Semi-automatic Software Feature-Relevant Information Extraction from Natural Language User Manuals

Quirchmayr, Thomas; Paech, Barbara; Kohl, Roland; Karey, Hannes

doi:10.1007/978-3-319-54045-0_19

Cited by 15 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other works address similar problems in the software engineering research area [62,204,261], in the agricultural regulation domain [77], and -up to some extent -in the digital forensics field [258], but the results are far from being applicable to complex, unstructured, heterogeneous standard specifications.…”

Section: Certifying Systems Against Textual Descriptions and System Runsmentioning

confidence: 99%

Towards a framework for certification of reliable autonomous systems

Fisher

Mascardi

Rozier

et al. 2020

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

A computational system is called autonomous if it is able to make its own decisions, or take its own actions, without human supervision or control. The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article.

show abstract

Section: Certifying Systems Against Textual Descriptions and System Runsmentioning

confidence: 99%

Towards a framework for certification of reliable autonomous systems

Fisher

Mascardi

Rozier

et al. 2020

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

show abstract

“…Other works address similar problems in the software engineering research area [56,187,237], in the agricultural regulation domain [68], and -up to some extent -in the digital forensics field [234], but the results are far from being applicable to complex, unstructured, heterogeneous standard specifications.…”

Section: Certifying Systems Against Textual Descriptions and System Runsmentioning

confidence: 99%

Towards a Framework for Certification of Reliable Autonomous Systems

Fisher¹,

Mascardi²,

Rozier³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Similarly, devising a complete set of generalizable criteria for distinguishing superfluous and relevant domain model elements is difficult, if not infeasible. Queries and heuristics, e.g., as employed by Rago et al [2016] and Quirchmayr et al [2017], can be helpful for specific domains and document types; however, these approaches cannot adapt themselves to the reasoning applied by experts in a domain that has not been studied a priori. In contrast, our approach, which builds on ML, can mimic the logic applied by experts in any domain, without the need for this logic to be made explicit and articulated.…”

Section: Conclusion Validitymentioning

confidence: 99%

An Active Learning Approach for Improving the Accuracy of Automated Domain Model Extraction

Arora

Sabetzadeh

Nejati

et al. 2019

ACM Trans. Softw. Eng. Methodol.

View full text Add to dashboard Cite

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.

show abstract

Semi-automatic Software Feature-Relevant Information Extraction from Natural Language User Manuals

Cited by 15 publications

References 26 publications

Towards a framework for certification of reliable autonomous systems

Towards a framework for certification of reliable autonomous systems

Towards a Framework for Certification of Reliable Autonomous Systems

An Active Learning Approach for Improving the Accuracy of Automated Domain Model Extraction

Contact Info

Product

Resources

About