To determine the limits of human observers' ability to identify visually presented American Sign Language (ASL), the contrast s and the amount of additive noise n in dynamic ASL images were varied independently. Contrast was tested over a 4:1 range; the rms signal-to-noise ratios (s/n) investigated were s/n = 1/4, 1/2, 1, and infinity (which is used to designate the original, uncontaminated images). Fourteen deaf subjects were tested with an intelligibility test composed of 85 isolated ASL signs, each 2-3 sec in length. For these ASL signs (64 x 96 pixels, 30 frames/sec), subjects' performance asymptotes between s/n = 0.5 and 1.0; further increases in s/n do not improve intelligibility. Intelligibility was found to depend only on s/n and not on contrast. A formulation in terms of logistic functions was proposed to derive intelligibility of ASL signs from s/n, sign familiarity, and sign difficulty. Familiarity (ignorance) is represented by additive signal-correlated noise; it represents the likelihood of a subject's knowing a particular ASL sign, and it adds to s/n. Difficulty is represented by a multiplicative difficulty coefficient; it represents the perceptual vulnerability of an ASL sign to noise and it adds to log(s/n).
Dynamic images of individual signs of American Sign Language (ASL) with a resolution of 96 X 64 pixels were bandpass filtered in adjacent frequency bands. Intelligibility was determined by testing deaf subjects fluent in ASL. The following results were obtained. (1) By iteratively varying the center frequencies and bandwidths of the spatial bandpass filters, it was possible to divide the original signal into four different component bands of high intelligibility. (2) The measured temporal-frequency spectrum was approximately the same in all bands. (3) The masking of signals in band i by noise in band j was found to be inversely proportional to log [f signal/f noise]. At constant performance, the ratio of root-mean-square signal amplitude to noise amplitude, s/n, was the same for bands 2,3, and 4 and higher for band 1. (4) When weak signals i and j were added linearly, there was a slight intelligibility advantage for signals in the same band (i = j) compared with signals in adjacent bands and for signals in adjacent bands compared with signals in distant bands.
System debugging is the diagnostic and repair work that begins after a system fails, and it concludes with successful repair and testing of the product. The knowledge and skills of system debuggers are varied. Accordingly, this article will cover a diversity of topics, both psychological and technological, including traits of expert debuggers, mental representations used by experts for understanding systems, strategies used in debugging, social factors impacting debugging effectiveness, the management of debugging expertise, and debugging technologies and tools.
This article is organized in the following manner. After a brief discussion of the history of debugging, a five‐stage model of debugging is described, consisting of familiarization, stabilization, localization, correction, and validation. Several of these stages are used to organize the two following sections of this article, which concern the psychology and technology of debugging. This article concludes with a discussion of management issues relevant to system debugging.
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