A Situation-Centric Approach to Identifying New User Intentions Using the MTL Method

Abstract

Human factors have been increasingly recognized as one of the major driving forces of requirement changes. We believe that the requirements elicitation (RE) process should largely embrace human-centered perspectives, and this paper focuses on changing human intentions and desires over time. To support software evolution due to requirement changes, Situ framework has been proposed to model and detect human intentions by inferring their desires through monitoring environmental and human behavioral contexts prior to or after system deployment. Researchers have reported that Situ is able to infer users' desires with high accuracy using the Conditional Random Fields method. However, manual analysis is still needed for new intention identification and new requirements elicitation. This work attempts to find a computable way to identify users' new intentions with minimal help from human oracle. We discuss the feasibility of implementing the concept of DIKW (Data, Information, Knowledge, Wisdom) to bridge the gap between user behavioral & contextual data and requirements, and propose a situation-centric approach using the Multi-strategy, Task-adaptive Learning (MTL) method. A case study shows that the proposed approach is able to identify users' new intentions, and is especially effective to capture alternatives of low-level task.