Software reconnaissance: mapping program features to code
Journal of Software Maintenance: Research and Practice
Recovering Traceability Links between Code and Documentation
IEEE Transactions on Software Engineering
Locating Features in Source Code
IEEE Transactions on Software Engineering
Assessing test-driven development at IBM
Proceedings of the 25th International Conference on Software Engineering
Test-Driven Development as a Defect-Reduction Practice
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
Advancing Candidate Link Generation for Requirements Tracing: The Study of Methods
IEEE Transactions on Software Engineering
Can LSI help Reconstructing Requirements Traceability in Design and Test?
CSMR '06 Proceedings of the Conference on Software Maintenance and Reengineering
ICPC '06 Proceedings of the 14th IEEE International Conference on Program Comprehension
IEEE Transactions on Software Engineering
ACM Transactions on Software Engineering and Methodology (TOSEM)
Combining textual and structural analysis of software artifacts for traceability link recovery
TEFSE '09 Proceedings of the 2009 ICSE Workshop on Traceability in Emerging Forms of Software Engineering
Improving automated requirements trace retrieval: a study of term-based enhancement methods
Empirical Software Engineering
Test intents: enhancing the semantics of requirements traceability links in test cases
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Requirements traceability is linking requirements to software artifacts, such as source code, test-cases and configuration files. For stakeholders of software, it is important to understand which requirements were tested, whether sufficiently, if at all. Hence tracing requirements in test-cases is an important problem. In this paper, we build on existing research and use features, realization of functional requirements in software [15], to automatically create requirements traceability links between requirements and test-cases. We evaluate our approach on a chat system, Apache Pool [21] and Apache Log4j [11]. We obtain precision/recall levels of more than 90%, an improvement upon currently existing Information Retrieval approaches when tested on the same case studies.