Our article „Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis“ by Sebastian Nielebock, Robert Heumüller, Kevin Michael Schott, and Frank Ortmeier has been accepted for publication in Springer’s Journal Automated Software Engineering.
In this article, we target the problem of API misuses which occur when developers falsely apply the application programming interface of a certain code library. Existing detection tools apply pattern mining techniques, namely, collect a set of potentially correct API applications and infer frequent usage patterns as correct specifications. This way, violations to these specifications could be determined as API misuses. In this work, we analyzed the effect of different previous filter and search strategies to obtain source code for the pattern mining step and could show that certain strategies improve the results of the pattern mining and misuse detection agnostic to the applied mining and detection technique.
A preprint of this article can be found on arXiv.