Kleene Algebras, Regular Languages and Substructural Logics

We introduce the two substructural propositional logics KL, KL+, which use disjunction, fusion and a unary, (quasi-)exponential connective. For both we prove strong completeness with respect to the interpretation in Kleene algebras and a variant thereof. We also prove strong completeness for language models, where each logic comes with a different interpretation. We show that for both logics the cut rule is admissible and both have a decidable consequence relation.Comment: In Proceedings GandALF 2014, arXiv:1408.556

Reducing a Set of Regular Expressions and Analyzing Differences of Domain-specific Statistic Reporting

Due to the large amount of daily scientific publications, it is impossible to manually review each one. Therefore, an automatic extraction of key information is desirable. In this paper, we examine STEREO, a tool for extracting statistics from scientific papers using regular expressions. By adapting an existing regular expression inclusion algorithm for our use case, we decrease the number of regular expressions used in STEREO by about $33.8\%$. We reveal common patterns from the condensed rule set that can be used for the creation of new rules. We also apply STEREO, which was previously trained in the life-sciences and medical domain, to a new scientific domain, namely Human-Computer-Interaction (HCI), and re-evaluate it. According to our research, statistics in the HCI domain are similar to those in the medical domain, although a higher percentage of APA-conform statistics were found in the HCI domain. Additionally, we compare extraction on PDF and LaTeX source files, finding LaTeX to be more reliable for extraction

Efficient asymmetric inclusion of regular expressions with interleaving and counting for XML type-checking

The inclusion of Regular Expressions (REs) is the kernel of any type-checking algorithm for XML manipulation languages. XML applications would benefit from the extension of REs with interleaving and counting, but this is not feasible in general, since inclusion is EXPSPACE-complete for such extended REs. In Colazzo et al. (2009) [1] we introduced a notion of ?conflict-free REs?, which are extended REs with excellent complexity behaviour, including a polynomial inclusion algorithm [1] and linear membership (Ghelli et al., 2008 [2]). Conflict-free REs have interleaving and counting, but the complexity is tamed by the ?conflict-free? limitations, which have been found to be satisfied by the vast majority of the content models published on the Web.However, a type-checking algorithm needs to compare machine-generated subtypes against human-defined supertypes. The conflict-free restriction, while quite harmless for the human-defined supertype, is far too restrictive for the subtype. We show here that the PTIME inclusion algorithm can be actually extended to deal with totally unrestricted REs with counting and interleaving in the subtype position, provided that the supertype is conflict-free.This is exactly the expressive power that we need in order to use subtyping inside type-checking algorithms, and the cost of this generalized algorithm is only quadratic, which is as good as the best algorithm we have for the symmetric case (see [1]). The result is extremely surprising, since we had previously found that symmetric inclusion becomes NP-hard as soon as the candidate subtype is enriched with binary intersection, a generalization that looked much more innocent than what we achieve here

Abstract Execution: Automatically Proving Infinitely Many Programs

Abstract programs contain schematic placeholders representing potentially infinitely many concrete programs. They naturally occur in multiple areas of computer science concerned with correctness: rule-based compilation and optimization, code refactoring and other source-to-source transformations, program synthesis, Correctness-by-Construction, and more. Mechanized correctness arguments about abstract programs are frequently conducted in interactive environments. While this permits expressing arbitrary properties quantifying over programs, substantial effort has to be invested to prove them manually by writing proof scripts. Existing approaches to proving abstract program properties automatically, on the other hand, lack expressiveness. Frequently, they only support placeholders representing all possible instantiations; in some cases, minor refinements are supported. This thesis bridges that gap by presenting Abstract Execution (AE), an automatic reasoning technique for universal behavioral properties of abstract programs. The restriction to universal (no existential quantification) and behavioral (not addressing internal structure) properties excludes certain applications; however, it is the key to automation. Our logic for Abstract Execution uses abstract state changes to represent unknown effects on local variables and the heap, and models abrupt completion by symbolic branching. In this logic, schematic placeholders have names: It is possible to re-use them at several places, representing the same program elements in potentially different contexts. Furthermore, the represented concrete programs can be constrained by an expressive specification language, which is a unique feature of AE. We use the theory of dynamic frames to scale between full abstraction and total precision of frame specifications, and support fine-grained pre- and postconditions for (abrupt) completion. We implemented AE by extending the program verifier KeY. Specifically for relational verification of abstract Java programs, we developed REFINITY, a graphical KeY frontend. We used REFINITY it in our signature application of AE: to model well-known statement-level refactoring techniques and prove their conditional safety. Several yet undocumented behavioral preconditions for safe refactorings originated in this case study, which is one of very few attempts to statically prove behavioral correctness of statement-level refactorings, and the only one to cover them to that extent. AE extends Symbolic Execution (SE) for abstract programs. As a foundational contribution, we propose a general framework for SE based on the semantics of symbolic states. It natively integrates state merging by supporting m-to-n transitions. We define two orthogonal correctness notions, exhaustiveness and precision, and formally prove their relation to program proving and bug detection. Finally, we introduce Modal Trace Logic (MTL), a trace-based logic to represent a variety of different program verification tasks, especially for relational verification. It is a “plug-in” logic which can be integrated on-demand with formal languages that have a trace semantics. The core of MTL is the trace modality, which allows expressing that a specification approximates an implementation after a trace abstraction step. We demonstrate the versatility of this approach by formalizing concrete verification tasks in MTL, ranging from functional verification over program synthesis to program evolution. To reason about MTL problems, we translate them to symbolic traces. We suggest Symbolic Trace Logic (STL), which comes with a sequent calculus to prove symbolic trace inclusions. This requires checking symbolic states for subsumption; to that end, we provide two generally useful notions of symbolic state subsumption. This framework relates as follows to the other parts of this thesis: We use the language of abstract programs to express synthesis and compilation, which connects MTL to AE. Moreover, symbolic states of STL are based on our framework for SE

The Inclusion Problem for Regular Expressions

This paper presents a new polynomial-time algorithm for the inclusion problem for certain pairs of regular expressions. The algorithm is not based on construction of finite automata, and can therefore be faster than the lower bound implied by the Myhill-Nerode theorem. The algorithm automatically discards unnecessary parts of the right-hand expression. In these cases the right-hand expression might even be 1- ambiguous. For example, if r is a regular expression such that any DFA recognizing r is very large, the algorithm can still, in time independent of r, decide that the language of ab is included in that of (a+r)b. The algorithm is based on a syntax-directed inference system. It takes arbitrary regular expressions as input, and if the 1-ambiguity of the right-hand expression becomes a problem, the algorithm will report this