Name
Abstract | ||
|---|---|---|
A relevant activity in the requirements engineering process consists in the identification, assessment and management of potential risks, which can prevent the system-to-be from meeting stakeholder needs. However, risk analysis techniques are often time- and resource- consuming activities, which may introduce in the requirements engineering process a significant overhead. To overcome this problem, we aim at supporting risk management activity in a semi-automated way, merging the capability to exploit existing risk-related information potentially present in a given organisation, with an automated ranking of the goals with respect to the level of risk the decision-maker estimates for them. In particular, this paper proposes an approach to address the general problem of risk decision-making, which combines knowledge about risks assessment techniques and Machine Learning to enable an active intervention of human evaluators in the decision process, learning from their feedback and integrating it with the organisational knowledge. The long term objective is that of improving the capacity of an organisation to be aware and to manage risks, by introducing new techniques in the field of risk management that are able to interactively and continuously extract useful knowledge from the organisation domain and from the decision-maker expertise. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/RE.2015.7320432 | 2015 IEEE 23rd International Requirements Engineering Conference (RE) |
Keywords | Field | DocType |
machine learning,requirements engineering process,potential risk identification,potential risk assessment,potential risk management,risk analysis techniques,risk-related information,automated ranking,decision-maker,risks assessment techniques,human evaluators,decision process,organisational knowledge,knowledge extraction | Systems engineering,Computer science,Risk management plan,Risk management information systems,Risk management,Artificial intelligence,IT risk management,Management science,Stakeholder,Ranking,Risk analysis (business),Exploit,Machine learning | Conference |
ISSN | Citations | PageRank |
1090-705X | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Avesani | 1 | 7 | 2.19 |
| Anna Perini | 2 | 1200 | 70.76 |
| Alberto Siena | 3 | 297 | 27.63 |
| Angelo Susi | 4 | 1057 | 83.69 |