An Empirical Study on Practicality of Specification Mining Algorithms on a Real-World Application

Abstract

Dynamic model inference techniques have been the center of many research projects recently. There are now multiple open source implementations of state-of-the-art algorithms, which provide basic abstraction and merging capabilities. Most of these tools and algorithms have been developed with one particular application in mind, which is program comprehension. The output models can abstract away the details of the program and represent the software behaviour in a concise and easy to understand form. However, one application context that is less studied is using such inferred models for debugging, where the behaviour to abstract is a faulty behaviour (e.g., a set of execution traces including a failed test case). We tried to apply some of the existing model inference techniques in a real-world industrial context to support program comprehension for debugging. Our initial experiments have shown many limitations both in terms of implementation as well as the algorithms. The paper will discuss the root cause of the failures and proposes ideas for future improvement.

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

“…Consider developing large, high-level software models, detecting abnormal behavior during the developing process have many benefits, such as reducing debugging time and saving costs. In [124], existing specification mining techniques were applied to test finite state machine in realworld industrial settings and have helped developers in the debugging process. In [125], a diagnosis framework was proposed to automatically localize an error, where specifications were mined from traces.…”