · Software Engineering Research · Online Learning and Analytics
MethodsGloVe Embeddings
