Software error detection is one of the most challenging problems in software engineering. Now, you can learn how to make the most of software testing by selecting test cases to maximize the probability of revealing latent errors. Software Error Detection through Testing and Analysis begins with a thorough discussion of test-case selection and a review of the concepts, notations, and principles used in the book.
Let us first discuss some issues related, directly ,indirectly, to error detection and correction.
Types of ErrorsRedundancyDetection Versus CorrectionForward Error Correction Versus RetransmissionCoding
We evaluate the effect of adding parse features to a leading model of preposition usage. Results show a signiﬁcant improvement in the preposition selection task on native speaker text and a modest increment in precision and recall in an ESL error detection task. Analysis of the parser output indicates that it is robust enough in the face of noisy non-native writing to extract useful information.
Despite the rising interest in developing grammatical error detection systems for non-native speakers of English, progress in the ﬁeld has been hampered by a lack of informative metrics and an inability to directly compare the performance of systems developed by different researchers. In this paper we address these problems by presenting two evaluation methodologies, both based on a novel use of crowdsourcing.
This work introduces a new approach to checking treebank consistency. Derivation trees based on a variant of Tree Adjoining Grammar are used to compare the annotation of word sequences based on their structural similarity. This overcomes the problems of earlier approaches based on using strings of words rather than tree structure to identify the appropriate contexts for comparison. We report on the result of applying this approach to the Penn Arabic Treebank and how this approach leads to high precision of error detection. ...
Automatic error detection is desired in the post-processing to improve machine translation quality. The previous work is largely based on conﬁdence estimation using system-based features, such as word posterior probabilities calculated from N best lists or word lattices. We propose to incorporate two groups of linguistic features, which convey information from outside machine translation systems, into error detection: lexical and syntactic features.
This paper describes a method of detecting grammatical and lexical errors made by Japanese learners of English and other techniques that improve the accuracy of error detection with a limited amount of training data. In this paper, we demonstrate to what extent the proposed methods hold promise by conducting experiments using our learner corpus, which contains information on learners’ errors.
Data can be corrupted during transmission. For reliable
communication, error must be detected and corrected
are implemented either at the data link layer or the
transport layer of the OSI model.Error detection uses the concept of redundancy, which
means adding extra bits for detecting errors at the
Arabic handwriting recognition (HR) is a challenging problem due to Arabic’s connected letter forms, consonantal diacritics and rich morphology. In this paper we isolate the task of identiﬁcation of erroneous words in HR from the task of producing corrections for these words. We consider a variety of linguistic (morphological and syntactic) and non-linguistic features to automatically identify these errors. Our best approach achieves a roughly ∼15% absolute increase in F-score over a simple but reasonable baseline. ...
We demonstrate that the bidirectionality of deep grammars, allowing them to generate as well as parse sentences, can be used to automatically and effectively identify errors in the grammars. The system is tested on two implemented HPSG grammars: Jacy for Japanese, and the ERG for English. Using this system, we were able to increase generation coverage in Jacy by 18% (45% to 63%) with only four weeks of grammar development.
Networks must be able to transfer data from
one device to another with complete accuracy.
Data can be corrupted during transmission.
For reliable communication, errors must be
detected and corrected.Single bit errors are the least likely type of
errors in serial data transmission because
the noise must have a very short duration
which is very rare. However this kind of
errors can happen in parallel transmission.
Data transmission can contain errors.
– Burst errors of length n.
(n: distance between the first and last errors in data
• How to detect errors.
– If only data is transmitted, errors cannot be detected.
Send more information with data that satisfies a
The availability of learner corpora, especially those which have been manually error-tagged or shallow-parsed, is still limited. This means that researchers do not have a common development and test set for natural language processing of learner English such as for grammatical error detection. Given this background, we created a novel learner corpus that was manually error-tagged and shallowparsed.
Faced with the problem of annotation errors in part-of-speech (POS) annotated corpora, we develop a method for automatically correcting such errors. Building on top of a successful error detection method, we ﬁrst try correcting a corpus using two off-the-shelf POS taggers, based on the idea that they enforce consistency; with this, we ﬁnd some improvement. After some discussion of the tagging process, we alter the tagging model to better account for problematic tagging distinctions.
Bài giảng Mạng máy tính - Chương 5a cung cấp những nội dung kiến thức về Data Link Layer. Chương này gồm có các nội dung kiến thức sau: Introduction and services, error detection and correction, multiple access protocols & LAN, Link-layer addressing & ARP. Mời mọi người cùng tham khảo.
• Network errors are in the form of corrupted data or lost data.
• Network errors occur naturally on all networks due to electrical noise and distortion
and must be detected and corrected by either hardware or software.
• Bit Error Rates (BERs) are calculated as the number of bits in error divided by the
number of bits transmitted. A BER of 1 in 100,000 might be shown as 1:105
as a BER of 10-5
• Errors often occur in bursts where many bits in a sequence will be in...
Nowadays, digital terrain models (DTM) are an important source of spatial data for various applications in many scientific disciplines. Therefore, special attention is given to their main characteristic ‐ accuracy. At it is well known, the source data for DTM creation contributes a large amount of errors, including gross errors, to the final product.
In this study, a novel approach to robust dialogue act detection for error-prone speech recognition in a spoken dialogue system is proposed. First, partial sentence trees are proposed to represent a speech recognition output sentence. Semantic information and the derivation rules of the partial sentence trees are extracted and used to model the relationship between the dialogue acts and the derivation rules.
It is important to correct the errors in the results of speech recognition to increase the performance of a speech translation system. This paper proposes a method for correcting errors using the statistical features of character co-occurrence, and evaluates the method. The proposed method comprises two successive correcting processes. The first process uses pairs of strings: the first string is an erroneous substring of the utterance predicted by speech recognition, the second string is the corresponding section of the actual utterance.