https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:46475 Wed 23 Nov 2022 15:48:35 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:43502 Wed 21 Sep 2022 10:01:20 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:39861 incidental incidents. Our qualitative and quantitative analyses show that incidental incidents are significant in terms of both number and cost. Therefore, it is important to prioritize incidents by identifying incidental incidents in advance to optimize incident management efforts. In particular, we propose an approach, called DeepIP (Deep learning based Incident Prioritization), to prioritizing incidents based on a large amount of historical incident data. More specifically, we design an attention-based Convolutional Neural Network (CNN) to learn a prediction model to identify incidental incidents. We then prioritize all incidents by ranking the predicted probabilities of incidents being incidental. We evaluate the performance of DeepIP using real-world incident data. The experimental results show that DeepIP effectively prioritizes incidents by identifying incidental incidents and significantly outperforms all the compared approaches. For example, the AUC of DeepIP achieves 0.808, while that of the best compared approach is only 0.624 on average.]]> Wed 06 Jul 2022 08:36:19 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:44006 Wed 05 Oct 2022 14:57:36 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:49596 Tue 23 May 2023 12:45:20 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:32674 learning to test, which learns the characteristics of bug-revealing test programs from previous test programs that triggered bugs. Based on the idea of learning to test, we propose LET, an approach to prioritizing test programs for compiler testing acceleration. LET consists of a learning process and a scheduling process. In the learning process, LET identifies a set of features of test programs, trains a capability model to predict the probability of a new test program for triggering compiler bugs and a time model to predict the execution time of a test program. In the scheduling process, LET prioritizes new test programs according to their bug-revealing probabilities in unit time, which is calculated based on the two trained models. Our extensive experiments show that LET significantly accelerates compiler testing. In particular, LET reduces more than 50% of the testing time in 24.64% of the cases, and reduces between 25% and 50% of the testing time in 36.23% of the cases.]]> Tue 10 Jul 2018 15:38:29 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45930 Tue 08 Nov 2022 10:00:09 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:42112 Thu 18 Aug 2022 14:07:42 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:43375 Thu 15 Sep 2022 15:53:31 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:31727 Sat 24 Mar 2018 08:43:31 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:31845 Sat 24 Mar 2018 08:43:16 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:27051 Sat 24 Mar 2018 07:25:23 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:46583 Fri 25 Nov 2022 14:00:32 AEDT ]]>