ORCID Profile
0000-0003-1466-7386
Current Organisations
Deakin University
,
Monash University
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Publisher: IEEE
Date: 08-2014
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/ESEM.2013.13
Publisher: IEEE
Date: 05-2021
Publisher: ACM
Date: 02-10-2016
Publisher: IEEE
Date: 05-2023
Publisher: Association for Computing Machinery (ACM)
Date: 09-01-2019
DOI: 10.1145/3293454
Abstract: Domain models are a useful vehicle for making the interpretation and elaboration of natural-language requirements more precise. Advances in natural-language processing (NLP) have made it possible to automatically extract from requirements most of the information that is relevant to domain model construction. However, alongside the relevant information, NLP extracts from requirements a significant amount of information that is superfluous (not relevant to the domain model). Our objective in this article is to develop automated assistance for filtering the superfluous information extracted by NLP during domain model extraction. To this end, we devise an active-learning-based approach that iteratively learns from analysts’ feedback over the relevance and superfluousness of the extracted domain model elements and uses this feedback to provide recommendations for filtering superfluous elements. We empirically evaluate our approach over three industrial case studies. Our results indicate that, once trained, our approach automatically detects an average of ≈ 45% of the superfluous elements with a precision of ≈ 96%. Since precision is very high, the automatic recommendations made by our approach are trustworthy. Consequently, analysts can dispose of a considerable fraction – nearly half – of the superfluous elements with minimal manual work. The results are particularly promising, as they should be considered in light of the non-negligible subjectivity that is inherently tied to the notion of relevance.
Publisher: ACM
Date: 21-05-2022
Publisher: IEEE
Date: 11-2021
Publisher: IEEE
Date: 09-2023
Publisher: ACM
Date: 18-09-2014
Publisher: Elsevier BV
Date: 08-2023
Publisher: IEEE
Date: 09-2021
Publisher: Springer Science and Business Media LLC
Date: 13-09-2020
DOI: 10.1007/S10664-020-09864-1
Abstract: A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements. In principle, by following suitable writing and markup conventions, one can provide an immediate and unequivocal demarcation of requirements at the time a specification is being developed. However, neither the presence nor a fully accurate enforcement of such conventions is guaranteed. The result is that, in many practical situations, analysts end up resorting to after-the-fact reviews for sifting requirements from other material in a requirements specification. This is both tedious and time-consuming. We propose an automated approach for demarcating requirements in free-form requirements specifications. The approach, which is based on machine learning, can be applied to a wide variety of specifications in different domains and with different writing styles. We train and evaluate our approach over an independently labeled dataset comprised of 33 industrial requirements specifications. Over this dataset, our approach yields an average precision of 81.2% and an average recall of 95.7%. Compared to simple baselines that demarcate requirements based on the presence of modal verbs and identifiers, our approach leads to an average gain of 16.4% in precision and 25.5% in recall. We collect and analyze expert feedback on the demarcations produced by our approach for industrial requirements specifications. The results indicate that experts find our approach useful and efficient in practice. We developed a prototype tool, named DemaRQ, in support of our approach. To facilitate replication, we make available to the research community this prototype tool alongside the non-proprietary portion of our training data.
Publisher: ACM
Date: 17-05-2022
Publisher: Springer Science and Business Media LLC
Date: 25-04-2022
DOI: 10.1007/S10669-022-09855-1
Abstract: For mission critical (MC) applications such as bushfire emergency management systems (EMS), understanding the current situation as a disaster unfolds is critical to saving lives, infrastructure and the environment. Incident control-room operators manage complex information and systems, especially with the emergence of Big Data. They are increasingly making decisions supported by artificial intelligence (AI) and machine learning (ML) tools for data analysis, prediction and decision-making. As the volume, speed and complexity of information increases due to more frequent fire events, greater availability of myriad IoT sensors, smart devices, satellite data and burgeoning use of social media, the advances in AI and ML that help to manage Big Data and support decision-making are increasingly perceived as “Black Box”. This paper aims to scope the requirements for bushfire EMS to improve Big Data management and governance of AI/ML. An analysis of ModelOps technology, used increasingly in the commercial sector, is undertaken to determine what components might be fit-for-purpose. The result is a novel set of ModelOps features, EMS requirements and an EMS-ModelOps framework that resolves more than 75% of issues whilst being sufficiently generic to apply to other types of mission-critical applications.
Publisher: IEEE
Date: 05-2023
Publisher: IEEE
Date: 09-2021
Publisher: ACM
Date: 18-08-2013
Publisher: IEEE
Date: 09-2023
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2023
Publisher: IEEE
Date: 06-2022
Publisher: ACM
Date: 11-2016
Publisher: ACM
Date: 07-11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: ACM
Date: 07-11-2022
Publisher: IEEE
Date: 05-2021
Publisher: IEEE
Date: 05-2021
Publisher: ACM
Date: 29-06-2020
Publisher: IEEE
Date: 05-2023
Publisher: IEEE
Date: 09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2015
Publisher: IEEE
Date: 09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 08-2023
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2023
Publisher: IEEE
Date: 05-2023
Publisher: Elsevier BV
Date: 06-2023
Publisher: IEEE
Date: 08-2022
Publisher: IEEE
Date: 09-2019
Publisher: Springer Science and Business Media LLC
Date: 18-04-2019
Publisher: IEEE
Date: 08-2015
Publisher: ACM
Date: 30-08-2015
Publisher: MDPI AG
Date: 04-12-2021
DOI: 10.3390/S21238117
Abstract: Recent scientific and technological advancements driven by the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI), distributed computing and data communication technologies have opened up a vast range of opportunities in many scientific fields—spanning from fast, reliable and efficient data communication to large-scale cloud/edge computing and intelligent big data analytics. Technological innovations and developments in these areas have also enabled many opportunities in the space industry. The successful Mars landing of NASA’s Perseverance rover on 18 February 2021 represents another giant leap for humankind in space exploration. Emerging research and developments of connectivity and computing technologies in IoT for space/non-terrestrial environments is expected to yield significant benefits in the near future. This survey paper presents a broad overview of the area and provides a look-ahead of the opportunities made possible by IoT and space-based technologies. We first survey the current developments of IoT and space industry, and identify key challenges and opportunities in these areas. We then review the state-of-the-art and discuss future opportunities for IoT developments, deployment and integration to support future endeavors in space exploration.
No related grants have been discovered for Chetan Arora.