You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Interactive theorem provers have been used extensively to reason about various software hardware systems and mathematical theorems. The key challenge when using an interactive prover is finding a suitable sequence of proof steps that will lead to a successful proof requires a significant amount of human intervention. This paper presents an automated technique that takes as input examples of successful proofs and infers an Extended Finite State Machine as output. This can in turn be used to generate proofs of new conjectures. Our preliminary experiments show that the inferred models are generally accurate (contain few false-positive sequences) and that representing existing proofs in such a way can be very useful when guiding new ones.

Cited Abstract(s):
[1]: The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering techniques focus either on control-specific behaviour (e.g. in the form of Finite State Machines), or data-specific behaviour (e.g. as pre post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software's operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these techniques can be non deterministic, the inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presents a novel EFSM inference technique that addresses the problems of inflexibility and non determinism. It also adapts an experimental technique from the field of Machine Learning to evaluate EFSM inference techniques, and applies it to two open-source software projects.
Since this model incorporates data, the merging process includes a step to ensure that the model remains consistent with the data guards. Each transition in the tree is mapped to its corresponding variable configurations. Pairs of states are only merged if the resulting model completely obeys the data classifiers (guard conditions) that were obtained in the previous step. If the inferred data model predicts that the data value for a given transition is followed by a label @math , any merge involving the target state can only occur if the resulting state machine contains an outgoing transition that is labelled by @math . After each merge, the resulting state machine is further post-processed to ensure that each transition is deterministic [1] .