Practical LR Parser Generation

Parsing is a fundamental building block in modern compilers, and for industrial programming languages, it is a surprisingly involved task. There are known approaches to generate parsers automatically, but the prevailing consensus is that automatic parser generation is not practical for real programming languages: LR/LALR parsers are considered to be far too restrictive in the grammars they support, and LR parsers are often considered too inefficient in practice. As a result, virtually all modern languages use recursive-descent parsers written by hand, a lengthy and error-prone process that dramatically increases the barrier to new programming language development. In this work we demonstrate that, contrary to the prevailing consensus, we can have the best of both worlds: for a very general, practical class of grammars -- a strict superset of Knuth's canonical LR -- we can generate parsers automatically, and the resulting parser code, as well as the generation procedure itself, is highly efficient. This advance relies on several new ideas, including novel automata optimization procedures; a new grammar transformation ("CPS"); per-symbol attributes; recursive-descent actions; and an extension of canonical LR parsing, which we refer to as XLR, which endows shift/reduce parsers with the power of bounded nondeterministic choice. With these ingredients, we can automatically generate efficient parsers for virtually all programming languages that are intuitively easy to parse -- a claim we support experimentally, by implementing the new algorithms in a new software tool called langcc, and running them on syntax specifications for Golang 1.17.8 and Python 3.9.12. The tool handles both languages automatically, and the generated code, when run on standard codebases, is 1.2x faster than the corresponding hand-written parser for Golang, and 4.3x faster than the CPython parser, respectively

User support for software development technologies

The adoption of software development technologies is very closely related to the topic of user support. This is especially true in early phases, when the users are not familiar with the modification or the build processes of the software that has to be developed nor with the technology used for software development. This work introduces an approach to improve the usability of software development technologies represented by the Combinatory Logic Synthesizer (CL)S Framework. (CL)S is based on a type inhabitation algorithm for the combinatory logic with intersection types and aims to automatically create software components from a domain-specified repository. The framework yields a complete enumeration of all inhabitants. The inhabitation results are computed in the form of tree grammars. Unfortunately, the underlying type system allows limited application of domain-specific knowledge. To compensate for this limit, this work provides a framework for debugging intersection type specifications and filtering inhabitation results using domain-specific constraints as main aspects. The aim of the debugger is to make potentially incomplete or erroneous input specifications and decisions of the inhabitation algorithm understandable for those who are not experts in the field of type theory. The combination of tree grammars and graph theory forms the foundation of a clear representation of the computed results that informs users about the search process of the algorithm. The graphical representations are based on hypergraphs that illustrate the inhabitation in a step-wise fashion. Within the scope of this work, three filtering algorithms were implemented and investigated. The filtering algorithm integrated into the framework for user support and used for the restriction of inhabitation results is practically feasible and represents a clear improvement compared to existing approaches. It is based on modifying the tree grammars resulting from the (CL)S Framework. Additionally, the usability of the (CL)S Framework is supported by eight perspectives included in a web-based integrated development environment (IDE) that provides detailed graphical and textual information about the synthesis