Abstract
Ambiguous user requirements are usually considered problematic in software engineering. Therefore, many studies have been conducted on its avoidance and detection. However, the detected ambiguities were resolved manually using interviews, brainstorming, and group discussion sessions among the elicitors and stakeholders for whom the software was developed. If not addressed efficiently, it gives rise to the explicit issues of additional time and cost involved and the stakeholders' availability to clarify them during multiple sessions. However, if appropriately addressed, it can reveal some implicit issues, such as tacit knowledge, hesitation, and terminological discrepancies. Identifying these implicit issues is not easy, as it requires expert elicitation skills that usually come with experience. In addition to the increasing demand for an automated approach to address these implicit issues, the recent COVID 19 pandemics has also amplified the demand to address the explicit issue of stakeholder availability. This paper proposes an implementable semi-automated approach to help elicitors address these demands. The proposed approach uses intuitionistic fuzzy logic to address hesitation and statistical functions to identify discordance and tacit knowledge. It also uses the heuristic knowledge gained in each iteration to improve itself. We implemented it in an online tool and conducted controlled experiments to evaluate our approach, and the results were compared. We achieved precision, recall, and F1 score of 0.769, 1, and 0.869, respectively, during our experiments. The results show that the proposed approach may minimize the explicit issues and help novice elicitors address the implicit issues discussed earlier.