We posit that machine learning can be applied to effectively address requirements engineering problems. Specifically, we present a requirements traceability method based on the machine learning technique Reinforcement Learning (RL). The RL method demonstrates a rather targeted generation of candidate links between textual requirements artifacts (high level requirements traced to low level requirements, for example). The technique has been validated using two real-world datasets from two problem domains. Our technique demonstrated statistically significant better results than the Information Retrieval technique.

Requirements traceability is an important undertaking as part of ensuring the quality of software in the early stages of the Software Development Life Cycle. This paper demonstrates the applicability of swarm intelligence to the requirements tracing problem using pheromone communication and a focus on the common text around linking terms or words in order to find related textual documents. Through the actions and contributions of each individual member of the swarm, the swarm as a whole exposes relationships between documents in a collective manner. Two techniques have been examined, simple swarm and pheromone swarm. The techniques have been validated using two real-world datasets from two problem domains.

We posit that swarm intelligence can be applied to effectively address requirements engineering problems. Specifically, this paper demonstrates the applicability of swarm intelligence to the requirements tracing problem using a simple ant colony algorithm. The technique has been validated using two real-world datasets from two problem domains. The technique can generate requirements traceability matrices (RTMs) between textual requirements artifacts (high level requirements traced to low level requirements, for example) with equivalent or better accuracy than traditional information retrieval techniques.