Improving Accuracy Of Requirement Traceability Links
An Effective Technique
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Among the proposed technique, the literature shows that information retrieval (IR) techniques can automatically recover traceability links between free text requirements and source code. However, precision and recall of IR techniques are impacted by some factors, which impact the input of requirements traceability process. Due to low precision and or recall, developers condense in the effectiveness of traceability link recovery technique is negatively acted. In this study, our research is that adding more sources of information using a trust-based model and integrating developers' knowledge in...
Among the proposed technique, the literature shows that information retrieval (IR) techniques can automatically recover traceability links between free text requirements and source code. However, precision and recall of IR techniques are impacted by some factors, which impact the input of requirements traceability process. Due to low precision and or recall, developers condense in the effectiveness of traceability link recovery technique is negatively acted. In this study, our research is that adding more sources of information using a trust-based model and integrating developers' knowledge in automated IR-based requirements traceability approaches could mitigate the impact of the factors and improve the precision and recall of IR techniques. Our conjecture is that the accuracy of information retrieval techniques could be improved if two (or more) sources of information vote for a link. We use software prepositional, binary class relationships, source code partitioning, and developers knowledge as extra sources of information to conform a link.
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