Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - EA-Analyzer
T2 - ASE 2009, 24th IEEE/ACM International Conference on Automated Software Engineering
AU - Sardinha, Alberto
AU - Chitchyan, Ruzanna
AU - Weston, Nathan
AU - Rashid, Awais
PY - 2009
Y1 - 2009
N2 - One of the aims of aspect-oriented requirements engineering is to address the composability and subsequent analysis of crosscutting and non-crosscutting concerns during requirements engineering. Composing concerns may help to reveal conflicting dependencies that need to be identified and resolved. However, detecting conflicts in a large set of textual aspect-oriented requirements is an error-prone and time-consuming task. This paper presents EA-analyzer, the first automated tool for identifying conflicts in aspect-oriented requirements specified in natural-language text. The tool is based on a novel application of a Bayesian learning method that has been effective at classifying text. We present an empirical evaluation of the tool with three industrial-strength requirements documents from different real-life domains. We show that the tool achieves up to 92.97% accuracy when one of the case study documents is used as a training set and the other two as a validation set.
AB - One of the aims of aspect-oriented requirements engineering is to address the composability and subsequent analysis of crosscutting and non-crosscutting concerns during requirements engineering. Composing concerns may help to reveal conflicting dependencies that need to be identified and resolved. However, detecting conflicts in a large set of textual aspect-oriented requirements is an error-prone and time-consuming task. This paper presents EA-analyzer, the first automated tool for identifying conflicts in aspect-oriented requirements specified in natural-language text. The tool is based on a novel application of a Bayesian learning method that has been effective at classifying text. We present an empirical evaluation of the tool with three industrial-strength requirements documents from different real-life domains. We show that the tool achieves up to 92.97% accuracy when one of the case study documents is used as a training set and the other two as a validation set.
KW - Aspect-Oriented Requirements Engineering
KW - Aspect-Oriented Software Development
KW - Conflicting Dependencies
KW - Requirements Analysis
KW - Requirements Composition
U2 - 10.1109/ASE.2009.31
DO - 10.1109/ASE.2009.31
M3 - Conference contribution/Paper
SN - 978-1-4244-5259-0
SP - 530
EP - 534
BT - Automated Software Engineering, 2009. ASE '09. 24th IEEE/ACM International Conference on
PB - IEEE Publishing
Y2 - 16 November 2009 through 20 November 2009
ER -