Software Engineering Event Modeling using Relative Time in Temporal Knowledge Graphs
This work addresses software engineering project management by providing datasets and methods, but it is incremental as it extends existing temporal models with relative time information.
The authors tackled the problem of modeling software engineering events on GitHub by creating temporal knowledge graphs for link and time prediction, but found existing models performed poorly on extrapolated queries, with no concrete performance numbers provided.
We present a multi-relational temporal Knowledge Graph based on the daily interactions between artifacts in GitHub, one of the largest social coding platforms. Such representation enables posing many user-activity and project management questions as link prediction and time queries over the knowledge graph. In particular, we introduce two new datasets for i) interpolated time-conditioned link prediction and ii) extrapolated time-conditioned link/time prediction queries, each with distinguished properties. Our experiments on these datasets highlight the potential of adapting knowledge graphs to answer broad software engineering questions. Meanwhile, it also reveals the unsatisfactory performance of existing temporal models on extrapolated queries and time prediction queries in general. To overcome these shortcomings, we introduce an extension to current temporal models using relative temporal information with regards to past events.