Isolating Concerns in Requirements Using Latent Semantic Analysis

Latent Semantic Analysis (LSA) is an approach developed by Thomas Landauer et al [LSA]. It applies statistical analysis of a large text corpora for assessing the similarity of terms described in a sample input text. Other work on Aspects, Con-cerns and Requirements [Theme/Doc, Sampaio] has shown that depicting the relationships between concepts described in text is helpful to a developer who identifying a set of early concerns. The Latent Semantic Analysis approach may facili-tate this activity, since finding clusters of requirements is the first step in locating broadly scoped properties that affect many other requirements. In this work, we applied the publicly available LSA tools, in combination with our own manipula-tion of the results of those tools, to determine whether LSA is a promising technique for Aspect-Oriented Requirements Analy-sis (AORA). We found that LSA is indeed a useful approach, however more work is needed to compare it against other natural language based approaches such as [Sampaio] to determine whether it is the best approach, or could be used in concert with such approaches. This paper provides prelimi-nary evidence of the appropriateness of LSA for AORA. 1