Object-based Control/Data-flow Analysis

Not only does a clear distinction between control and data flow enhance the readability of models, but it also allows different tools to operate on the two distinct parts of the model. This paper shows how the modelling based on control/data-flow analysis can benefit from an object-based approach. We have developed a translation mechanism that is faithful and gives an extra dimension (hierarchy) to the existing paradigm of control and data flow interacting in a model. Our methodology provides a comprehensible separation of these two parts, which can be used to feed another analysis or synthesis tools, while still being able to reason about both parts through formal methods of verification

Tracking Data-Flow with Open Closure Types

Type systems hide data that is captured by function closures in function types. In most cases this is a beneficial design that favors simplicity and compositionality. However, some applications require explicit information about the data that is captured in closures. This paper introduces open closure types, that is, function types that are decorated with type contexts. They are used to track data-flow from the environment into the function closure. A simply-typed lambda calculus is used to study the properties of the type theory of open closure types. A distinctive feature of this type theory is that an open closure type of a function can vary in different type contexts. To present an application of the type theory, it is shown that a type derivation establishes a simple non-interference property in the sense of information-flow theory. A publicly available prototype implementation of the system can be used to experiment with type derivations for example programs.Comment: Logic for Programming Artificial Intelligence and Reasoning (2013

RFC: Data flow from the MICE experiment

This article can be accessed at the link below.This document sketches out the flow of data from the MICE experiment, as I currently understand it. This includes not only illustrating the structure of the data flow, but also setting out a consistent vocabulary with which to describe it. Many aspects of this data flow are either misunderstood by me, currently undecided, not yet implemented, or simply have never been considered before; so feedback is both welcomed and essential. Background information about job submission and file storage on the Grid can be found in previous MICE Notes and the references therein. In particular the first two sections of Note 247 are meant to provide a gentle introduction to Grid data storage from the MICE perspective, and timid MICE may wish to read those first

TRACTABLE DATA-FLOW ANALYSIS FOR DISTRIBUTED SYSTEMS

Automated behavior analysis is a valuable technique in the development and maintainence of distributed systems. In this paper, we present a tractable dataflow analysis technique for the detection of unreachable states and actions in distributed systems. The technique follows an approximate approach described by Reif and Smolka, but delivers a more accurate result in assessing unreachable states and actions. The higher accuracy is achieved by the use of two concepts: action dependency and history sets. Although the technique, does not exhaustively detect all possible errors, it detects nontrivial errors with a worst-case complexity quadratic to the system size. It can be automated and applied to systems with arbitrary loops and nondeterministic structures. The technique thus provides practical and tractable behavior analysis for preliminary designs of distributed systems. This makes it an ideal candidate for an interactive checker in software development tools. The technique is illustrated with case studies of a pump control system and an erroneous distributed program. Results from a prototype implementation are presented

Data-flow Analysis of Programs with Associative Arrays

Dynamic programming languages, such as PHP, JavaScript, and Python, provide built-in data structures including associative arrays and objects with similar semantics-object properties can be created at run-time and accessed via arbitrary expressions. While a high level of security and safety of applications written in these languages can be of a particular importance (consider a web application storing sensitive data and providing its functionality worldwide), dynamic data structures pose significant challenges for data-flow analysis making traditional static verification methods both unsound and imprecise. In this paper, we propose a sound and precise approach for value and points-to analysis of programs with associative arrays-like data structures, upon which data-flow analyses can be built. We implemented our approach in a web-application domain-in an analyzer of PHP code.Comment: In Proceedings ESSS 2014, arXiv:1405.055

Control versus Data Flow in Parallel Database Machines

The execution of a query in a parallel database machine can be controlled in either a control flow way, or in a data flow way. In the former case a single system node controls the entire query execution. In the latter case the processes that execute the query, although possibly running on different nodes of the system, trigger each other. Lately, many database research projects focus on data flow control since it should enhance response times and throughput. The authors study control versus data flow with regard to controlling the execution of database queries. An analytical model is used to compare control and data flow in order to gain insights into the question which mechanism is better under which circumstances. Also, some systems using data flow techniques are described, and the authors investigate to which degree they are really data flow. The results show that for particular types of queries data flow is very attractive, since it reduces the number of control messages and balances these messages over the node