Making Tacit Knowledge in Requirements Explicit

Lead Research Organisation: Open University
Department Name: Computing

Abstract

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.

Publications

10 25 50
 
Description A number of natural language processing techniques to analyse ambiguities in software requirements.
Exploitation Route Applications in a range of areas including medicine.
Sectors Digital/Communication/Information Technologies (including Software),Healthcare,Pharmaceuticals and Medical Biotechnology,Security and Diplomacy

URL http://www.matrex-project.net
 
Description The research has found applications outside engineering in the analysis of suicide notes The tools we developed won an international competition (MedNLP), with Microsoft Research Asia and the Mayo Clinic (USA) in 2nd and 3rd place...
First Year Of Impact 2010
Sector Digital/Communication/Information Technologies (including Software)
Impact Types Cultural,Societal,Economic,Policy & public services

 
Description European Research Council (ERC)
Amount £2,000,000 (GBP)
Funding ID 291652 - ASAP 
Organisation European Research Council (ERC) 
Sector Public
Country Belgium
Start 10/2012 
End 09/2017
 
Description European Research Council (ERC)
Amount £2,000,000 (GBP)
Funding ID 291652 - ASAP 
Organisation European Research Council (ERC) 
Sector Public
Country Belgium
Start 10/2012 
End 09/2017