Formal Methods

@techreport{RISC7136,
author = {Wolfgang Schreiner and William Steingartner},
title = {{Semantics-Based Rapid Prototyping of a Subset of SQL}},
language = {english},
abstract = {This report documents the application of our semantics-based language generator SLANG to developing a rapid prototype of a non-trivial domain-specific language, a substantial subset of the Structured Query Language SQL that we have named SubSQL. After developing a mathematical/logical formulation of the language’s abstract syntax, formal type system, and denotational semantics, we have translated this formulation into a SLANG specification from which the SLANG software generates Java code that implements a parser, a printer, a type-checker, and an executor of the language. This implementation is based on several manually created Java classes that implement the mathematical domains and operations used in the formalization, a simple persistent database, and a high-level application programming interface that allows to execute complete SubSQL scripts from file or individual SubSQL commands within Java programs. The results represent a blueprint for the semantics-based development of other domain-specific languages of similar complexity.},
number = {25-02},
year = {2025},
month = {February},
keywords = {formal semantics of programming languages, domain specific languages, rapid prototyping, interpreters},
sponsor = {Aktion Österreich–Slowakei project 2024-05-15-001, KEGA project 030TUKE-4/2023},
length = {179},
license = {CC BY 4.0 International},
type = {RISC Report Series},
institution = {Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz},
address = {Altenberger Straße 69, 4040 Linz, Austria},
issn = {2791-4267 (online)}
}

@techreport{RISC6684,
author = {Bruno Buchberger},
title = {{Is ChatGPT Smarter Than Master’s Applicants?}},
language = {English},
abstract = {During the selection procedure for a particular informatics fellowship program sponsored by Upper Austrian companies, I ask the applicants a couple of simple technical questions about programming, etc., in a Zoom meeting. I put the same questions to the dialogue system ChatGPT, [ChatGPT]. The result surprised me: Nearly all answers of ChatGPT were totally correct and nicely explained. Also, in the dialogues to clarify some critical points in the answers, the explanations by ChatGPT were amazingly clear and goal-oriented.In comparison: I tried out the same questions in the personal Zoom interviews with approximately 30 applicants from five countries. Only the top three candidates (with a GPA of 1.0, i.e., the highest possible GPA in their bachelor’s study) performed approximately equally well in the interview. All the others performed (far) worse than ChatGPT. And, of course, all answers from ChatGPT came within 1 to 10 seconds, whereas most of the human applicants' answers needed lengthy and arduous dialogues.I am particularly impressed by the ability of ChatGPT to extract meaningful and well-structured programs from problem specifications in natural language. In this experiment, I also added some questions that ask for proofs for simple statements in natural language, which I do not ask in the student's interviews. The performance of ChatGPT was quite impressive as far as formalization and propositional logic are concerned. In examples where predicate logic reasoning is necessary, the ChatGPT answers are not (yet?) perfect. I am pleased to see that ChatGPT tries to present the proofs in a “natural style” This is something that I had as one of my main goals when I initiated the Theorema project in 1995. I think we already achieved this in the early stage of Theorema, and we performed this slightly better and more systematically than ChatGPT does.I also tried to develop a natural language input facility for Theorema in 2017, i.e., a tool to formalize natural language statements in predicate logic. However, I could not continue this research for a couple of reasons. Now I see that ChatGPT achieved this goal. Thus, I think that the following combination of methods could result in a significant leap forward:- the “natural style” proving methods that we developed within Theorema (for the automated generation of programs from specifications, the automated verification of programs in the frame of knowledge, and the automated proof of theorems in theories), in particular, my “Lazy Thinking Method” for algorithm synthesis from specifications- and the natural language formalization techniques of ChatGPT.I propose this as a research project topic and invite colleagues and students to contact me and join me in this effort: Buchberger.bruno@gmail.com.},
number = {23-04},
year = {2023},
month = {January},
keywords = {ChatGPT, automated programming, program synthesis, automated proving, formalization of natural language, master's screening},
length = {30},
license = {CC BY 4.0 International},
type = {RISC Report Series},
institution = {Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz},
address = {Altenberger Straße 69, 4040 Linz, Austria},
issn = {2791-4267 (online)}
}

@techreport{RISC6749,
author = {Wolfgang Schreiner and William Steingartner},
title = {{The SLANG Semantics-Based Language Generator}},
language = {english},
abstract = {This report documents the SLANG semantics-based language generator. SLANG is a software for generating rapid prototype implementations of programming languages from their formal specifications. Its input is a text file that describes the abstract syntax of a language and its concrete text representation; from this, a parser is generated (utilizing the ANTLR4 tool) that transforms the text representation of a program into its abstract syntax tree and a printer that generates from the abstract syntax tree its text representation. Furthermore, one can equip the language with a formal type system (by logical inference rules) from which a type checker is generated. Finally, one can give the language a formal semantics, in the denotational style (by function equations) and/or in the big-step operational style (by transition steps); from this, a language interpreter is generated. SLANG is implemented in Java and produces Java source code; it should be easy to extend the software also to other target languages.},
number = {23-13},
year = {2023},
month = {September},
keywords = {formal semantics of programming languages, denotational semantics, operational semantics, type systems, interpreters},
sponsor = {Supported by the Slovak Academic Information Agency SAIA project 2023-03-15-001 “Semantics-Based Rapid Prototyping of Domain-Specific Languages”},
length = {59},
license = {CC BY 4.0 International},
type = {RISC Report Series},
institution = {Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz},
address = {Altenberger Straße 69, 4040 Linz, Austria},
issn = {2791-4267 (online)}
}