Tacit knowledge / 'knowing more than we can tell' / is knowledge that we know we have but can't articulate, or knowledge that we don't know that we have but nevertheless use. We rely on tacit knowledge to communicate effectively: we need not make every assumption we hold explicit, allowing us to focus on the essence of what we wish to communicate. As engineers concerned with the development of software and systems, however, we are taught to make our assumptions explicit, and indeed any kind of knowledge that is not made explicit makes our systems analysis more difficult and error prone. This problem is particularly acute during requirements engineering (RE) / when knowledge about the problem world and stakeholder requirements is elicited, and precise specifications of system structure and behaviour are developed. Requirements are often first communicated in natural language (NL), and are often ambiguous, incomplete, and inevitably full of undocumented assumptions and other omissions. Effective analysis of such requirements needs to surface this tacit knowledge / automatically or semi-automatically where possible / to document more precise requirements that can be relied upon by stakeholders to communicate effectively. Our proposed project aims to investigate techniques for analysing NL requirements, in order to discover, manage, and mitigate the negative effects of tacit knowledge in requirements. We propose to adopt an empirical approach to characterise and elicit tacit knowledge, and a constructive, theoretically-grounded but user-driven approach to develop practical techniques and tools to guide analysts concerned with the development of precise requirements for software-intensive systems.Our proposed approach is to mitigate the negative consequences of tacit knowledge by developing techniques to discover its differential impact on the understanding and use of requirements artefacts. This will enable the management of the effects of tacit knowledge, helping analysts identify where knowledge needs to be made explicit and providing tools capable of resolving at least some of the harmful effects. The results of our work will comprise tools and techniques for: improving the management of requirements information through automatic trace recovery; discovering the presence of tacit knowledge from the tracking of presuppositions and unprovenanced requirements; and the detection of nocuous ambiguity in requirements documents that imply potential for misinterpretation. A number of robust, lightweight natural language processing (NLP) techniques already exist that we will extend to develop our tools. If successful, the results of the work may have tangible benefits to RE practice. More fundamentally, by focusing on the down-stream symptoms of tacit knowledge, our work will make an important contribution to deepening our understanding of the role played by tacit knowledge in RE.

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