Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Rachel Harrison"'
Autor:
Plinio S. Leitao-Junior, Deuslirio Silva-Junior, Rachel Harrison, Celso G. Camilo-Junior, Altino Dantas
Publikováno v:
SAC
Fault localization is the activity of precisely indicating the faulty commands in a buggy program. It is known to be a highly costly and tedious process. Automating this process has been the goal of many studies, showing it to be a challenging proble
Publikováno v:
Data in Brief, Vol 18, Iss, Pp 840-845 (2018)
Data in Brief
Addi. Archivo Digital para la Docencia y la Investigación
instname
Data in Brief
Addi. Archivo Digital para la Docencia y la Investigación
instname
Classifying software defects according to any defined taxonomy is not straightforward. In order to be used for automatizing the classification of software defects, two sets of defect reports were collected from public issue tracking systems from two
Publikováno v:
Applied Soft Computing. 62:579-591
In software engineering, associating each reported defect with a category allows, among many other things, for the appropriate allocation of resources. Although this classification task can be automated using standard machine learning techniques, the
Autor:
Celso G. Camilo-Junior, Plinio S. Leitao-Junior, Diogo M. de Freitas, Silvia Regina Vergilio, Rachel Harrison
Publikováno v:
Information and Software Technology. 123:106295
Context Software Fault Localisation (FL) refers to finding faulty software elements related to failures produced as a result of test case execution. This is a laborious and time consuming task. To allow FL automation search-based algorithms have been
Publikováno v:
Neurosci Lett
The dynamics of the resting-state activity in brain functional networks are complex, containing meaningful patterns over multiple temporal scales. Such physiologic complexity is often diminished in older adults. Here we aim to examine if the resting-
Publikováno v:
CEC
Fault localisation is an expensive and time-consuming stage of software maintenance. Research is continuing to develop new techniques to automate the process of reducing the effort needed for fault localisation without losing quality. For instance, s
Publikováno v:
EASE
Semi-Supervised Learning (SSL) is a data mining technique which comes between supervised and unsupervised techniques, and is useful when a small number of instances in a dataset are labelled but a lot of unlabelled data is also available. This is the
Publikováno v:
CEC
In this short paper, we compare well-known rule/tree classifiers in software defect prediction with the CTC decision tree classifier designed to deal with class imbalanced. It is well-known that most software defect prediction datasets are highly imb
Publikováno v:
RE Workshops
In this paper we apply self-labeling algorithms as Semi-Supervised Classification (SSC) techniques in order to automate the classification of functional and non-functional requirements contained in reviews in the App Store. In this domain, where it i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f2d07d6303f5ac421abff4b6ba4b4b6
https://radar.brookes.ac.uk/radar/items/17f8ef4a-3c18-4453-a000-1c97708b0288/1/
https://radar.brookes.ac.uk/radar/items/17f8ef4a-3c18-4453-a000-1c97708b0288/1/
Publikováno v:
Artificial Intelligence in Medicine
Individuals with type 1 diabetes have to monitor their blood glucose levels, determine the quantity of insulin required to achieve optimal glycaemic control and administer it themselves subcutaneously, multiple times per day. To help with this proces
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8162230bdfcfb2d2e4df3fc982642a77
https://radar.brookes.ac.uk/radar/items/f440600a-77b9-45aa-8301-7f2d833396fd/1/
https://radar.brookes.ac.uk/radar/items/f440600a-77b9-45aa-8301-7f2d833396fd/1/