How do we trace requirements: an initial study of analyst behavior in trace validation tasks
Proceedings of the 4th International Workshop on Cooperative and Human Aspects of Software Engineering
Towards overcoming human analyst fallibility in the requirements tracing process (NIER track)
Proceedings of the 33rd International Conference on Software Engineering
Traceability research: taking the next steps
Proceedings of the 6th International Workshop on Traceability in Emerging Forms of Software Engineering
Proceedings of the 6th International Workshop on Traceability in Emerging Forms of Software Engineering
Towards a model of analyst effort for traceability research
Proceedings of the 6th International Workshop on Traceability in Emerging Forms of Software Engineering
Do better IR tools improve the accuracy of engineers' traceability recovery?
Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering
A comparative evaluation of two user feedback techniques for requirements trace retrieval
Proceedings of the 27th Annual ACM Symposium on Applied Computing
On the impact of trace-based feature location in the performance of software maintainers
Journal of Systems and Software
Proceedings of the 2013 International Conference on Software Engineering
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The requirements traceability matrix (RTM) supports many software engineering and software verification and validation (V&V) activities such as change impact analysis, reverse engineering, reuse, and regression testing. The generation of RTMs is tedious and error-prone, though, thus RTMs are often not generated or maintained. Automated techniques have been developed to generate candidate RTMs with some success. When using RTMs to support the V&V of mission-or safety-critical systems, however, a human analyst must vet the candidate RTMs. The focus thus becomes the quality of the final RTM. This paper investigate show human analysts perform when vetting candidate RTMs. Specifically, a study was undertaken at two universities and had 26 participants analyze RTMs of varying accuracy for a Java code formatter program. The study found that humans tend to move their candidate RTM toward the line that represents recall = precision. Participants who examined RTMs with low recall and low precision drastically improved both.