13,995 research outputs found
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
This extended paper presents 1) a novel hierarchy and recursion extension to
the process tree model; and 2) the first, recursion aware process model
discovery technique that leverages hierarchical information in event logs,
typically available for software systems. This technique allows us to analyze
the operational processes of software systems under real-life conditions at
multiple levels of granularity. The work can be positioned in-between reverse
engineering and process mining. An implementation of the proposed approach is
available as a ProM plugin. Experimental results based on real-life (software)
event logs demonstrate the feasibility and usefulness of the approach and show
the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware
Modeling and Discovery For Hierarchical Software Event Log Analysis. This
Technical Report version includes the guarantee proofs for the proposed
discovery algorithm
A Denotational Semantics for Communicating Unstructured Code
An important property of programming language semantics is that they should
be compositional. However, unstructured low-level code contains goto-like
commands making it hard to define a semantics that is compositional. In this
paper, we follow the ideas of Saabas and Uustalu to structure low-level code.
This gives us the possibility to define a compositional denotational semantics
based on least fixed points to allow for the use of inductive verification
methods. We capture the semantics of communication using finite traces similar
to the denotations of CSP. In addition, we examine properties of this semantics
and give an example that demonstrates reasoning about communication and jumps.
With this semantics, we lay the foundations for a proof calculus that captures
both, the semantics of unstructured low-level code and communication.Comment: In Proceedings FESCA 2015, arXiv:1503.0437
Process Algebras
Process Algebras are mathematically rigorous languages with well defined semantics that permit describing and verifying properties of concurrent communicating systems.
They can be seen as models of processes, regarded as agents that act and interact continuously with other similar agents and with their common environment. The agents may be real-world objects (even people), or they may be artifacts, embodied perhaps in computer hardware or software systems.
Many different approaches (operational, denotational, algebraic) are taken for describing the meaning of processes. However, the operational approach is the reference one. By relying on the so called Structural Operational Semantics (SOS), labelled transition systems are built and composed by using the different operators of the many different process algebras. Behavioral equivalences are used to abstract from unwanted details and identify those systems that react similarly to external
experiments
Equivalence checking for weak bi-Kleene algebra
Pomset automata are an operational model of weak bi-Kleene algebra, which
describes programs that can fork an execution into parallel threads, upon
completion of which execution can join to resume as a single thread. We
characterize a fragment of pomset automata that admits a decision procedure for
language equivalence. Furthermore, we prove that this fragment corresponds
precisely to series-rational expressions, i.e., rational expressions with an
additional operator for bounded parallelism. As a consequence, we obtain a new
proof that equivalence of series-rational expressions is decidable
Tight polynomial worst-case bounds for loop programs
In 2008, Ben-Amram, Jones and Kristiansen showed that for a simple programming language - representing non-deterministic imperative programs with bounded loops, and arithmetics limited to addition and multiplication - it is possible to decide precisely whether a program has certain growth-rate properties, in particular whether a computed value, or the program's running time, has a polynomial growth rate. A natural and intriguing problem was to move from answering the decision problem to giving a quantitative result, namely, a tight polynomial upper bound. This paper shows how to obtain asymptotically-tight, multivariate, disjunctive polynomial bounds for this class of programs. This is a complete solution: whenever a polynomial bound exists it will be found. A pleasant surprise is that the algorithm is quite simple; but it relies on some subtle reasoning. An important ingredient in the proof is the forest factorization theorem, a strong structural result on homomorphisms into a finite monoid
Causal Consistency for Reversible Multiparty Protocols
In programming models with a reversible semantics, computational steps can be
undone. This paper addresses the integration of reversible semantics into
process languages for communication-centric systems equipped with behavioral
types. In prior work, we introduced a monitors-as-memories approach to
seamlessly integrate reversible semantics into a process model in which
concurrency is governed by session types (a class of behavioral types),
covering binary (two-party) protocols with synchronous communication. The
applicability and expressiveness of the binary setting, however, is limited.
Here we extend our approach, and use it to define reversible semantics for an
expressive process model that accounts for multiparty (n-party) protocols,
asynchronous communication, decoupled rollbacks, and abstraction passing. As
main result, we prove that our reversible semantics for multiparty protocols is
causally-consistent. A key technical ingredient in our developments is an
alternative reversible semantics with atomic rollbacks, which is conceptually
simple and is shown to characterize decoupled rollbacks.Comment: Extended, revised version of a PPDP'17 paper
(https://doi.org/10.1145/3131851.3131864
Exploring the hierarchical structure of human plans via program generation
Human behavior is inherently hierarchical, resulting from the decomposition
of a task into subtasks or an abstract action into concrete actions. However,
behavior is typically measured as a sequence of actions, which makes it
difficult to infer its hierarchical structure. In this paper, we explore how
people form hierarchically-structured plans, using an experimental paradigm
that makes hierarchical representations observable: participants create
programs that produce sequences of actions in a language with explicit
hierarchical structure. This task lets us test two well-established principles
of human behavior: utility maximization (i.e. using fewer actions) and minimum
description length (MDL; i.e. having a shorter program). We find that humans
are sensitive to both metrics, but that both accounts fail to predict a
qualitative feature of human-created programs, namely that people prefer
programs with reuse over and above the predictions of MDL. We formalize this
preference for reuse by extending the MDL account into a generative model over
programs, modeling hierarchy choice as the induction of a grammar over actions.
Our account can explain the preference for reuse and provides the best
prediction of human behavior, going beyond simple accounts of compressibility
to highlight a principle that guides hierarchical planning
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