H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 49 Citations 7,519 185 World Ranking 3057 National Ranking 1610

Overview

What is she best known for?

The fields of study she is best known for:

  • Software engineering
  • Software
  • Artificial intelligence

Her primary areas of study are Traceability, Software engineering, Requirements traceability, Software and TRACE. Her Traceability research includes themes of Requirements management, Software system, Tracing, Systems engineering and Probabilistic logic. Her Requirements management research is multidisciplinary, incorporating perspectives in Risk analysis and Requirements elicitation.

Her Software engineering study combines topics in areas such as Software maintenance, Software development and Software development process. Her research in Requirements traceability tackles topics such as Reverse semantic traceability which are related to areas like Traceability matrix and Software verification and validation. Her primary area of study in Software is in the field of Domain analysis.

Her most cited work include:

  • Event-based traceability for managing evolutionary change (270 citations)
  • Software traceability: trends and future directions (184 citations)
  • Goal-centric traceability for managing non-functional requirements (173 citations)

What are the main themes of her work throughout her whole career to date?

Her main research concerns Traceability, Software engineering, Software, TRACE and Requirements traceability. Her Traceability research integrates issues from Software system, Tracing, Systems engineering, Risk analysis and Source code. Her Software engineering study incorporates themes from Software maintenance, Software development, Requirements engineering, Software architecture and Requirements analysis.

Her studies examine the connections between Software and genetics, as well as such issues in Domain, with regards to Domain knowledge. Her research in Requirements traceability intersects with topics in Traceability matrix, Reverse semantic traceability, Formal specification, Software verification and validation and Process management. Her Data mining study combines topics from a wide range of disciplines, such as Probabilistic logic and Cluster analysis.

She most often published in these fields:

  • Traceability (39.38%)
  • Software engineering (38.94%)
  • Software (29.20%)

What were the highlights of her more recent work (between 2017-2021)?

  • Software (29.20%)
  • Traceability (39.38%)
  • TRACE (21.24%)

In recent papers she was focusing on the following fields of study:

Her primary scientific interests are in Software, Traceability, TRACE, Source code and Domain. Her work on Requirements engineering as part of general Software research is frequently linked to Natural disaster, bridging the gap between disciplines. Her research integrates issues of Information retrieval and Tracing in her study of Traceability.

Jane Cleland-Huang has included themes like Software system, Commit, Artificial intelligence, Code refactoring and Machine learning in her Source code study. Her research on Domain concerns the broader Software engineering. The Software engineering study combines topics in areas such as Ontology, Semantic network and Semantic Web.

Between 2017 and 2021, her most popular works were:

  • Traceability in the wild: automatically augmenting incomplete trace links (36 citations)
  • Dronology: an incubator for cyber-physical systems research (29 citations)
  • Traceability in the Wild: Automatically Augmenting Incomplete Trace Links (15 citations)

In her most recent research, the most cited papers focused on:

  • Software
  • Software engineering
  • Artificial intelligence

Jane Cleland-Huang mainly focuses on Software, Traceability, Process management, Source code and Software system. The various areas that Jane Cleland-Huang examines in her Software study include Domain, Software engineering and Distributed computing. Her Traceability research includes themes of Tracing and Data science.

The concepts of her Process management study are interwoven with issues in Goal modeling and Software evolution. Her studies deal with areas such as Software development, Classifier, Task analysis, Information retrieval and Commit as well as Source code. Her study in Software system is interdisciplinary in nature, drawing from both Knowledge sharing and Life-critical system.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Event-based traceability for managing evolutionary change

J. Cleland-Huang;C.K. Chang;M. Christensen.
IEEE Transactions on Software Engineering (2003)

425 Citations

Software traceability: trends and future directions

Jane Cleland-Huang;Orlena C. Z. Gotel;Jane Huffman Hayes;Patrick Mäder.
international conference on software engineering (2014)

291 Citations

Goal-centric traceability for managing non-functional requirements

Jane Cleland-Huang;Raffaella Settimi;Oussama BenKhadra;Eugenia Berezhanskaya.
international conference on software engineering (2005)

287 Citations

Best Practices for Automated Traceability

J. Cleland-Huang;R. Settimi;E. Romanova;B. Berenbach.
IEEE Computer (2007)

258 Citations

Utilizing supporting evidence to improve dynamic requirements traceability

J. Cleland-Huang;R. Settimi;Chuan Duan;Xuchang Zou.
international conference on requirements engineering (2005)

237 Citations

Software and Systems Traceability

Jane Cleland-Huang;Orlena Gotel;Andrea Zisman.
(2012)

236 Citations

Automated classification of non-functional requirements

Jane Cleland-Huang;Raffaella Settimi;Xuchang Zou;Peter Solc.
Requirements Engineering (2007)

234 Citations

The Detection and Classification of Non-Functional Requirements with Application to Early Aspects

J. Cleland-Huang;R. Settimi;Xuchang Zou;P. Solc.
ieee international conference on requirements engineering (2006)

233 Citations

A machine learning approach for tracing regulatory codes to product specific requirements

Jane Cleland-Huang;Adam Czauderna;Marek Gibiec;John Emenecker.
international conference on software engineering (2010)

209 Citations

On-demand feature recommendations derived from mining public product descriptions

Horatiu Dumitru;Marek Gibiec;Negar Hariri;Jane Cleland-Huang.
international conference on software engineering (2011)

185 Citations

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