Background. From information theory, surprisal is a measurement of how
unexpected an event is. Statistical language models provide a probabilistic
approximation of natural languages, and because surprisal is constructed with
the probability of an event occuring, it is therefore possible to determine the
surprisal associated with English sentences. The issues and pull requests of
software repository issue trackers give insight into the development process
and likely contain the surprising events of this process.
Objective. Prior works have identified that unusual events in software
repositories are of interest to developers, and use simple code metrics-based
methods for detecting them. In this study we will propose a new method for
unusual event detection in software repositories using surprisal. With the
ability to find surprising issues and pull requests, we intend to further
analyse them to determine if they actually hold importance in a repository, or
if they pose a significant challenge to address. If it is possible to find bad
surprises early, or before they cause additional troubles, it is plausible that
effort, cost and time will be saved as a result.
Method. After extracting the issues and pull requests from 5000 of the most
popular software repositories on GitHub, we will train a language model to
represent these issues. We will measure their perceived importance in the
repository, measure their resolution difficulty using several analogues,
measure the surprisal of each, and finally generate inferential statistics to
describe any correlations.

本文提出了一种利用 surprisal 在软件库中检测异常事件的新方法。通过对 5000 个最热门的 GitHub 软件库的问题和拉请取内容进行分析并生成推理统计数据，分析这些 surprising events 并确定它们在软件库中的重要性和可能对开发者造成的挑战。