We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL). The LTL synthesis problem is a well-known algorithmic challenge with a long history and an annual competition is organized to track the improvement of algorithms and tooling over time. New approaches using machine learning might open a lot of possibilities in this area, but suffer from the lack of sufficient amounts of training data. In this paper, we consider a method to generate large amounts of additional training data, i.e., pairs of specifications and circuits implementing them. We ensure that this synthetic data is sufficiently close to human-written specifications by mining common patterns from the specifications used in the synthesis competitions. We show that hierarchical Transformers trained on this synthetic data solve a significant portion of problems from the synthesis competitions, and even out-of-distribution examples from a recent case study.

In the development of safety and mission-critical systems, including autonomous space robotic missions, complex behaviour is captured during the requirements elicitation phase. Requirements are typically expressed using natural language which is ambiguous and not amenable to formal verification methods that can provide robust guarantees of system behaviour. To support the definition of formal requirements, specification patterns provide reusable, logic-based templates. A suite of robotic specification patterns, along with their formalisation in NASA's Formal Requirements Elicitation Tool (FRET) already exists. These pre-existing requirement patterns are domain agnostic and, in this paper we explore their applicability for space missions. To achieve this we carried out a literature review of existing space missions and formalised their requirements using FRET, contributing a corpus of space mission requirements. We categorised these requirements using pre-existing specification patterns which demonstrated their applicability in space missions. However, not all of the requirements that we formalised corresponded to an existing pattern so we have contributed 5 new requirement specification patterns as well as several variants of the existing and new patterns. We also conducted an expert evaluation of the new patterns, highlighting their benefits and limitations.

A circuit, predicted by a hierarchical Transformer, satisfying the specification (right).

We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL). The LTL synthesis problem is a well-known algorithmic challenge with a long history and an annual competition is organized to track the improvement of algorithms and tooling over time. New approaches using machine learning might open a lot of possibilities in this area, but suffer from the lack of sufficient amounts of training data. In this paper, we consider a method to generate large amounts of additional training data, i.e., pairs of specifications and circuits implementing them. We ensure that this synthetic data is sufficiently close to human-written specifications by mining common patterns from the specifications used in the synthesis competitions.