Abstract-This paper addresses the issues related to improving the overall quality of the dynamic candidate link generation for the requirements tracing process for Verification and Validation and Independent Verification and Validation analysts. The contribution of the paper is four-fold: We define goals for a tracing tool based on analyst responsibilities in the tracing process, we introduce several new measures for validating that the goals have been satisfied, we implement analyst feedback in the tracing process, and we present a prototype tool that we built, RETRO (REquirements TRacing On-target), to address these goals. We also present the results of a study used to assess RETRO's support of goals and goal elements that can be measured objectively.
A number of organizations ranging from terrorist groups such as ISIS to
politicians and nation states reportedly conduct explicit campaigns to
influence opinion on social media, posing a risk to democratic processes. There
is thus a growing need to identify and eliminate "influence bots" - realistic,
automated identities that illicitly shape discussion on sites like Twitter and
Facebook - before they get too influential. Spurred by such events, DARPA held
a 4-week competition in February/March 2015 in which multiple teams supported
by the DARPA Social Media in Strategic Communications program competed to
identify a set of previously identified "influence bots" serving as ground
truth on a specific topic within Twitter. Past work regarding influence bots
often has difficulty supporting claims about accuracy, since there is limited
ground truth (though some exceptions do exist [3,7]). However, with the
exception of [3], no past work has looked specifically at identifying influence
bots on a specific topic. This paper describes the DARPA Challenge and
describes the methods used by the three top-ranked teams.Comment: IEEE Computer Magazine, in pres
This paper presents an approach for improving requirements tracing based on framing it as an information retrieval (IR) problem. Specifically, we focus on improving recall and precision in order to reduce the number of missed traceability links as well as to reduce the number of irrelevant potential links that an analyst has to examine when performing requirements tracing. Several IR algorithms were adapted and implemented to address this problem. We evaluated our algorithms by comparing their results and performance to those of a senior analyst who traced manually as well as with an existing requirements tracing tool. Initial results suggest that we can retrieve a significantly higher percentage of the links than analysts, even when using existing tools, and do so in much less time while achieving comparable signal-to-noise levels.
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