Integrating Abstract Caches with Symbolic Pipeline Analysis

Static worst-case execution time analysis of real-time tasks is based on abstract models that capture the timing behavior of the processor on which the tasks run. For complex processors, task-level execution time bounds are obtained by a state space exploration which involves the abstract model and the program. Partial state space exploration is not sound. Symbolic methods using binary decision diagrams (BDDs) allow for a full state space exploration of the pipeline, thereby maintaining soundness. Caches are too large to admit an efficient BDD representation. On the other hand, invariants of the cache state can be computed efficiently using abstract interpretation. How to integrate abstract caches with symbolic-state pipeline analysis is an open question. We propose a semi-symbolic domain to solve this problem. Statistical data from industrial-level software and WCET tools indicate that this new domain will enable an efficient analysis

The CHREST model of active perception and its role in problem solving

We discuss the relation of TEC to a computational model of expert perception, CHREST, based on the chunking theory. TEC’s status as a verbal theory leaves several questions unanswerable, such as the precise nature of internal representations used, or the degree of learning required to obtain a particular level of competence: CHREST may help answer such questions

Package for Calculations and Simplifications of Expressions with Dirac Matrixes (MatrixExp)

This paper describes a package for calculations of expressions with Dirac matrixes. Advantages to existing similar packages are described. MatrixExp package is intended for simplification of complex expressions involving $\gamma$-matrixes, providing such tools as automatic Feynman parameterization, integration in $d$-dimensional space, sorting and grouping of results in a given order. Also, in comparison with existing similar package Tracer, presented package MatrixExp has more enhanced input possibility. User-available functions of MatrixExp package are described in detail. Also an example of calculation of Feynman diagram for process $b\to s\gamma g$ with application of functions of MatrixExp package is presented

Automatic Generation of Minimal Cut Sets

A cut set is a collection of component failure modes that could lead to a system failure. Cut Set Analysis (CSA) is applied to critical systems to identify and rank system vulnerabilities at design time. Model checking tools have been used to automate the generation of minimal cut sets but are generally based on checking reachability of system failure states. This paper describes a new approach to CSA using a Linear Temporal Logic (LTL) model checker called BT Analyser that supports the generation of multiple counterexamples. The approach enables a broader class of system failures to be analysed, by generalising from failure state formulae to failure behaviours expressed in LTL. The traditional approach to CSA using model checking requires the model or system failure to be modified, usually by hand, to eliminate already-discovered cut sets, and the model checker to be rerun, at each step. By contrast, the new approach works incrementally and fully automatically, thereby removing the tedious and error-prone manual process and resulting in significantly reduced computation time. This in turn enables larger models to be checked. Two different strategies for using BT Analyser for CSA are presented. There is generally no single best strategy for model checking: their relative efficiency depends on the model and property being analysed. Comparative results are given for the A320 hydraulics case study in the Behavior Tree modelling language.Comment: In Proceedings ESSS 2015, arXiv:1506.0325

Integrated testing and verification system for research flight software design document

The NASA Langley Research Center is developing the MUST (Multipurpose User-oriented Software Technology) program to cut the cost of producing research flight software through a system of software support tools. The HAL/S language is the primary subject of the design. Boeing Computer Services Company (BCS) has designed an integrated verification and testing capability as part of MUST. Documentation, verification and test options are provided with special attention on real time, multiprocessing issues. The needs of the entire software production cycle have been considered, with effective management and reduced lifecycle costs as foremost goals. Capabilities have been included in the design for static detection of data flow anomalies involving communicating concurrent processes. Some types of ill formed process synchronization and deadlock also are detected statically

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal