5,010 research outputs found
Consistent SDNs through Network State Fuzzing
The conventional wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Nevertheless, bugs, misconfigurations, faults or attacks can introduce inconsistencies that undermine correct operation. Previous work in this area, however, lacks a holistic methodology to tackle this problem and thus, addresses only certain parts of the problem. Yet, the consistency of the overall system is only as good as its least consistent part. Motivated by an analogy of network consistency checking with program testing, we propose to add active probe-based network state fuzzing to our consistency check repertoire. Hereby, our system, PAZZ, combines production traffic with active probes to continuously test if the actual forwarding path and decision elements (on the data plane) correspond to the expected ones (on the control plane). Our insight is that active traffic covers the inconsistency cases beyond the ones identified by passive traffic. PAZZ prototype was built and evaluated on topologies of varying scale and complexity. Our results show that PAZZ requires minimal network resources to detect persistent data plane faults through fuzzing and localize them quickly
Consistent SDNs through Network State Fuzzing
The conventional wisdom is that a software-defined network (SDN) operates
under the premise that the logically centralized control plane has an accurate
representation of the actual data plane state. Unfortunately, bugs,
misconfigurations, faults or attacks can introduce inconsistencies that
undermine correct operation. Previous work in this area, however, lacks a
holistic methodology to tackle this problem and thus, addresses only certain
parts of the problem. Yet, the consistency of the overall system is only as
good as its least consistent part. Motivated by an analogy of network
consistency checking with program testing, we propose to add active probe-based
network state fuzzing to our consistency check repertoire. Hereby, our system,
PAZZ, combines production traffic with active probes to periodically test if
the actual forwarding path and decision elements (on the data plane) correspond
to the expected ones (on the control plane). Our insight is that active traffic
covers the inconsistency cases beyond the ones identified by passive traffic.
PAZZ prototype was built and evaluated on topologies of varying scale and
complexity. Our results show that PAZZ requires minimal network resources to
detect persistent data plane faults through fuzzing and localize them quickly
while outperforming baseline approaches.Comment: Added three extra relevant references, the arXiv later was accepted
in IEEE Transactions of Network and Service Management (TNSM), 2019 with the
title "Towards Consistent SDNs: A Case for Network State Fuzzing
Learning Tractable Probabilistic Models for Fault Localization
In recent years, several probabilistic techniques have been applied to
various debugging problems. However, most existing probabilistic debugging
systems use relatively simple statistical models, and fail to generalize across
multiple programs. In this work, we propose Tractable Fault Localization Models
(TFLMs) that can be learned from data, and probabilistically infer the location
of the bug. While most previous statistical debugging methods generalize over
many executions of a single program, TFLMs are trained on a corpus of
previously seen buggy programs, and learn to identify recurring patterns of
bugs. Widely-used fault localization techniques such as TARANTULA evaluate the
suspiciousness of each line in isolation; in contrast, a TFLM defines a joint
probability distribution over buggy indicator variables for each line. Joint
distributions with rich dependency structure are often computationally
intractable; TFLMs avoid this by exploiting recent developments in tractable
probabilistic models (specifically, Relational SPNs). Further, TFLMs can
incorporate additional sources of information, including coverage-based
features such as TARANTULA. We evaluate the fault localization performance of
TFLMs that include TARANTULA scores as features in the probabilistic model. Our
study shows that the learned TFLMs isolate bugs more effectively than previous
statistical methods or using TARANTULA directly.Comment: Fifth International Workshop on Statistical Relational AI (StaR-AI
2015
You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems
Properly benchmarking Automated Program Repair (APR) systems should
contribute to the development and adoption of the research outputs by
practitioners. To that end, the research community must ensure that it reaches
significant milestones by reliably comparing state-of-the-art tools for a
better understanding of their strengths and weaknesses. In this work, we
identify and investigate a practical bias caused by the fault localization (FL)
step in a repair pipeline. We propose to highlight the different fault
localization configurations used in the literature, and their impact on APR
systems when applied to the Defects4J benchmark. Then, we explore the
performance variations that can be achieved by `tweaking' the FL step.
Eventually, we expect to create a new momentum for (1) full disclosure of APR
experimental procedures with respect to FL, (2) realistic expectations of
repairing bugs in Defects4J, as well as (3) reliable performance comparison
among the state-of-the-art APR systems, and against the baseline performance
results of our thoroughly assessed kPAR repair tool. Our main findings include:
(a) only a subset of Defects4J bugs can be currently localized by commonly-used
FL techniques; (b) current practice of comparing state-of-the-art APR systems
(i.e., counting the number of fixed bugs) is potentially misleading due to the
bias of FL configurations; and (c) APR authors do not properly qualify their
performance achievement with respect to the different tuning parameters
implemented in APR systems.Comment: Accepted by ICST 201
Towards Structural Testing of Superconductor Electronics
Many of the semiconductor technologies are already\ud
facing limitations while new-generation data and\ud
telecommunication systems are implemented. Although in\ud
its infancy, superconductor electronics (SCE) is capable of\ud
handling some of these high-end tasks. We have started a\ud
defect-oriented test methodology for SCE, so that reliable\ud
systems can be implemented in this technology. In this\ud
paper, the details of the study on the Rapid Single-Flux\ud
Quantum (RSFQ) process are presented. We present\ud
common defects in the SCE processes and corresponding\ud
test methodologies to detect them. The (measurement)\ud
results prove that we are able to detect possible random\ud
defects for statistical purposes in yield analysis. This\ud
paper also presents possible test methodologies for RSFQ\ud
circuits based on defect oriented testing (DOT)
Sensor placement for fault location identification in water networks: A minimum test cover approach
This paper focuses on the optimal sensor placement problem for the
identification of pipe failure locations in large-scale urban water systems.
The problem involves selecting the minimum number of sensors such that every
pipe failure can be uniquely localized. This problem can be viewed as a minimum
test cover (MTC) problem, which is NP-hard. We consider two approaches to
obtain approximate solutions to this problem. In the first approach, we
transform the MTC problem to a minimum set cover (MSC) problem and use the
greedy algorithm that exploits the submodularity property of the MSC problem to
compute the solution to the MTC problem. In the second approach, we develop a
new \textit{augmented greedy} algorithm for solving the MTC problem. This
approach does not require the transformation of the MTC to MSC. Our augmented
greedy algorithm provides in a significant computational improvement while
guaranteeing the same approximation ratio as the first approach. We propose
several metrics to evaluate the performance of the sensor placement designs.
Finally, we present detailed computational experiments for a number of real
water distribution networks
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