Detection of Duplicate Defect Reports Using Natural Language Processing
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Software product release planning through optimization and what-if analysis
Information and Software Technology
Can We Beat the Complexity of Very Large-Scale Requirements Engineering?
REFSQ '08 Proceedings of the 14th international conference on Requirements Engineering: Foundation for Software Quality
CAiSE'07 Proceedings of the 19th international conference on Advanced information systems engineering
EA-Miner: towards automation in aspect-oriented requirements engineering
Transactions on aspect-oriented software development III
REFSQ'11 Proceedings of the 17th international working conference on Requirements engineering: foundation for software quality
Empirical Software Engineering
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Developing large complex software products aimed for a broad market involves a great flow of wishes and requirements. The former are elicited from customers while the latter are brought forth by the developing organization. These are preferably kept separated to preserve the different perspectives. The interrelationships should however be identified and maintained to enable well-founded decisions. Unfortunately, the current manual linkage is cumbersome, time-consuming, and error-prone. This paper presents a pragmatic linguistic engineering approach to how statistical natural language processing may be used to support the manual linkage between customer wishes and product requirements by suggesting potential links. An evaluation with real requirements from industry is presented. It shows that in a realistic setting, automatic support could make linkage faster for at least 50% of the links. An estimation based on our evaluation also shows that considerable time savings are possible. The results, together with the identified enhancement, are promising for improving software quality and saving time in industrial requirements engineering.