Specification Mining of Symbolic Scenario-Based Models

Many dynamic analysis approaches to specification mining that ex-tract behavioral models from execution traces, do not consider ob-ject identities which limit their power when used to analyze traces of general object oriented programs. In this work we present a novel specification mining approach that considers object identi-ties, and, moreover, generalizes from specifications involving con-crete objects to their symbolic class-level abstractions. Our ap-proach uses data mining methods to extract significant scenario-based specifications in the form of Damm and Harel’s live sequence charts (LSC), a formal and expressive extension of classic sequence diagrams. We guarantee that all mined symbolic LSCs are sig-nificant (statistically sound) and all significant symbolic LSCs are mined (statistically complete). The technique can potentially be ap-plied to general object oriented programs to reveal expressive and useful reverse-engineered candidate specifications. 1

Automatic Recommendation of API Methods from Feature Requests

MERLIO

Mining Branching-Time Scenarios

Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces