![](bg6.png)
We also plan to evaluate LSA against other tech-
niques that may yield similar or better results. In par-
ticular, text reuse algorithms used in plagiarism de-
tection technologies may provide meaningful output,
such as n-gram overlap (Clough et al., 2002), sub-
string matching via greedy string tiling (Wise, 1996)
and sentence alignment (Piao et al., 2002).
7 CONCLUSION
We propose a method of pre-requirements tracing
that uses a corpus linguistics technique to achieve
semantic-level comparison. By splitting up require-
ments specifications and the source material from
which they were derived into chunks and compar-
ing their semantic similarities, it is possible to de-
termine likely sources for each chunk of the require-
ments specification. Further, this permits us to iden-
tify requirements not firmly derived from the sup-
plied source material. We argue that these require-
ments represent either poorly sourced knowledge or
instances of tacit knowledge embedded in the prob-
lem domain or the analyst’s mind. We have demon-
strated that LSA, a linguistic technique designed to
overcome the problems of polysemy and synonymy,
can approximate human expectations of semantic re-
latedness between chunks of source material and their
resulting specification. The source material contains
less rich text than found in other domains, such as
newspaper articles, but is still able to match human
expectation. We plan to show that this technique can
be used to identify instances of tacit processes and
enable pre-requirements tracing on an on-going soft-
ware development project to update the student reg-
istry system at Lancaster University.
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