It is well-known that simple, accidental BGP configuration errors can disrupt Internet connectivity. Yet little is known about the frequency of misconfiguration or its causes, except for the few spectacular incidents of widespread outages. In this paper, we present the first quantitative study of BGP misconfiguration. Over a three week period, we analyzed routing table advertisements from 23 vantage points across the Internet backbone to detect incidents of misconfiguration. For each incident we polled the ISP operators involved to verify whether it was a misconfiguration, and to learn the cause of the incident. We also actively probed the Internet to determine the impact of misconfiguration on connectivity.

This article provides an overview of the EPIC architecture being developed by Kieras and Meyer for modeling human cognition and performance (Kieras, Wood, & Meyer, 1997; Meyer & Kieras, 1997a, 1997b). EPIC is similar in spirit to the Model Human Processor (MHP; Card, Moran, & Newell, 1983), but EPIC incorporates many recent theoretical and empiri- cal results about human performance in the form of a software framework for computer simulation modeling. Using EPIC, a model can be con- structed that represents the general procedures required to perform a complex multimodal task a a set of production rules. When the model is supplied with the external stimuli for a specffic task, it will then execute the procedures in whatever way the task requires, thus simulating a human 's performing the task and generating the predicted actions in simulated real time. EPIC is an architecture for constructing models of 394 performance. It is notet a learning system and so has no mechanisms for learning how to perform a task. Rather, the purpose of EPIC is to repre- sent in detail the perceptual, motor, and cognitive constraints on the human ability to perform tasks

ing with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org. Which GOMS? p. 2 2 Keywords: GOMS, cognitive modeling, usability engineering ABSTRACT Since the seminal Card, Moran, & Newell (1983) book, The psychology of human-computer interaction, the GOMS model has been one of the few widely known theoretical concepts in human-computer interaction. This concept has spawned much research to verify and extend the original work and has been used in real-world design and evaluation situations. This paper synthesizes the previous work on GOMS to provide an integrated view of GOMS models and how they can be used in design. We briefly describe the major variants of GOMS that have matured sufficiently to be used in actual design. We then provide guidance to practitioners about which GOMS var...

Security toolbars in a web browser show security-related information about a website to help users detect phishing attacks. Because the toolbars are designed for humans to use, they should be evaluated for usability – that is, whether these toolbars really prevent users from being tricked into providing personal information. We conducted two user studies of three security toolbars and other browser security indicators and found them all ineffective at preventing phishing attacks. Even though subjects were asked to pay attention to the toolbar, many failed to look at it; others disregarded or explained away the toolbars ’ warnings if the content of web pages looked legitimate. We found that many subjects do not understand phishing attacks or realize how sophisticated such attacks can be.

With the growing importance of systems containing arbitrary semistructured relationships, the need for supporting users searching in such repositories has grown. Currently support for users' search needs either has required domain-specific user interfaces or has required users to be schema experts. We have developed a generalpurpose tool that offers users helpful navigation and refinement options for seeking information in these semistructured repositories. We show how a tool can be built without requiring domain-specific assumptions about the information being explored. In addition to describing a general approach to the problem, we provide a set of natural, general-purpose refinement tactics, many generalized from past work on textual information retrieval.

Architecturally-sensitive usability scenarios are important usability concerns that require early consideration in software design so that architectural support can render them easy and cost-effective to implement. Examples include providing the ability to cancel a command, undo commands, aggregate data, etc. This chapter reports on our experiences applying these scenarios to the design of MERBoard, a wall-sized interactive system developed by NASA to assist Mars Rover science teams with collaborative data analysis. We applied the scenarios during a major redesign of the software architecture that introduced usability as a valued quality attribute. In the process, we found that the scenarios were well-received by developers who readily understood how they related to MERBoard, that they applied to a collaborative workspace despite having been initially developed for a single-user desktop system, that they had a real impact on the architecture redesign, and that the scenario consideration process was quick and not too onerous for any of the team members.

An undo facility is an essential component of most interactive applications. In current operating system shells, whether textual or graphical, such facilities are typically very poor. Algorithms are presented for adding a recovery mechanism to a shell which allows previous commands to be selectively undone and redone, and previous versions of files to be recovered. The recovery mechanism involves making the shell control resources in a more intelligent way. Programs are run under greater control, with the shell monitoring and analysing their resource requests. This provides better high level information to the shell and, for example, provides techniques to prevent foreign or untrustworthy programs from doing any damage, and to reduce problems with conflicting resource requests from concurrent programs. A prototype implementation has been implemented to investigate the convenience and natural feel of these facilities. 1 Introduction A recovery ability is a crucial feature that many int...

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When (re)designing a work environment, tasks or functions are allocated more or less explicitly among humans and between humans and machines. After a brief review and discussion of issues related to task allocation, we argue that an important aspect to be addressed when (re)designing socio-technical systems is the systematic evaluation of the impact of allocation decisions on the overall reliability of such systems. It is contended that the cooperative dimension of such systems is one of the main elements that contribute to this reliability. This claim leads us to present a conceptual framework for modelling the human contribution to the overall reliability of complex cooperative work systems. The framework is characterized here as a set of notions, mainly regulation and shared context, used to discuss and reason about this role of humans in the error tolerance properties of such systems. These notions are demonstrated with different examples derived from empirical studies of work practices in two complex cooperative work settings (air traffic and nuclear reactor control). We then show how this conceptual framework can be used for the evaluation of allocation decisions and more generally to inform design.

There are 2 excellent reasons to compute usability problem-discovery rates. First, an estimate of the problem-discovery rate is a key component for projecting the required sample size for a usability study. Second, practitioners can use this estimate to calculate the proportion of discovered problems for a given sample size. Unfortunately, smallsample estimates of the problem-discovery rate suffer from a serious overestimation bias. This bias can lead to serious underestimation of required sample sizes and serious overestimation of the proportion of discovered problems. This article contains descriptions and evaluations of a number of methods for adjusting small-sample estimates of the problem-discovery rate to compensate for this bias. Aseries of Monte Carlo simulations provided evidence that the average of a normalization procedure and Good–Turing (Jelinek, 1997; Manning & Schutze, 1999) discounting produces highly accurate estimates of usability problem-discovery rates from small sample sizes. 1.