An Empirical Study on Practicality of Specification Mining Algorithms on a Real-world Application
10.1109/ICPC.2019.00020
1903.11242
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.